WO2007131066A2 - Decentralized physiological data collection and analysis system and process - Google Patents

Abstract

A system and process for the collection and analysis of physiological data obtained from a remote facility includes obtaining physiological data concerning at least one subject from a sensor associated with a subject located in a facility, transmitting the physiological data to a centralized location remote from the facility, and streaming the physiological data from the central location to at least two locations remote from the facility and the centralized location, and analyzing the physiological data with at least one algorithm during, prior to, and/or subsequent to the one or more of the obtaining, streaming, and analyzing.

Description

DECENTRALIZED PHYSIOLOGICAL DATA COLLECTION AND ANALYSIS

SYSTEM AND PROCESS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. §119(e) to provisional U.S. Patent Application No. 60/796.549, filed on May 2, 2006 the disclosure of which is expressly incorporated by reference herein in its entirety.

BACKGROUND OF THE SNVENTSON

1. Field of the Invention

[0002] The invention is directed to a process and system For improving the efficiency and economics of research and development that requires the coSlection and analyses of physϊologica! data and, more particularly, to improving efficiency and economics by enabling research in off-site research facilities.

2. Reiated Art

[0003] Currenfiy. the colSection and analysis of physiological data requires the use of high cost data acquisition with sensors and systems. The coiSection &nά analyses of research and development data requires the use of high cost data acquisition systems, high cost laboratories, the cost of test subjects such as animals, and the labor costs of lab assistants, researchers, and so on. Each of these factors has a tendency to increase the costs of performing research and dβveiopment More specifically, if a laboratory desires to perform research and deveSopment they must obtain the correct equipment to perform the research and development. If the research anύ development facility is not currently performing research and development, then the costs of entering this particular activity is rather high. Similarly, the cost of obtaining and caring for animals is also very high. Next, the facilities themselves are typically quite expensive. The laboratories must be built from the ground up to house various equipment, animals and researchers needed for the research process. Finally, the research assistants, analysts and others are also a large portion of the cost of running a research and development facility. [0004] Accordingly, there is a need for a less expensive research and development laboratory alternative and a further need to reduce the associated costs of such research so as to improve the efficiency and economics of the collection and analysis of research anύ development data while improving quality thereof. SUMMARY OF THE INVENTION

[0005] The invention provides a reduced cost research and development facility that allows for the acquisition of research data along with an automated analyses system.

[0006] The invention may be implemented in a number of ways. [0007] According to one aspect of the invention, a system for the collection and analysis of physiological data obtained from a remote facility includes a sensor system collecting physiological data from at least one of a wearable sensor and an implantable sensor configured to sense characteristics of a subject located in a facility, a computer system disposed remote from the facility and configured to receive the physiological data from the sensor system via a network, a storage device configured to archive the physiological data received by the computer system, and wherein the computer system is configured to stream the physiological data to a plurality of locations for the collaborative analysis thereof. [0008] The computer system and/or a processor may be configured to execute an algorithm to analyze the physiological data. The sensor may be structured and arranged to sense at least one of blood pressure, central venous pressure, pulmonary arterial pressure, pulse oximetry (SAO2), cardiac sounds, non- cardiovascular signals such as EEG K-complexes, muscular activity, neural activity, acoustic waveforms, and speech waveforms. The physiological data may be m ECG and the system further may include a processor to generate a nonlinear signal model based on the ECG signal, fit the nonlinear signal model to the ECG signal based on an optimization algorithm, and determine at least one feature of the ECG with the nonlinear signal model, and an output device to output the at least one feature of the ECG based on the nonlinear signal model. The computer system may be configured to receive physiological data from a plurality of wearable sensors and/or implantable sensors from the network. The computer system may include a platform. The platform may include a Hermes platform The storage may include a redundant array of independent disks. The sensor may transmit the physiological to the computer system via one of a wired connection and a wireless transceiver. The subject may be one of a human and an animal. The facility may include an off-site research facility The plurality of locations may include at least one of a research center, academic facility, physician's office, and clinician^'s office. [0009] According to another aspect of the invention a process for the collection and analysis of physiological data obtained from a remote facility includes the steps of obtaining physiological data concerning at least one subject from a sensor associated with a subject located in a facility, transmitting the physiological data to a centralized location remote from the facility, streaming the physiological data from the central location to at least two locations remote from the facility and the centralized location, and analyzing the physiological data with at least one algorithm during, prior to, and/or subsequent to the one or more of the obtaining, streaming, and analyzing steps,

[001 Oj The process may further include the step of analyzing the data at one of the plurality of locations. The transmitting step may include transmitting the physiological data from a sensor via at least one of wireless transmission &nά wired transmission. The obtaining step may include obtaining the physiological data from at least one of an implantable sensor and a wearable sensor. The process may further include the step of archiving the physiological data at the centralized location. The process may further include the step of visualizing the physiological data at the central location. The process may further include the step of enabling the collaborative interaction of a plurality of physicians or analysts at a plurality of locations. The subject may be one of a human and an animal. The facility may include an off-site research facility. The plurality of locations may include one of a research center, academic facility, physician's office, and clinician's office. The physiological data may include an ECG and the process may further include the steps of generating a nonlinear signal model based on the ECG signal, fitting the nonlinear signal model to the ECG signal based on an optimization algorithm, determining at least one feature of the ECG with the nonlinear signal model, and outputting the at least one feature of the ECG based on the nonlinear signal model.

[0011 J According to yet another aspect of the invention a system for the collection and analysis of physiological data obtained from a remote facility includes means for collecting physiological data from at least one of a wearable sensor and an implantable sensor configured to sense characteristics of a subject located in a facility, means for receiving the physiological data from the sensor system via a network disposed remote from the facility, means archiving the physiological data received by the collecting means, and means for streaming the physiological data to a plurality of locations for the collaborative analysis thereof. [0012] According to another aspect of the invention a computer readable medium having instructions stored thereon that when executed by a processor provides for the collection and analysis of physiological data obtained from a remote facility includes instructions for obtaining physiological data concerning at feast one subject from a sensor associated with a subject located in a facility, instructions for transmitting the physiological data to a centraiized location remote from the facility, instructions for streaming the physiological data from the central location to at least two locations remote from the facility and the centralized location, and instructions for analyzing the physiological data with at ieast one algorithm during, prior to, and/or subsequent to the execution of one or more of the obtaining instructions, streaming instructions, and analyzing instructions

[0013] Additional features, advantages, and embodiments of the invention may be set forth or apparent from consideration of the following detailed description, drawings, and claims. Moreover, it is to be understood that both the foregoing summary of the invention and the following detailed description are exemplary and intended to provide further explanation without limiting the scope of the invention as ciaimed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings, which are included to provide a further understanding of the invention, are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the detailed description serve to explain the principles of the invention. No attempt is made to show structural details of the invention in more detail than may be necessary for a fundamental understanding of the invention and the various ways in which it may be practiced. In the drawings:

[001 SJ Figure 1 schematically illustrates exemplary details of one embodiment of an off-site research facility constructed according to the principles of the invention; [0016] Figure 2 schematically illustrates one embodiment of the overall system for the collection and analysis of physiological data from the off-site research facility shown in Figure 1 ;

|001?J Figure 3 is a flowchart schematicaSSy illustrating the collection and analysis of physiological data from a off-site research facility operating according to the principles of the invention: [0018] Figure 4 is a flowchart schematically illustrating a generalized exemplary analysis process for constructing a mode! fit signal according to the principles of the invention, which may be used with the system of Figure 1 ;

[0019] Figure 5 shows an onginal (clean) graphed ECG signal, a mode! fit signal constructed according to the principles of the invention and the residual error between the two signals;

[0020] Figure 8 shows a mode! fit to an ECG signal using the principles of the invention under high noise conditions. The underlying signa! before noise was added and is shown. Note that the model fit preserves the overall morphology &nά placement of the onset and offset of the main features;

[0021] Figure 7 shows a ST-elevated waveform and model fit constructed according to the principles of the invention; and

|0022J Figure 8 shows a typical ECG with labels relevant to QT analysis.

DETAILED DESCRIPTION OF THE INVENTION [0023] The embodiments of the invention %nά the various features and advantageous details thereof are explained more fully with reference to the non- limiting embodiments and examples that &re described and/or illustrated in the accompanying drawings and detailed in the following description. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale, anό features of one embodiment may be employed with other embodiments as the skilled artisan would recognize, even if not explicitly stated herein. Descriptions of weli- known components and processing techniques may be omitted so as to not unnecessarily obscure the embodiments of the invention. The examples used herein are intended merely to facilitate an understanding of ways in which the invention may be practiced and to further enable those of skill in the art to practice the embodiments of the invention. Accordingly, the examples and embodiments herein should not be construed as limiting the scope of the invention, which is defined solely by the appended claims and applicable law. Moreover, it is noted that like reference numerals represent similar parts throughout the several views of the drawings. [0024] The invention is directed generally to a virtual laboratory process and system by which traditional "brick and mortar'^' contract research organizations (CROs) are replaced by a decentralized, dispersed "fiat" model in which the cost of research and development is greatly reduced. The cost of such a "virtuaP laboratory. which is also referred to herein as the "off site research facility", may be reduced by locating the off-site research facility in a location, off-site from the higher cost location of the researchers, business unit, and so on. Off-site, as used herein, may include any remote site where efficiencies are obtained from such location due to decreased costs or other advantages, such as areas outside of urban areas, in other cities, or in other countries.

[0025] The virtual laboratory may include a plurality of remote processes and business functions that tradϊtionaliy would be centralized about the current contract CRO business models. The remote capability of these processes and functions may be enabled by three technologies described in greater detail beiow. These technoϊogies inciude instrumentation and its sensors 102, a data archiving visualization, and a streaming platform 104, and algorithms for automated analyses 106.

[0026] The invention may enable the remote collection of physiological data from sensors including wearable or implantable sensors, such as sensors disposed on the surface of the skin, in test subjects, such as animals or humans, in an off-site research facility, such as a preclinical trial facility. The sensor may be wireless or wired. The type of facility contemplated in the invention may be lower in cost and simpler than most traditional laboratory environments since it need not employ highly trained, high cost clinicians to implant sensors, care for animals, and collect data. This reduces the size, complexity, and cost of the facility. For example, the off-site facility may be a simple, low cost "animal farm" administered by personnel having lower labor costs for example.

[O027J Data may be collected from implantable wired or wireless sensors that may convert the physiological data into a format for network transmission. For example, the data may be converted into rentable internet protocol (IP) packets or the like. [0028| Figure 1 schematically illustrates exemplary details of one embodiment of an off-site research facility constructed according to the principles of the invention. As shown in Figure 1. the data may be sent to a Remote Communications Processor (RCP) 250 that buffers the data for transmission over a standard, low cost data communications circuit 252 such as DSL modem. The RCP 250 may perform several communication and administrative functions to allow centralized control over the remote processes. [0029] A physiological data collection and visualization platform 104 may be the central data collection technology that may gather streams of networked data from the sensors at many off-site laboratories or locations simultaneously. The capacity and throughput of the platform 104 may allow the platform to replace many standalone remote PCs, thus lowering the overall cost of capture and storage. [0030] The physiological data collection and visualization platform 104 may permit users in various remote locations to peruse_r analyze, and annotate the physiological data in a centralized data base 114 as discussed in greater detail below. This permits the use of lower cost, highly trained clinicians in remote parts of the world where wages are typically much lower than in the US or portions of Europe or Asia. [0031] The invention may include various sensor technologies, a platform For collection of data, a network for connecting the platform as discussed below with an off-site research facility and the off-site research facility 299 itself, in particuiar, the sensor technologies may be used in humans, various types of mammalians, or other types of animals and may be implanted or attached thereto. The sensor technology may be wired or wireless including a wireless fidelity (Wi-Fi), ceSlular, or the like. The sensor technologies may be low cost, implantable, and wearable. Moreover, the sensor technologies may allow for large amounts of high speed Song term physiologicai data to be collected,

[0032] As shown in Figure 1 , the sensor technology may be an implantable wireless sensor 132 with a stimulator that may be implanted into the subject. Wired sensors are also contemplated. The implantable wireless sensor may have bidirectional communication 254 to a transceiver 256 through, for example, a high gain directional antenna 258 and/or multiple input multiple output type of device. The transceiver 256 may be a multiple channel receiver having both receive and transmit functions. The multipSe channel transceiver 266 may use a ZiGBEE type of network configuration setting. The multiple channel transceiver 256 may be connected to the RCP 250. The RCP 250 may ha\/e a small form factor and/or single board computer. Additionally, the RCP 250 may include a MINJ-STX or a NANO-STX layout. Moreover, the RCP 250 may operate using a Linux or Windows OS operating system for example only. The RCP 250 may include a CPU and a hard disk drive to provide sensor data buffer functions. Moreover, the RCP 250 may include a router that may form a network connection, such as a DSL modem, that may be configured separate from the RCP 250. The router may connect to a locaS LAN network anύ may also connect to the internet via the network connection. Accordingly, the pSatform 104 may also connect to the internet providing communications therebetween. The RCP 250 may provide a web server interface for remote configuration control may also allow routing configurations and settings, alternate route/path settings, date storage buffer settings, Communications protocol settings, sensor controls, and ZiGSEE networking configuration settings. Next, a system for use in operating the above- noted off-site facility wil! be discussed.

[0033| Figure 2 schematicaiiy iiiustrates one embodiment of the overail system for the coiiection and anaiysis of physiological data from the off-site research facility shown in Figure 1. in particular, a system 100 and associated process may include a combination of instrumentation and sensors 102: data archiving, visuaiization, and streaming 104; and aigorithms for automated anaiysis 106, as discussed in greater detaii beiow. More specificaiiy the instrumentation and sensors 102 may include wearabie and implantable sensor technology, or other sensors known in the art. This sensor technology may include wired or wireless type transmission of sensor data. The data archiving, visualization, and streaming 104 may aiiow for the data acquisition from the sensors that may be digitized, stored, and streamed to remote archiving, analysis servers. Finally, the algorithms for automated analysis 106 may inciude the capabiiity to analyze data automaticaiiy and may further include the capability to have the data reviewed by one or more clinicians remotely at separate locations via internet or other type of network.

[0034] The instrumentation and sensors 102 of the invention may include wearable devices 122 such as a holter monitor as is known in the art to permit continuous iong- term subject monitoring. The holter monitor is also referred to as an ambuiatory electrocardiography device that may be a portable device and may aliow for continuous monitoring of the heart for up to or more than 24 hours. The holter monitor may include a series of electrodes and may provide recording of the output of the electrodes to a flash memory or the like.

[0035] The sensors noted above may aiso be implantable 132, The sensor may be implanted and may contain a self-sufficient energy source or battery. The battery may have the ability to be recharged subcutaneoυsly. in another embodiment, the sensor may contain a radio frequency responsive and/or powered circuit energy source. Such sensors allow for ECG monitoring, autonomic monitoring, peripheral nerve monitoring, systemic glucose monitoring and so on. The implantable type sensors 132 are configured to be small and operate subcutaneously. The implantabie type sensor 132 may non-invastve arrangement. Both the wearable 122 and implantable sensors 132 provide a robust event monitoring anaiysss with realtime data capture and streaming. The implantable sensor 132 may have a self- contained form factor, multi-modal sensor and/or anaiysts capabiiity, and aliow for longitudinal data analysis. Both types of sensors may include a housing, memory, operating circuitry, a battery and other components known in the art. The sensors may also be implemented as battery-iess sensors that receive power through inductive type circuits. Furthermore, in order to provide wireiess transmission of the data, the sensors may include a transceiver and the like. The sensor, may include various input/output connections and the like.

[0036] In particular, the sensor technology used in conjunction with the invention may sense any type of physiological phenomena including electrical, acoustic, vibratory and so on. In particular, any known sensor technology may be used in conjunction herewith, although it is preferred to use electrical sensing sensors. [0037] A sensor may be used to measure any physiological signal, and, for example, may include blood pressure, central venous pressure, pulmonary arterial pressure, puise oximetry (SAO2). cardiac sounds, non-cardiovascuiar signals such as EEG K-compiexes, muscular activity, neural activity, acoustic waveforms, and speech waveforms.

[0O38J Such types of sensors may include the ability to automatically detect events and transmit data, and may further include real-time data streaming. The data transmission may include transmission directiy from a patient's device to an analysis platform by a cellular data network. The platform, as described in greater detail below, may include one or more of a software application, operating system, and hardware. The transmission may include any known protocol including GPRS, EVDO. UNTS, Wimax, WiFi, Bluetooth or the like.

[0030] The data archiving visualization and streaming platform 104 may provide for the long-term physiological collection and analysis of the data collected by the instrumentation and sensors 102 noted above in particular, the data archiving, visualization, and streaming platform 104 may ailow for the collection, formatting, storage, visualization, automated analysis, annotation, event detection and the like of the physiological data collected by the instrumentation and sensors 102. [0040] The data archiving, visualization and streaming platform 104 may include a platform 108 allowing for the high-speed, high-throughput, multi-channel data collection from the instrumentation and sensors 102. For example, collection may be carried out over, in part, the internet or other type of data transmission network 116. Moreover, the platform 104 may allow for efficient high-volume formatting of the data and RAID-based (RAID-Redundant Array of independent Disks) storage 1 14 for multiple-user, mυltipie-site access and archiving. Moreover, the platform 104 may alϊow for locaS or remote retrospective or real-time graphical visualization using such processes as a web server process. Additionally, the platform 104 may allow for single or multiple users 110 (collaborative), multiple location annotation of the data, Finally, the platform 104 may include various event detections such as heart rate detection for notification and alarming. The coliaborative web-based technology model enables collection of physiological data and analysis of results at many different locations 112, 112', 112". Moreover, the above-described model may allow for a simple existing web-browsing technology for visualization, analysis, and annotation of data. This model as described above may enable a number of remote users 1 10 to collaborate simultaneously. Additionally, the mode! allows for a centralized database storage 114 instead of replication at multipie user sites. [0041] The platform 104 may also allow for real-time streaming of physioiogical data from a large number of remote sensors. The sensors being one or more sensors described above or others as are weli known in the art. The platform further may allow for the use of screening algorithms. The screening algorithms may be able to quickly highlight areas of interest in the data that is held by the data archiving, visualization, and streaming platform 10S that was obtained through the instrumentation and sensors 102. The platform may provide a very high volume of capture, storage, and screening of incoming sensor data for automated, Song-term testing. Examples of data archiving, visualization, and streaming platforms that may be used in the invention are disclosed in copending United States Patent Application

No. . filed May 2, 2007, for COLLECTION AND ANALYSIS OF

PHYSIOLOGICAL DATA having attorney docket No, 2048120-5008US, the disclosure of which is incorporated by reference in its entirety herein and includes the known Hermes™ type platform, which includes Hermes™ servers, operating system, and so on. The Hermes™ platform may be provided by Hermes Medical Solutions, inc. Chicago, Illinois, U.S.A. The Hermes™ platform 108 may include any type of processor such as a PC or server, storage system such as a RAID level one (mirror) configuration or the like. The Hermes™ system may further include any form of seamiess network integration.

[0042] The algorithms for automated analysis 106 may include any known type of analysis that is algorithm-based or otherwise, in one particular aspect, the analysis may include an ECG-type anaiysis as discussed in greater detaii below. However, the invention contemplates any type of automated analysis whether based on an algorithm or otherwise. Moreover, the invention contemplates analysis of any type of physiologicai data including blood pressure, centra! venous pressure, puirnonary arterial pressure, puise oximetry (SAO2), cardiac sounds, non-cardiovascular signals such as EEG K-complexes, muscular activity, neural activity, acoustic waveforms, speech waveforms, and so on.

[0043] Figure 3 is a flow chart schematicaily iiiustrating the coiiection and anaiysis of physiologicai data process according to the principles of the invention. In particuiar, Figure 3 shows a coiiection and anaiysis process 200 that may be performed by the system 100 in Figure 2 or any other equivaient type system or arrangement.

|0044] In step 202, subjects are provided with wearable type sensor arrangements or implantable type sensor arrangements or other sensors known in the art at the off- site research facility. The sensors provide sensor output as noted above. The wireless type sensors may output to a wireless access point, cβliuiar tower or the like. The wired type sensors may be configured to connect to some form of network type connection such as the internet.

[004Sj Thereafter, as shown in step 204 the data that is acquired from the subjects may then be transmitted over a network such as the Internet to a data archiving, visualization, and streaming type platform 108. In the piatform, the data which is the output from the sensors may be archived and stored in a large database, it also may be modified to provide an additionai level of analysis and reporting such as a visual imaged-based report.

£00463 ^^ext- ^{m ste}P 206 the data may be streamed to other locations. The other location can include one or more remote or local medical facilities, physicians, analysts, clinicians, and the like, which may be located e.g., in higher cost, more urban locations than the off-site research facility. Further the data archiving, data visualization, streaming platform or the various medical analysts may then be abSe to further apply an algorithm or other type of analysis to the data as shown in step 208, Finally, the various physicians and analysts and the like may then further collaborate together with the information obtained as described above, even if they are in different locations from each other and the off-site research facility. [0047] The flowchart of Figure 4 shows a generalized exemplary analysis process for constructing a model according to the invention. In particular, the process of the invention provides a genera! framework for deriving models of quasi-stationary signals for robust filtering, compression and segmentation of a signal and for identifying the location of regions of change. As such, the process can be viewed as a type of nove! adaptive filter or as a process for correlated source separation in the time domain In particular, the approach is suited to physiological signals, which are often characterized by oscillations at specific frequencies, and contaminated by in- band noise (which is both periodic and statistical). This approach is set forth in greater detail in copending United Patent Application No. 11/470,506. METHOD AND DEVICE FOR FILTERING, SEGMENTING. COMPRESSING AND CLASSIFYING OSCILLATORY SIGNALS, filed in the name of Gari D. Clifford, the disclosure of which is incorporated by reference herein in its entirety. [0048] The assumption in the following method is that the time series under analysis is composed of a set of distinct, yet transient (although not necessarily independent) morphologies. Examples of these include the set of features used to classify sieep from the electroencephalogram, {such as K complexes and sleep spindles), the heart sounds recorded in the phonocardiogram, or the waves in a pulsatile blood pressure waveform. Once a set of general features is identified, a template of each feature may be formed and a mixture of temporally shifted basis functions (such as Gaussians) may be fitted to each major turning point in the signal using an optimization procedure

[0040] The signal model is a dynamic model, where each turning point in a signal is represented by a Gaussian of varying width and amplitude, centered at different points in time. This nove! implementation extends the model by adding a new Gaussian for each asymmetric turning point, then adaptiveiy modifying the parameters to fit a distinct observation. Here, the concept is generalized to model any signal and provide an automatic method for deriving the mode parameters. [005OJ If we assume a transient feature (such as a K complex) is smoothSy varying and composed of M symmetric anύ N asymmetric turning points, then M + 2N Gaussians are required to describe the feature (since a Gaussian is symmetric). For example, an asymmetric turning point requires two Gaussians to be accurately represented. The segment of the signal z, which describes the feature under analysis is given by:

where

is the relative position of each turning point from the location in time t, of a reference point (fiducial marker), K = a,/2b, is a normalization constant (chosen for consistency with the originai dynamic mode!), and the z_s are baseline offset parameters for each of the turning points. The coefficients a, govern the magnitude of the turning points and the b_; define the width (time duration) of each turning point. The model is therefore fuily described by 3(M + 2N) parameters. [0051] In order to fit Equation (1) to a feature, an approximate tempiate must be constructed. A genera! method for this is to appiy a coarse matched fitter (such as cross correiation with a population independent genera! template) or an energy thresholding technique (which is common in ECG analysis) to the signal in question. The selection of one technique for this process over another depends on the distribution of the energy of the observation over time. If the signal energy is evenly distributed over time, some a priori knowiedge of the features may be used to form a simpie template for a matched fiiter.

[0052] Fiducial markers may then be located at various points in time that provide time- specific reference markers for each candidate feature (segment of signai) as shown in step 302 of Figure 4. By segmenting the time series around each fiduciai point, and performing a tempora! average, a first template class is generated as shown by step 304. By comparing each candidate feature to the first template class, possible artifacts or patterns belonging to other feature classes may be rejected using a suitable threshold such as a cross-correlation as shown in step 306, £0053] The first feature class may then be modified to be the average of the non- rejected individual features (to construct a more specific feature). The rejected candidate may then be averaged to form a second feature ciass tempiate and the process repeated (see arrows A and B) until the number of possible remaining candidates (which were not included in the previous classes) are beiow some predefined threshold, or the inter-pattern variance between the remaining candidate patterns becomes too high to allow the formation of any more distinct groups. [0054] In the case of an ECG, the first feature class is likely to be a sinus beat (as long as it is the dominant morphology in the time series). Abnormal beats may be rejected and the dominant abnormal beat may become the second feature class- High correiations between the average of this rejected set and each member of the set may identify the new members of the set. Rejected beats may cascade down to the next candidate class.

[0055] For each template class, an initial model must then be derived. The mode! order O = M + 2N, the number of symmetric plus twice the number of asymmetric turning points in the ciass. Often, this is a known quantity for most physiological features, but in some circumstances, an unsupervised method for determining the model order is required.

[0056] One method is as foSSows: if there are enough feature candidates to form a smooth. Sow noise template, the number of turning points may be calculated by numericaSiy differentiating the feature and iocating the zero crossing points (after allowing for delays in the numerical differentiation function) as shown in step 308. [0057] The degree of asymmetry for each turning point may then be found by squaring the resultant differential &nά comparing the resultant two peaks (one for the upslope and one for the downslope) as shown in step 310 if a given pair of peaks are similar in height and width, then the peak is symmetric anύ oniy one set of a, b,, and t_s are required for the peak. If the peaks in the squared differentia!, for a given pair, differ sufficiently {by some predefined threshold that depends on the feature class and signal amplitudes) then the peak is deemed asymmetric and two Gaussians are required to describe the turning point. [0058] It should be noted that this procedure effectively determines the approximate starting points for fitting the mode! to each feature candidate (see step 312). However, the height (a_s) and width {b,} of each Gaussian in the initial model remain to be determined. For most applications, (as long as the t, are initially limited so that they do not vary significantly) the initialization of the a, and b, do not affect the final outcome, and random smaSS values are sufficient. However, in some situations, abnormal iocai minima in the model fitting procedure are possible and the use of an estimate of the width anύ height of the turning points not oniy helps to avoid this, but also allows a significant acceleration in the time for fitting each feature candidate. [0059] The residua! error between the result of the mode! fitting procedure (described below) and the original feature provides a faculty to reject particuiar fits as shown in step 314. It should also be noted that a classification may be performed by initializing with each possible class (variant of the mode!) and picking the class with the minimum residual error, or the smallest distance {in parameter space) between a given fit and a cluster center of representative candidates in the same parameter space.

[0060] An efficient method of fitting the signal mode! (Equation (1)) to a candidate vector s(t), is to minimize the squared error between s and the mode! output, z, In other words, one shouid find

over al! of the 3(M + 2N) parameters in the model. Equation (1) may be solved using an (3M + 8M)-dimensionai nonlinear gradient descent on the parameter space, in general, the problem of muϊtidimensiona! nonlinear least squares fitting requires the minimization of the squared residuals of n functions. f_s in p parameters, x,

ali algorithms for achieving the minimization may proceed from an initiai guess using the linearization.

where x is the initiai point, p is the next step and J is the Jacobian matrix Additional strategies can be used to eniarge the region of

convergence anύ include requiring a decrease in the norm jjFjj on each step or using a trust region to avoid steps that fai! outside the iinear regime. This procedure has been impiemented in two different iibraries: the Gnu Scientific Libraries (GSL) in C, and in Matiab using the function ϊsqnonlin.

[0061] In one specific appiication, the invention may be appiied in a novel technique for fitting a nonlinear ECG model (a sum of temporally shifted Gaussian waveform morphologies) to the ECG using a nonlinear least squares optimization. Figure 5 illustrates the performance of the fitting procedure for a typical ECG with no noise in the original signal. Figure 4 iliustrates the performance of the technique when fitting the model to m extremely noisy beat. Not only does the technique allow a powerful method for filtering the ECG on a beat-by-beat basis even in high noise conditions, but also the use of Gaussian descriptors allows for a statistically meaningful description of wave onset and offset, in particular, this modei-fitting procedure provides an excellent method for Q-wave onset anύ T-wave offset localization.

[0062] The model-based fitting of an ECG allows one to more precisely determine the locations of the P, Q, R, S and T features of each beat, and their respective onsets and offsets (determined as a certain number of standard deviations away from the central point). Furthermore, since noise may not be explicitly encoded in the waveform, the fitting procedure makes for an excellent noise suppression technique. Although the representation of the beat as just 18 coefficients in a nonlinear mode! means that (iossy) compression is possible, the clustering of these coefficients allows one to classify beats on this basis. However, perhaps the most usefυi and immediate application of this model-fitting procedure is in the determination of wave boundaries in noisy conditions to aliow robust and accurate QT analysis. [0063| Accordingly, by fitting a modified version of the model to each beat, and constraining the fit with a time averaged template, a filtering of each beat is performed. The model consists of a sum of Gaussians centered on each wave of the ECG (P, Q, R, S, and T). Each Gaussian is fuliy specified by three parameters: location in time, ampiitude, and broadness. Therefore, the representation of the ECG as a series of Gaussians is aiso a form of (lossy) compression. Finally, the parameters for each beat may be compared to a norma! set of parameters and a classification made.

|0064| Another way to apply the above-noted approach is to describe each feature of the ECG (PQRS & T) by a Gaussian with three parameters: the amplitude α_; .. width h_s , and phase (or relative position with respect to the R- peak). The

verticai displacement of the ECG, z , is described by an ordinary differential equation,

where is the relative phase. Note that no z -offset exists as the

model-fit assumes z=0 at the isoelectric levei. Numerical integration of this equation using appropriate set of α, .. /> , θ,, leads to the familiar ECG waveform. [0065] Furthermore, an optional extra parameter has been added to the T feature, denoted by a superscripted - or +, to indicate that they are located at values of θ (or t) slightly either side of the original θ₁ . By using two sets of { a_i. , b_i , θ_i } to represent a particular feature, an asymmetric turning point may be formed. Although this is particularly important for the T-wave on the ECG, it is of negligible importance for the other four features in the ECG. Therefore, six features may be required for the ECG:

P₁ Q₁ R₁ S₁ T ₁ T⁺.

[0066] Again, an efficient method of fitting the ECG model described above to an observation s(t), is to minimize the squared error between the s(t) and τ. That is, one may find

over ail six i , with Fortunately, one may analytically integrate (3) to give

Equation (4) may then be soived using an

eighteen-dimensional gradient descent in the parameter space. The MatSab function Isqnoniin.m or the like may perform the required implementation of this nonlinear least squares optimization. [QQ67J To minimize the search space for fitting the parameters {«,, , /> , and O₁ ), a simpie peak-detection and time-aligned averaging to form an average beat morphology template is formed over, for example, at ieast the first 60 beats centered on their R-peaks. (The template window is unimportant, as long as it contains aϊi the PQRST features and does not extend into the next beat). Cross correlation is then performed between each beat and the template to remove outliers (with a linear cross-correlation coefficient less than, for example, (X 95). If more than about 20% of the beats are removed, then another 60 beats may be allowed into the average template, and the outlier rejection procedure is re-iterated. When less than about 20% of the beats are discarded, another average template is then made of the remaining beats. Peak and trough detection is then performed on this template (using refactory constraints for each wave) to find the relative locations of the turning points in time (and hence the θ_t ). The values T^' and T' may be initialized ± 40 ms either side of θ, . By measuring the heights of each peak (or trough) an estimate of the O₁ may also be made. Each b_t may be initialized with a value 10 + 5// , where μ is a uniform distribution on the interval [0, . . . , 1], Each of the values, a_i, and θ_i , were initialized with random perturbations of μ and 20μ respectively.

[0068] Note that it is important that salient features that one wishes to fit (the P- wave and QRS segment in the case of the ECG) are sampled at a high enough frequency to allow them to contribute sufficiently to the optimization, in empirical tests, it has been found that aϊi ECGs below approximately 512 Hz required upsampiing (with an appropriate antialiasing filter). This corresponds to about 30 sample points in the QRS complex. Using less than 30 samples in a wave may lead to some extremely bizarre fits that fulfill the optimization criteria, [0069] Figure 5 shows an original (clean) graphed ECG signal, a model fit signal constructed according to the invention and the residual error between fit and model signals, in particular, Figure δ illustrates a real beat (recorded from a Vδ lead), a typical fit to a template of real beat, and the residual error [0070] Figure 8 illustrates the results of fitting the mode! to a segment of ECG cleanly recorded and contaminated by electrode motion noise. Note that despite the significant waveform distortion, the locations of the P, Q₅ R₁ S, and T peaks match the underlying wncorrupted signal) to sub-sample precision, even with (F₅ > 1 kHz). Note also that the error around the iso-electric point and ST-lβvei are negligible in a clinical sense (< 0.1 mV. or about 5% to 10% of the QRS amplitude for a sinus beat on a VS lead) (Amplitudes have been scaled by an arbitrary, but consistent factor). [0071] Filtering of the ECG by fitting equation 3 to small segments of the ECG around each QRS-detection fiduciai point is an excellent way to provide an idealistic (zero-noise) representation of the morphology that captures much of the clinica! information of that beat. In fact, this approach may be generalized to any band- limited waveform with fewer than F,, oscillations per sample, In particular, the signal we are representing does not need to be periodic and is therefore particularly suited to physiological signals. Since the model is a compact representation of oscillatory signals with few turning points compared to the sampling frequency, it therefore has a band pass filtering effect leading to a lossy transformation of the data into a set of integrable Gaussians distributed over time. [O072J It should be noted that that the fitting procedure effects a (lossy)

compression at a rate of per beat or where is the reciprocal

of the (average) heart rate, F_s is the sampling frequency, and k - n + 2m is the number of features or turning points used to fit the heart beat morphology (with n symmetric and m asymmetric turning points). For a low ECG sampling rate of 128 Hz, this translates into a compression ratio greater than 7:1 at a heart rate of 60 bpm. However, for high sampiing rates (F_s = 1024} this may lead to compression rates of almost 57:1. Reducing k from the full representation of k=18 is often appropriate for tasks which require only the QRS complex (k = 9) or the ST segment (k= 12) to be analyzed. High heart rates may reduce this compression unless the dynamic properties of the model are used to encode the heart rate-dependent variations through dynamic shifts in the values of the υ_t , b_} , and θ, . For a given segment of r

seconds with an average heart rate of , the compression ratio rises by a factor

3] The above model is just an approximation and therefore the compression becomes even more lossy. One should also note that no explicit accounting of abnormal beats has been made in these calculations and a new set of parameters must be derived, possibly for each new abnormal beat encountered in the ECG record,

[0074] The above-described method for simultaneously filtering, compressing, and classifying a physiological signal, such as the ECG, from a subject may work in real time on a modern desktop PC and the like. The PC may execute a stgnal processing program such as Matiab™ {Available from: The Math Works, Inc. Natick. MA 01780- 2098) or the like to perform the above-noted method as is known in the art. By fitting a set of six Gaussians, each specified by three parameters in an ordinary differential equation, and performing a constrained nonlinear optimization, it has been shown that in-band noise may be removed. One advantage of using prior knowledge concerning beat morphology is that a fitting error may be calculated with respect to the model, and thus we have an in-line measure of how well the procedure has filtered the ECG segment. By measuring the distance between the fitted parameters and pre-trained clusters in the 18-dimensional parameter space, classification is possible

[007S] It should be noted that the real test of the filtering properties is not the residua! error, but how distorted the clinical parameters of the ECG (such as the ST- level and QT interval) really are anύ whether they cause an abnormal beat to be erroneously classified as a normal beat. The methods of the invention produce insignificant distortion in clinical parameters for high levels of noise. For instance the model-based filter may introduce insignificant clinical distortion in the GT intervai anύ QRS width down to an SNR ≥ OdB for 1/f^ΛBeta noise for Beta < 2, The fiduciai point location may be insignificantly distorted {< 1 ms) for an SNR ≥ 2dB, and the ST-!evβl may be stabie down to SNR > 12dB, The PR-interval may be more sensitive to noise due to the low amplitude nature of the P-wave, but still robust to noise, in general, the filter performance may be degraded by increasing Beta. [0076J The method of producing confidence intervals for a particular fit, or classification is an important step in determining the performance of a particular algorithm. In-line methods such as these may facilitate the robust interpretation of data and algorithms, reducing the number of faise alarms that are triggered. In particular, the smooth nature of the fitted waveform allows for simple anά robust detection of clinical features such as the iso-eiectric point, QT-interval, and ST level. The residua! error from the fitting procedure then provides a confidence measure for the model-derived values of these features.

[0077] The above-described model has been generalized to aiiow modeling of turning points that exhibit asymmetries (such as the T-wave) by allowing such a feature to be described by two Gaussians, The mode! as such, may now be used to represent any waveform. However, the model complexity increases considerably for stochastic processes that inherently have many fluctuations compared to the sampling frequency. The main utility of the method detailed herein lies in the fact that the model represents smooth osculations with few turning points compared to the sampling frequency, and therefore has a morphology-specific multi-band pass filtering effect leading to a iossy transformation of the data into a set of integrate Gaussians distributed over time. Each ciinicai feature of the ECG waveform is represented by a known and limited set of parameters. This allows for a very compact representation of the ECG morphology and makes the description mathematically tractable and completely generaiizable to any semi-periodic signal. [007Sj Testing of the invention has resulted in accurate QT interval estimates. In contrast, it has been found that ECG analysts consistently pick the T offset to be early, since the analysts are unable to discern T-wave ends from the noise in the data. Accordingly, adaptation of the Gaussian model-based algorithm to locate Q- onset and T-offset points in a robust fashion, allows an accurate method for QT interval measurement, even in high noise situations,

[0079] It is further contemplated that the invention may utilize extra information with 12 leads with the use of a multi-channei QT anaiysis system, with noise rejection using Independent Component Analysis, Principa! Component Analysis and Frank lead reconstruction (using the (inverse) Dower transform). By determining the noise content of each lead and using these dimensiortaSity reduction techniques, the sensitivity of QT analysis to varying ievels and types of noise may be evaluated, to provide a principled on-line confidence index for each QT interval evaiuation. The relationship between the QT interval, preceding and foilowing RR intervals, and other ECG mode! parameters (P, Q, R, S, and T amplitude and duration) such as ϋ wave detection and characterization, T-wave height, and T-wave asymmetry are also contempiated by the invention.

[0080] The aigorithm and analytic framework discussed above may also be adapted in the foilowing ways:

-if Bi-phasic QRS complex and P waves are empioyed, two Gaussians can be used either when there is a substantia! asymmetry (skew greater than a given lead dependent ttireshoid for the P wave or T wave) or when there are two significant peaks for each conventiona! point (P₁ Q₅ R₁ S. T₁ or U).

-The asymmetry of each wave (T in particular) may be well modeϊed by a log- normal distribution. Therefore, other embodiments of this approach may consider log-normal distributions aSso. Disadvantages exist in that the probabiiistic interpretation is not so weil defined, but there are fewer parameters to Rt.

-QT intervai determination may be made using probabilities - as weil as using the zero-gradient criterion, to more accurately mimic the more conservative human tendency to under-estimate the end of repolarization using the M-sigma point of the two Gaussians in the T wave. This may be calculated using (for N Gaussians); mu_T = a_(N-1)^*mu_{N-1)+a_N*mu_N sigma_T * (a_(N-1)^Λ2^*sig_(N-1)^Λ2 + a_N^Λ2^*sig_N^Λ2)^Λ1Λ2. and the end of the T wave is taken to be mu_jr+M*sigma_T, where U=2 for most leads, but can take other values,

-More sensitive QT anaiysis is also possible with the mode! of the invention. There may be considerable overlap in QT/QTc between norma! and non-normal patient groups. See e.g., "The Spectrum of Symptoms and QT Intervals in Carriers of the Gene for the Long-QT Syndrome," G, M. Vincent. K, W, Timothy, M. Leppert, M, Keating, N Engl J Med. 1992; 327; 846-852. More sensitive measures of other repolarization-reiated properties may be used to revea! a more sensitive metric for classifying patients as norma! or not norma!, including:

- Measure of T-wave amplitude or relative T~wave amplitude (such as the R-peak divided by T-wave peak height);

- Asymmetry of the T-wave {such as the skewness of the JT segment);

- Length of the jT segment (as measured by the 2 sigma point defined above sigma__T = (a_(N~1}^Λ2^*sig__(N~1)^Λ2 + a__N^Λ2*sig_N^Λ2)^Λ1/2.

- Peakiness of the T-wave (such as the kurtosis of the jT segment). -Short QT syndrome (SQTS) leads to an abbreviated QTc interval and predisposes patients to life-threatening arrhythmias To date, three forms of the disease have been identified. SQTI , caused by a gain of function substitution in the HERG (IKr) channel, SGT2, caused by a gain of function substitution in the KvLQTI (Iks) channel, and, SQT3, which has a unique ECG phenotype characterized by asymmetrical T waves. See, e.g. "A Novel Form of Short QT Syndrome (SQT3) Is Caused by a Mutation in the KGNJ2 Gene," Siivia G. Priori, Sandeep V. Pandit, liaria Rivolta, Omer Berenfeld, Eiena Ronchetti, Amit Dhamoon, Cario Napoiitano, Justus Anumonwo, Marina Raffaele di Barietta, Smitha Gudapakkam, Giuiiano Bosi, Marco Stramba-Badiaie, and Jose Jaiife, Circ. Res. 96: 800-807. Therefore, the above discussion of height, skew, width and kurtosis variabies as above may be used in a SQTS application to heip improve the sensitivity of short QT analysis significantly.

-QTd (QT dispersion) is defined as the difference between the maximum and minimum QT intervals of any of 12 leads. QTd is sometimes thought to be a marker of myocardial electrical instability and has been proposed as a marker of the risk of death for those awaiting heart transpiantafion. See e g., "Development of Automated 12~Lead QT Dispersion Algorithm for Sudden Cardiac Death/^' M. 8. Malarvifi, S, Hussain, Ab. Rahsm Ab. Rahman, The Internet Journai of Medicai Technology, 2005, Volume 2 Number 2. In a similar way to QT intervals, QTd takes a Gaussian histogram of vaiues for a particular population. There is a significant cross-over between norma! and those at risk of sudden cardiac death (SCD) The mean value of QTd+ISD is 37.28 ± 11.13ms (p < 0 05) for a non-MI group and 66.17 ± 13.95ms (p < 0.05} for the Mi group. With QTd < 50ms is the threshoid for normality, but this wouid lead to 20-30% of the normals being classified as Mi and -20% being classified as non-MI. Using the height, skew, width and kurtosis variables as above would improve the sensitivity significantly.

[0081] The algorithm and analytic framework discussed above may also be used to perform very sensitive analysis of any feature of an ECG, including to:

- filter ECGs

- compress ECGs for efficient storage, transmission, and reconstruction

- perform P-wave detection (and hence atrial beat/rhythm classification)

- perform amplitude and QRS axis analysis (for deriving respiration)

- perform beat classification (from clustering the parameters)

- perform robust QT interval analysis

- perform robust ST-segmβnt analysis

- perform PQRST subtraction for high frequency QRS analysis for diagnosis of ischemic heart disease, respiration related issues, and the like

- perform PQRST subtraction for late potential analysis

- perform T-wave alternan classification

- perform rhythm analysis

[0082] In accordance with various embodiments of the invention, the methods described herein are intended for operation with dedicated hardware implementations including, but not limited to, PCs. PDAs, semiconductors, application specific integrated circuits (ASIC), programmable logic arrays, and other hardware devices constructed to implement the methods described herein. Moreover, various embodiments of the invention described herein are intended for operation as software programs running on a computer processor. Furthermore, alternative software implementations including, but not limited to, distributed processing, component/object distributed processing, parallel processing, virtual machine processing, any future enhancements, or any future protocols thereof may also be used to implement the methods described herein.

[0083] It should also be noted that the software implementations of the invention as described herein are optionally stored on a tangible storage medium, such as: a magnetic medium such as a disk or tape; a magneto-optical or optical medium such as a disk; or a solid state medium such as a memory card or other package that houses one or more read-only (non-volatile) memories, random access memories, or other re-writable (volatile) memories. A digital file attachment to email or other self- contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. Accordingly, the invention is considered to include a tangible storage medium or distribution medium, as listed herein and including art-recognized equivalents and successor media, in which the software implementations herein are stored.

[0084] While the invention has been described in terms of exemplary embodiments, those skilled in the art will recognize that the invention can be practiced with modifications in the spirit and scope of the appended ciaims. These examples given above are mereiy iliustrative and are not meant to be an exhaustive list of all possible designs, embodiments, appiications, or modifications of the invention. For example, the invention may fit a set of aiternate basis functions to the signai, perhaps using some other form of optimization, may use other signais other than physiological signais; may use any set of basis functions, not just Gaussians; may use any optimization routine to fit the basis functions to the observation - ieast squares, nonϊinear least squares, gradient descent with any cost function and any activation function (such as tanh or softmax in a neural networK). Moreover, IIR/FIR filters, independent Component Anaiysis (ICA); Principai Component Analysis (PCA) / Singular Vaiue Decomposition (SVD) / Karhunen Loeve Transform (KLT) / Hoteliing Transform; Auto-Regressive (AR) modeiing - equivaient to Fourier Transform; and Wavelet Analysis (Laguna et al, Hughes et al.) approaches may also be used for further pre-processing or post- processing.

Claims

WHAT IS CLAIMED;

1. A system for the collection and analysis of physioiogicai data obtained from a remote facility, said system comprising: a sensor system collecting physiological data from at least one of a wearable sensor and an implantable sensor configured to sense characteristics of a subject iocated in a faculty; a computer system disposed remote from the facility and configured to receive the physiologicai data from said sensor system via a network; a storage device configured to archive the physiological data received by said computer system; and said computer system being configured to stream the physiological data to a plurality of locations for the collaborative analysis thereof.

2. The system according to claim 1 wherein one of said computer system and a processor is configured to execute an algorithm to analyze the physiologicai data.

3, The system according to claim 1 wherein said sensor is structured %nά arranged to sense at least one of biood pressure, central venous pressure, pulmonary arterial pressure, pulse oximetry (SAO2), cardiac sounds, non-cardiovascuiar signals such as EEG K-compiexes, muscular activity, neural activity, acoustic waveforms, and speech waveforms,

4. The system according to claim 1 wherein the physioiogicai data comprises an ECG, the system further comprising: a processor to generate a nonlinear signal model based on the ECG signal, fit the noniinear signal model to the ECG signal based on an optimization algorithm, and determine at least one feature of the ECG with the noniinear signal modei; and an output device to output the at least one feature of the ECG based on the nonlinear signal model.

5. The system according to the claim 1 wherein said computer system is configured to receive physiological data from a plurality of wearable sensors and/or implantable sensors from the network.

6. The system according to the claim 1 wherein said computer system comprises a platform.

7. The system according to the claim 6 wherein said platform comprises a Hermes platform.

8, The system according to claim 1 wherein said storage comprises a redundant array of independent disks.

9, The system according to claim 1 wherein the sensor transmits the physiological to said computer system via one of a wired connection and a wireless transceiver,

10, The system according to claim 1 wherein the subject is one of a human and an animal.

11. The system according to claim 1 wherein the facility comprises an off-site research facility.

12, The system according to claim 1 wherein the pluraiity of iocations comprises at least one of a research center academic facility, physician's office, anό clinician's office.

13. A process for the collection and analysis of physioSogicaS data obtained from a remote facility comprising the steps of; obtaining physiological data concerning at least one subject from a sensor associated with a subject located in a facility; transmitting the physiological data to a centralized location remote from the facility; streaming the physioiogicai data from the central location to at least two locations remote from the facility and the centralized location; and analyzing the physiological data with at least one algorithm during, prior to, and/or subsequent to the one or more of the obtaining, streaming, and analyzing steps.

14. The process according to claim 13 further comprising the step of analyzing the data at one of tne plurality of locations.

15. The process according to claim 13 wherein the transmitting step comprises transmitting the physioiogicai data from a sensor via at least one of wireless transmission and wired transmission.

16, The process according to claim 13 wherein the obtaining step compnses obtaining the physiological data from at least one of an implantable sensor &nά a wearable sensor.

17, The process according to ciaim 13 further comprising the step of archiving the physioiogicai data at the centralized location.

18, The process according to claim 13 further comprising the step of visualizing the physiological data at tne central location.

19. The process according to ciaim 13 further comprising the step of enabling the collaborative interaction of a plurality of physicians or analysts at a plurality of locations.

20, The process according to claim 13 wherein the subject is one of a human and &n animal.

21. The process according to claim 13 wherein the facility comprises an off-site research facility.

22. The process according to claim 13 wherein the plurality of locations comprises at least one of a research center, academic facility, physician's office, and clinician s office.

23. The process according to claim 13 where the physiological data comprises an ECG, the process further comprising the steps of: generating a nonlinear signal model based on the EGG signal; fitting the noniinear signal mode! to the ECG signal based on an optimization aigorithm; determining at least one feature of the ECG with the nonlinear signal model; and outputting the at least one feature of the ECG based on the noniinear signal motiei.

24, A system for the collection and analysis of physioiogicai data obtained from a remote facility, said system comprising: means for coliecting physiological data from at least one of a wearable sensor and an impiantable sensor configured to sense characteristics of a subject iocated in a faciiity, means for receiving the physiological data from said sensor system via a network disposed remote from the facility; means archiving the physioiogical data received by said collecting means; anό means for streaming the physiological data to a plurality of locations for the collaborative analysis thereof,

25. A computer readable medium having instructions stored thereon that when executed by a processor provides for the coilecfion and analysis of physiological data obtained from a remote facility comprising: instructions for obtaining physioiogicai data concerning at ϊeast one subject from a sensor associated with a subject located in a facility; instructions for transmitting the physioiogicai data to a centralized location remote from the faciiity; instructions for streaming the physiological data from the centra! location to at least two locations remote from the facility and the centralized location; and instructions for analyzing the physiological data with at least one algorithm during, prior to, and/or subsequent to the execution of one or more of the obtaining instructions, streaming instructions, and analyzing instructions.