WO2006013585A1 - Procede et equipement pour piloter la fonctionalite de la moelle epiniere - Google Patents

Procede et equipement pour piloter la fonctionalite de la moelle epiniere Download PDF

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Publication number
WO2006013585A1
WO2006013585A1 PCT/IT2004/000440 IT2004000440W WO2006013585A1 WO 2006013585 A1 WO2006013585 A1 WO 2006013585A1 IT 2004000440 W IT2004000440 W IT 2004000440W WO 2006013585 A1 WO2006013585 A1 WO 2006013585A1
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WIPO (PCT)
Prior art keywords
signal
electric
stimulation
equipment
potential
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PCT/IT2004/000440
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English (en)
Inventor
Francesco Bracchi
Lorenzo Rossi
Anna Caterina Merzagora
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Universita' Degli Studi Di Milano
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Priority to PCT/IT2004/000440 priority Critical patent/WO2006013585A1/fr
Publication of WO2006013585A1 publication Critical patent/WO2006013585A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
    • A61B5/407—Evaluating the spinal cord
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B2018/00315—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body for treatment of particular body parts
    • A61B2018/00434—Neural system
    • A61B2018/0044—Spinal cord

Definitions

  • the present invention relates to a method and equipment for monitoring the functionality of the spinal cord of a living creature.
  • the monitoring of the functionality of body organs, and generally the vital parameters of a living creature is known to be a basic aspect in up-to-date surgery techniques.
  • a continuous monitoring of the spinal cord functionality is required.
  • the integrity and functionality of the cord may be compromised by manoeuvres performed by the surgeon to such an extent as to cause, for example, a prolonged insufficient blood afflux to the cord and, consequently, the temporary malfunctioning of the latter.
  • manoeuvres can cause a permanent and irreversible damage to the spinal cord and determine, in the most serious cases, the paraplegia of the living creature.
  • monitoring the cord functionality during surgery is aimed at early detecting states of cord distress such as to be able to remedy before any irreversible damage may occur.
  • the monitoring methods currently used are still at the experimental stage and cannot provide sufficiently accurate results. Particularly, said methods are based on the stimulation of the nervous system with subsequent observation of effects induced by these stimulations which may be related with the functionality of spinal cord.
  • some are invasive in that they provide the bloody positioning of catheters provided with stimulating electrodes and recorders within the vertebral canal.
  • These monitoring methods are disadvantageous not only because they are difficult to be implemented, but also because they may give rise to cord lesion or infection. Furthermore, sometimes the electrodes cannot be held in position throughout the surgery.
  • the monitoring method based on the detection of the somatosensory evoked potential is performed by electrically stimulating a peripheral nerve of the living creature's body by means of electrodes placed proximate to the nervous fiber, for example through a transcutaneous stimulation at the popliteal canal (on the rear side of the knee) .
  • the method provides the recording of a electroencephalographic potential (EEG) by means of further electrodes positioned on the scalp proximate to the somatosensitive cerebral cortex.
  • EEG electroencephalographic potential
  • this monitoring method provides carrying out subsequent series of electric stimulations, each series consisting of at least fifty stimulations, with consequent recording of the cerebral activity at each stimulation in the series, i.e. fifty electroencephalographic potentials (EEG) are recorded corresponding to each stimulation in the series.
  • EEG electroencephalographic potentials
  • Each recorded electroencephalographic potential can be assimilated to a background noise which includes the SEP potential, i.e. the signal to be detected.
  • the ' SEP potential corresponding to the series of fifty stimulations can be extracted by averaging the EEG potentials recorded.
  • the noise, i.e. the EEG potential, superimposed to SEP potential can be eliminated only by assuming it is white.
  • the hypothesis of the EEG potential as being a white noise is not a correct one, since it has a not null autocorrelation and further the somatosensitive evoked potential is not a time-invariant process, all the more in long-time surgery.
  • the monitoring method based on the detection of the motor potential provides that the motor cerebral cortex is stimulated by means of magnetic pulses. Subsequent to this stimulation, the motor cortex generates nervous pulses, or action potentials, propagating along the nervous pathways to the peripheral muscles. By being excited by the action potentials, these muscles generate muscular electric potentials which can be detected and recorded by means of electromyographs.
  • the monitoring method based on the H-reflex detection provides a stimulation of a body' s peripheral nerve which controls the sensory and motor functions. In response to this stimulation, the method provides recording the potentials produced by the contraction of the addressed muscular bundles, i.e. the myoelectric potentials.
  • Each of the above monitoring methods is implemented by known, marketed equipment, such as electromyographs, electroencephalographs, electric and magnetic stimulators. Each of the above monitoring methods has drawbacks from the neurophysiologic point of view.
  • a drawback of the method for detecting the motor potential lies in the difficulty of magnetically stimulating inner portions of the motor cerebral cortex, such as the portion controlling the muscles of lower limbs. Accordingly, only the functionality of the cord portion controlling the upper motor pathways of the body can be effectively inspected by this method. For this reason, the motor potential method is applied only for research purposes and is not a clinical routine.
  • the monitoring method based on the detection of H-reflex allows to inspect only the ascending and descending neural pathways running within the spinal cord portion located approximately beneath the ninth dorsal vertebra. As a consequence, some functional alterations of the cord portion extending above the ninth vertebra either cannot be detected or can be only indirectly detected.
  • results achieved by the above monitoring methods can also change as a function of the living creature being monitored, the anesthesia techniques used, the body temperature and the spinal cord temperature. Accordingly, since the variability of the recorded signal not always depends on alterations of the cord functionality, wrong results (false positives) may be obtained.
  • the object of the present invention is to provide a method for monitoring the spinal cord functionality allowing to overcome the drawbacks of the above conventional methods.
  • Figure 2 is a functional block diagram of an inner structure of a portion of the equipment from Figure
  • FIG. 1; - Figure 3 is a block diagram of a stimulation module of the equipment from Figure 1;
  • FIG 4 is a block diagram of an acquisition module of the equipment from Figure 1;
  • Figure 5 schematically illustrates an exemplary application of the equipment from Figure 1 to a living creature's body to monitor the cord functionality thereof;
  • Figure 6 illustrates an exemplary neural network implemented by a block of the structure from Figure 2;
  • Figures 7A and 7B are waveform diagrams of an electroencephalograph ⁇ potential and a somatosensory evoked potential, respectively;
  • FIG 8 is a waveform diagram of a H-reflex potential.
  • Equipment 100 for monitoring the spinal cord functionality in accordance with the invention is described with reference to Figures 1, 2, 3 and 4.
  • This equipment 100 allows to simultaneously detect and compare electric signals of biological nature or bio-signals excited in a living creature's body (such • as, particularly, a human being) in response to suitable electric stimulations.
  • the above bio- signals are a H-reflex muscular potential and a somatosensory evoked potential (SEP) obtained from the recording of an electroencephalograph ⁇ potential (EEG) .
  • SEP somatosensory evoked potential
  • equipment 100 comprises a main body 8 to manage the monitoring operation and electric connection means 5, 6 and 7 between the main body 8 and the living creature's body (not shown in Figure 1) whose cord functionality is desired to be monitored.
  • These connecting means 5, 6 and 7 are embodied by electric cables having connectors 9, 10, 11, respectively, for connection to the main body 8 on the one end thereof, and on the other end a plurality of metal terminals or electrodes 14-21.
  • the metal terminals of cables 5 and 6 are first 14, 15, 16 and second 17, 18, 19 recording electrodes, respectively, whereas the metal terminals of cable 7 are stimulating electrodes 20 and 21.
  • the stimulating electrodes 20 and 21 are electrically isolated from the recording ones in order to supply electric stimulations to the living creature to be monitored.
  • the first recording electrodes 14-16 comprise an anode 14 and a cathode 16 to detect a first electric signal Sl referred to a common electrode 15.
  • This first signal Sl is an electric analogue signal indicating an electroencephalograph ⁇ potential EEG from which the evoked potential SEP can be extracted according to modalities which will be described below.
  • the second recording electrodes 17-19 comprise the anode 17 and cathode 19 to detect a second electric signal S2 referred to the common electrode 18.
  • This second signal S2 is an electric analogue signal indicating the H-reflex muscular potential.
  • the main body 8 comprises further connectors 12 through which equipment 100 can be connected to a supply voltage and advantageously to further external equipment (such as mass-storaging devices) or to control interfaces (e.g., mouse, electronic processor keyboards, etc.) .
  • equipment 100 can be connected to a supply voltage and advantageously to further external equipment (such as mass-storaging devices) or to control interfaces (e.g., mouse, electronic processor keyboards, etc.) .
  • equipment 100 comprises a touch screen 13 to promote a quick communication between equipment 100 and an external operator (e.g. sending commands to the equipment or receiving information therefrom) who will simply have to touch specific areas on the screen 13 to send selections, command signals, etc. to the equipment 100.
  • an external operator e.g. sending commands to the equipment or receiving information therefrom
  • the monitoring results can be viewed on screen 13.
  • an inner structure 200 of main body 8 of equipment 100 can be provided in a plurality of functional blocks or modules, either of the analogue or digital types, such as schematically illustrated in Figure 2.
  • some of the above blocks are provided as circuital modules, i.e. with a hardware, whereas others are provided as software modules, i.e. program instruction sequences.
  • Structure 200 comprises a main block or system control unit 22 (UCS) .
  • This unit 22 is, for example, a microprocessor, a microcontroller or a DSP (digital signal processor) intended to run a dedicated program, i.e. a series of machine instructions implemented therein.
  • the system control unit 22 interacts with a stimulation module 24 (MOD STIM) by exchanging with it first information 23 of a numerical type to control the delivery of the electrical stimulus to the living creature's body to be monitored.
  • the exchanged information 23 are bit flows relating to characteristics of the electric stimulus such as width, frequency and duration of the stimulus as well as data indicating the amount of electric charge supplied per each stimulus.
  • structure 200 comprises a first 27 (MOD ACQl) and a second 28 (MOD ACQ2) acquisition modules of first Sl and second S2 electric signals, respectively.
  • these acquisition modules 27 and 28 are electronic devices comprising circuits (not shown in detail in Figure 2) to receive and filter the above signals and in which the parameters (amplification gains, cut-off frequency) can be modified by unit 22 through control instructions 25 and 26 of a numeric type (bit flows) .
  • a first SdI and a second Sd2 digital signals on output from the acquisition modules 27 and 28 are obtained by performing an analogue/digital conversion of the electric signals Sl and S2.
  • the digital signals SdI and Sd2 are bit sequences codifying samples of the first Sl and second S2 electric signals, respectively.
  • These signals SdI and Sd2 are the input information of the control unit 22 and, particularly, of a first 33 (MOD VAL 1) and a second 34 (MOD VAL 2) evaluation module of the signal quality, respectively.
  • the evaluation modules 33 and 34 are made in logic blocks of a program implemented by the system control unit 22.
  • unit 22 interacts with modules 33 and 34 through first 31 and further first 32 instructions to modify procedures and evaluation functions, respectively, and through second 31' and further second 32' instructions to receive the evaluation results.
  • the first 31, 32 and second 31', 32' instructions are information of the digital type.
  • first 33 and second 34 evaluation modules can be also provided within a hardware structure, i.e. by means of an electronic device, such as a DSP.
  • the first 33 and second 34 evaluation modules can be connected to first 35 (ELAB DIG 1) and second 36 (ELAB DIG 2) digital processing modules of digital signals SdI and Sd2, respectively, by means of logical switches managed by activating commands 201 of the system control unit 22.
  • These processing modules 35 are 36 logic blocks of a program implemented by the control unit 22 to process the acquired signals.
  • the results of the above processing are provided at the control unit 22 by means of further instructions 37 and 38 of the digital type.
  • a memory module 40 is connected to the system control unit 22 to store first numerical data 39, i.e. bit flows corresponding to results of the processing of signals SdI and Sd2.
  • these storing means are embodied in a non-volatile memory of the flash type.
  • a display module 213 (VIS) is connected to unit 22 to receive second numerical data 41 to be displayed.
  • the display module 213 includes the touch screen 13 helping the quick transmission of external commands 42 to the control unit 22.
  • a preferred embodiment of the stimulation module 24 of the invention is described with reference to the block diagram of Figure 3. It should be observed, particularly, that the stimulation module 24 is an electronic device comprising digital and analogue circuits each of which is represented by a block within said diagram.
  • the module 24 comprises a digital control unit 41 (CONT DIG) to control and interpret the first numerical information 23 coming from the system control unit 22.
  • the digital unit 41 interfaces with a stimulation isolation unit 43 (UIS) of the analogue type through a digital-analogue converter 42 (D/A CONV) .
  • a reference analogue voltage 240 is transferred to the isolation unit 43 to control the amplitude and waveform of the generated electric stimulus.
  • a timing digital signal 241 directly supplied by the digital unit 41 to the isolation unit 43, allows to adjust the frequency and duration of the electric stimulus.
  • the configuration of the output stage of the isolation unit 43 may change depending on the stimulation mode employed. In fact, it is possible to perform either current stimulation or voltage stimulation.
  • the isolation unit comprises a high-impedance output stage outlined by block 44.
  • this output stage is embodied by a DC-DC isolation converter to transfer a low supply voltage to a high voltage.
  • a power transistor which can be activated by the transferred high voltage in order to act as a current generator and supply the desired stimulus current.
  • the stimulation module 24 can further comprise a suitable circuitry (not illustrated in Figure 3) to calculate the amount of electric charge supplied with the stimulus and send the result of this operation to the system control unit 22 in a digitalized form.
  • the output stage of the isolation unit 43 is a low-output-impedance stage comprising for example an isolated power transformer outlined by block 45.
  • module 24 comprises a current measuring circuit 46 (MIS CORR) to detect the current delivered following the voltage stimulation.
  • This measuring circuit 46 is for example a amperometric clamp comprising an isolation transformer.
  • the stimulation module 24 comprises an integration circuit block or integrator 47 (INT) and an analogue/digital converter 48 (A/D CONV) to evaluate the amount of charge corresponding to the measured current and supply such information to the digital control unit 41 in a digital form, respectively.
  • INT integration circuit block or integrator 47
  • A/D CONV an analogue/digital converter 48
  • the circuit architecture 400 of each acquisition module 27, 28 comprises electrodes 49 and 50 to record signals relative to a common electrode 51.
  • This common electrode 51 is indicated by a ground potential and placed away from the recording spot.
  • the electrodes 49-51 correspond to electrodes 14-16 from Figure 1 if the architecture 400 implements the first acquisition module 27, whereas if it implements the second module 28 they will correspond to electrodes 17-19.
  • electrodes 49 and 50 are connected to a high-impedance differential input stage 52 (ST IN) .
  • this input stage 52 is provided by a low-noise operational amplifier in a buffer configuration.
  • the input stage 52 is connected to a first analogue filter 53 (FIL AN) of the high-pass type having a modifiable cut-off frequency.
  • FIL AN first analogue filter 53
  • the cut-off frequency of the filter is 0,01 Hz
  • second module 28 it is 10 Hz.
  • the architecture 400 provides a differential amplifier 54 (AMP) cascade-connected to the first filter 53 and having a high common mode rejection ratio CMRR.
  • CMRR common mode rejection ratio
  • a preferred value of the common mode rejection ratio is 120 dB.
  • the amplification factor of amplifier 54 is different for the first 27 and second 28 acquisition module. In the case of first module 27 the amplification factor is, for example, 10.000 (80 dB) , whereas in the second module 28 the amplification factor is, for example, 100 (40 dB) .
  • a second analogue filter 55 (FIL) of the low-pass type is provided.
  • the cut-off frequency can be also modified, and may- take values for example of 0,1 kHz and 10 kHz for first 27 and second 28 modules, respectively.
  • all blocks described in the circuit architecture 400 have a ground reference potential in common and are connected to the common electrode 51.
  • an electric isolation is provided, which is outlined in Figure 4 with an isolation block 56
  • this isolation block 56 is situated on output of the applied part to supply a isolated supply voltage 58 to the circuit blocks of the applied part.
  • isolation block 56 is connected to an input of a further analogue-digital converter 57 (A/D) having, for example, a 12 bit resolution.
  • A/D analogue-digital converter 57
  • both bio-signals also involves the same periferal nerve endings and that each of them depends on the integrity of the spinal cord nervous pathway.
  • a state of cord distress such as a cord lesion, influences both of these bio-signals (SEP and H-reflex) and that, advantageously, this state can be detected by noticing a simultaneous variation of both bio-signals.
  • a monitoring method of the spinal cord functionality of the invention provides, first of all, the simultaneous detection of first Sl and second S2 electric signals indicating the electroencephalograph ⁇ potential EEG and the H-reflex muscular potential, respectively.
  • said bio-signals EEG and H-reflex are generated in the body of the living creature to be monitored in response to the same electrical stimulation.
  • this electrical stimulation may be one single electric pulse, a train of electric pulses, or biphasic electric pulses.
  • this electrical stimulation will be assumed as being one single electric pulse.
  • equipment 100 allows to perform monitoring in accordance with the above method.
  • this equipment 100 provides suitable electric pulses to stimulate the living creature's body to be monitored by detecting and processing the electric signals Sl and S2 thus generated in real time.
  • equipment 100 is connected to the living creature's body to be monitored by means of the first 14-16 and second 17-19 recording electrodes and by means of the stimulation electrodes 20 and 21.
  • the living creature to be monitored has been schematically represented by encephalon 501, a vertebral column 502 (i.e. the spinal cord) and a leg 503.
  • the stimulation electric pulse is supplied transcutaneously, by positioning the stimulation electrodes 20 and 21 of equipment 100 on a knee 504 of leg 503. Particularly, one of both electrodes (for example, electrode 20) is positioned on the posterior side of knee 504 proximate to a popliteus muscle and a posterior tibial nerve, whereas the other one (now shown in Figure 5) is placed on the front side of knee 504.
  • This pulse supplies a suitable amount of electric charge thus selectively stimulating both nervous fibers connected to a soleus gastrocnemius muscle group of leg 503 and cutaneous sensitive fibers of the posterior side of knee 504.
  • An action potential i.e. a nervous pulse generated by the electric stimulation propagates through the body towards the central nervous system via the above- mentioned nervous fibers. This propagating step is outlined by a first path 506 of Figure 5.
  • the stimulated nervous fibers enter the vertebral canal (not shown in Figure 5) of vertebral column 502 such that the action potential being generated may propagate in the direction of the encephalon 501 thus exciting the sensitive fibers arranged on the right or left side (according to the stimulated leg) of cord.
  • these sensitive fibers when stimulated excite a population of peripheral nervous cells of their own or motoneurons present in the cord grey matter located in the front part thereof.
  • the motoneurons give rise to a reflex response to the electric stimulus supplied with electrodes 20 and 21.
  • This reflex response is a further electric potential the characteristics of which depend on the parameters of the electric stimulation and the functionality of the nervous pathways involved.
  • This further potential produced by the motoneurons propagates along nervous pathways within the body, such as schematically shown by a second path 507, until stimulating the soleus-gastrocnemius muscle of leg 503.
  • the excitation of the soleus muscle following the stimulation generates the H-reflexed bio-signal which can be detected in the form of electric potential.
  • this electric potential is the second signal S2 detected by means of the second recording electrodes 17-19 positioned at a first recording site 517 proximate to a calf 509 of leg 503.
  • the nervous electric potential generated by the electric pulse applied to the knee 504 is transmitted to the spinal cord through cutaneous sensitive fibers and by further nervous fibers which are not involved in the generation of the H- reflexed bio-signal.
  • said fibres enter the vertebral canal and move forward to the encephalon 501 such as schematically shown by a third path 510.
  • the action potentials being transmitted stimulate a portion of the sensitive cerebral cortex.
  • the cerebral cortex generates the somatosensitive evoked potential SEP which sums up to the electroencephalograph ⁇ potential EEG.
  • This sum of potentials is the first electric signal Sl which can be detected by means of the first recording electrodes 14-16 positioned at a second recording site 514 on the living creature's scalp.
  • EEG potential can be assimilated to a background noise superimposed to the evoked potential SEP which one desires to analyse.
  • the first electric signal Sl should be suitably processed to be indicative of the evoked potential SEP.
  • second signal S2 should not be subjected to excessive processing to supply the information required on the H-reflexed potential.
  • the electric signals Sl and S2 are amplified and filtered at the first 27 and second 28 acquisition module, respectively ( Figure 4) .
  • the amplification factor of the differential amplifier 54 used in modules 27 and 28 is different for the first Sl and second S2 signal in that these signals have different amplitudes.
  • the amplitude of the first electric signal Sl is in the range of ⁇ V (for example, 30-40 ⁇ V for potential EEG)
  • the amplitude of second signal S2 is in the range of mV (for example, 1-10 mV for H-reflexed signal) .
  • the peak amplitude of each signal is thus optimized to favour the proper functioning of the analogue/digital converter 57.
  • the components of signals Sl and S2 of lower or higher frequencies than those of interest are eliminated by the analogue filters 53 and 55, respectively.
  • these frequency components may be originated by external disturbances, in which case they are not significant in the subsequent processing step.
  • the analogue electric signals Sl and S2 are sampled and quantized by converter 57. Quantization levels of the samples per each signal are coded by means of bit sequences (for example 12 bit) to be sent to first 33 and second 34 evaluation modules as the first SdI and second Sd2 digital signals, respectively.
  • the samples of these signals are contained in a time interval or recording "window" having a preset, though modifiable duration. This time interval is known to those skilled in the art as the recording sweep.
  • the recording sweep has a duration of 125 ms and contains 320 signal samples.
  • the sweep has, for example, a duration of 62,5 ms and contains 640 signal samples.
  • equipment 100 allows to acquire a further first Sl' and a further second S2' electric signals which are test signals indicating the encephalographic potential bio- signal EEG and an electromyographic muscular potential bio-signal EMG, respectively. Contrarily to signals Sl and S2 detected following the electric stimulation, these test signals Sl' and S2' indicate the potentials EEG and EMG existing in the living creature's body being monitored regardless of the stimulation pulse being sent.
  • a suitable timer of equipment 100 sets the time instants for the acquisition of said signals.
  • signals Sl' and S2' are acquired every 10-15 seconds.
  • the recording sweeps containing the signal samples Sl' and S2' are analysed by the evaluation modules 33 and 34 ( Figure 2) to check that the potential EEG and EMG are not temporarily altered or degraded by external disturbing signals (such as caused by employing a electrosurgery unit during a surgery to the vertebral column) .
  • the processing of altered or modified signals may supply wrong and contradictory information on the cord functionality thus nullifying the objective of an early diagnosis of states of cord distress.
  • the first 33 and second 34 evaluation modules check that these signals meet preset principles or evaluation parameters.
  • module 33 checks that the amplitude of said signal Sl' does not exceed maximum and minimum values, i.e. this amplitude always takes values comprised within a preset variation range. For example, this variation range is -I V - I V.
  • the amplitude of the low-frequency oscillation of signal Sl' takes place if, at the signal baseline, excessive ripples are present.
  • the presence of too abrupt oscillations of the acquired signal is examined by means of the analysis of the first derivative of the same signal .
  • the amplitude is checked of those high- frequency components of signal Sl' which have not been eliminated by the analogue low-pass filter 55.
  • module 33 sends a stimulation-starting signal to the system control unit 22 through second instructions 31' .
  • the EEG potential is acquired to be processed to extract the evoked potential SEP contained therein.
  • second instructions 31' sent to unit 22 contain a wait signal for the temporary inhibition of stimulation.
  • module 34 checks that the amplitude of said signal always takes values comprised between a preset variation range (for example, -IV - IV), i.e. such as to avoid the saturation of the acquisition stage. If signal S2' meets this evaluation parameter, module 34 sends a stimulation starting signal to the system control unit 22 through further second instructions 32' . In this case, a H-reflex potential can be acquired. On the contrary, these further second instructions 32' contain a wait signal.
  • control unit 22 commands the stimulation module 24 through first information 23 for sending a stimulation pulse.
  • the system control unit 22 is able to process digital data at high frequency (such as in the range of hundreds MHz) , the evaluation of the quality of a single sweep relative to acquired signals Sl' and S2' is a very quick operation. In this case, it is legitimate to assume that the recording sweeps relative to the electric signals detected following this pulse (such as the above Sl and S2 signals) are exempt from disturbances and hence processable. Accordingly, the control unit 22 transfers these sweeps to the digital processing modules 35 and 36 through the activation command 201.
  • each stimulation pulse can only be applied to the living creature's body after a suitable time has passed from the previous pulse.
  • the nerve centers controlling the generation of the H-reflex bio-signal require a recovery time of about 6-7 seconds to return to optimum conditions to generate a new bio-signal.
  • a time interval of about 10 seconds between one stimulation delivery and the next one is provided.
  • this module 35 processes the digitalized samples (i.e. the first digital signal SdI) of first signal Sl indicating the electroencephalograph ⁇ potential EEG in order to extract the evoked potential SEP bio-signal to be analysed and displayed.
  • the processing module 35 reconstructs the evoked potential S relative to a single recording sweep starting from a sum of mathematical functions, for example, of the gaussian type.
  • the module 35 implements a multi-layer Radial-Basis Function Neural Network 600 such as schematically illustrated in Figure 6.
  • the neural network 600 is a feedforward unbiased neural network the general characteristics of which are known to those skilled in the art. This network 600 comprises three layers.
  • the vector X is sent to N neurons 604 of a hidden layer 602.
  • an activation function having radial symmetry for example, a gaussian function designated with N( ⁇ -j, ⁇ ) (with j varying between 1 and N) .
  • These gaussian functions have their respective centers, being coincindent with the mean values ⁇ j , located within the time interval of the single recording sweep.
  • said gaussian functions have different mean value ⁇ j from one another, i.e. the gaussian centers are located in fixed points different from one another within the single recording sweep.
  • 25 gaussian functions can be set per single 125 ms recording sweep, i.e. one every five ms. All gaussians of neurons 604 have the same spread ⁇ .
  • the neural network 600 comprises an output layer 603 to perform a sum of neuron outputs of the hidden layer 602 weighted through weights Wj (with j varying from 1 to N) .
  • the output of the neural network can be calculated as:
  • index i denotes the i th sweep taken at the i th stimulus.
  • Y(X) is indicated the estimated value of the evoked potential S
  • ⁇ j and ⁇ 2 are the mean and variance of a gaussian-type function associated to a j th neuron in the network.
  • w (l) j is the weight attributed to the output of the same neuron for the i th recording sweep.
  • H is a N X M-sized matrix in which each line is a time sequence of the M outputs of the j th neuron of the hidden layer 602,
  • H j [H j (l)...H j (k)...H j (M)] (4) whereas W (l> is a column vector containing the N neural weights of the i th experimental test.
  • the overall operation of the network depends on the neuron number N of the hidden layer, on the location of the gaussian centers ⁇ j and on the spread value ⁇ .
  • the neuron number N of the hidden layer is correlated to the shape of the signal to be detected, particular reference is made to the number of signal peaks.
  • the gaussian centers are a function of the number N of gaussians and the extension M of the time sweep.
  • a basic parameter related to the monitoring of the spinal cord functionality is the time interval of a peak occurrence in the waveform of the evoked potential S which is defined as signal latency. It has been observed that the fixed distribution of the centers of the gaussian functions in accordance with (2) is misleading for the evaluation of the latency. For this reason, it has been chosen to adaptatively change the center ⁇ j* of a gaussian while maintaining the other centers fixed.
  • the common value of the gaussian spread ⁇ (i.e. of variance ⁇ 2 ) is preset and is not changed over processing since this does not degrade the ability of the neural network 600 to approximate the evoked potential S.
  • the spread ⁇ is linked both to the neuron number N of the hidden layer and the sample number M of the sweep according to the relation:
  • an experimental parameter ⁇ is set relative to a compromise between the ability to follow changings both at high-frequency and low-frequency of the signal to be detected.
  • the amplitude of the gaussian used will be greater or lower based on a greater or lower value than ⁇ .
  • the parameter ⁇ is fixed for all gaussians unless an external action is performed to change it.
  • the detection of the evoked potential S is performed by instructing the neural network sweep by sweep, i.e. adaptatively updating the weights W (l) on output from each neuron and the location of the center ⁇ -j * of a gaussian function.
  • this is achieved by the minimization of the mean square error between the estimated output of the neural network Y and the detected signal SdI.
  • the output of the neural network Y (l) and the detected signal SdI (l) are compared and the mean square error is calculated.
  • this error is expressed in a matrix form as:
  • the adaptation is performed in two steps: the first one is connected to the updating of weights W (l) and the second one is connected to the displacement of center ⁇ -j * .
  • the expression of the mean square error (7) can be modified as follows:
  • E[E (i)T E ⁇ i> ] E[(S (i) -Y (i) ) T (S (i) -Y (i) )]+E[Ns (i)T Ns (1> ] (8)
  • E [Ns (l)T Ns (l> ] can be considered as being independent from W (l) and the placement of the centers, the mean square error is minimized when E[Y (l) ]- ⁇ S (l> .
  • the weight convergence is performed by using a Least Mean Squares algorithm or LMS expressed in a matrix form:
  • W (i + D w ⁇ i > +2 ⁇ H(Sdl (i> -Y (i) ) (9)
  • ⁇ e is a convergence rate.
  • the (9) indicates that the updated weight w (l+1> depends on the weigth at the previous step W (l) and the difference between the detected signal and the output of the neural network, i.e. Sdl (l) -Y ⁇ x) .
  • the convergence rate ⁇ controls the convergence speed and the convergence stability. In fact, with a low convergence rate ⁇ , greater stability is obtained against slow learning by the neural network, whereas with a high ⁇ rate, a greater learning speed is obtained against a greater residual noise.
  • the center ⁇ -j * of a gaussian function is iteratively updated. This is done by gradually moving this center over the entire interval comprised between the preceding center and the subsequent one and stopping the movement when the mean square error reaches the minimum value. In greater detail, if an iteration step is set to 0,05, at the beginning of the same iteration the center ⁇ - j* is set to ⁇ -j * -0,05 and the mean square error is evaluated.
  • FIG. 7A is a diagram of a first waveform of an electroencephalograph ⁇ potential EEG (relative to a single 125 ms recording sweep) before the latter undergoes the processing as described above. This signal has been obtained following the stimulation of the living creature's body during a surgery.
  • Figure 7B is a diagram of a second waveform relative to an evoked potential SEP reconstructed following the processing of signal EEG of Figure 7A. From the analysis of this second waveform information on the spinal cord functionality are obtained which are referable to the amplitude of evoked potential SEP and latency L, i.e. the time elapsing between the beginning of the recording sweep and the occurrence of a peak 700 of the waveform.
  • the above processing method provides the adaptative variation of the center ⁇ - j* of the gaussian function located proximate to a negative peak 701 of the waveform of the registered signal EEG. Thereby, the result of the processing is the evoked potential SEP.
  • the latter operates to obtain from the samples of each sweep of signal S2 a maximum value for this signal and the instant of occurrence of such maximum value.
  • FIG. 8 the diagram of a further waveform relative to a H-reflex potential is illustrated in Figure 8.
  • Useful information on monitoring derived from the analysis of the latter waveform concern the amplitude and a further latency L' relative to a further peak 800.
  • the results of the processing performed by modules 35 and 36 are sent to the system control unit 22 and from the latter to the display module 213.
  • the waveforms of the evoked potential SEP and the H-reflex being generated at one single electric stimulus can be displayed on the touch screen 13 of equipment 100.
  • the reference signal is calculated by a recurrent synchronous average of the preceding recordings:
  • r t ⁇ *r t ⁇ +(l- ⁇ )*x, (10) where xi and ri are the current recording and reference mean, respectively.
  • xi and ri are the current recording and reference mean, respectively.
  • the recording should be continuous, in order to avoid having to initialize the filter at each sweep.
  • ARX filters besides allowing the extraction of the single evoked potential, also allows a parametrisation thereof such as suggested by the method in Cerutti S., Baselli G., Liberati D., Pavesi G.,
  • the i th recording Xi is given by the composition of the electroencephalographic activity, in this case being considered as the noise, and the evoked potential, i.e. the signal of interest; it is assumed that both these components are uncorrelated to each other.
  • the EEG signal of disturbance n ⁇ (k) is modelled as an AR process driven by white noise e(k) whereas the single evoked contribution Ci(k) is estimated by filtering the reference signal ri(k), thus obtaining the following generation model:
  • n,m,d The value of the optimum triplet (n,m,d) is found by means of the minimization of FPE (Final Prediction Error) and AIC (Akaike Information Criterion) functionals.
  • the coefficients of ai and b j are identified by the minimisation of the mean square error.
  • the reference ri is filtered by the coefficients ai and bj.
  • the reference used is again a recurrent synchronous average of the preceding recordings.
  • ⁇ is a proportionality constant
  • ⁇ indicates the sigmoid slope
  • netl indicates the net input for that given neuron and will be
  • nlntpnt netI h ⁇ w // ⁇ + ⁇ 9 / ( 16 )
  • nHidden netl j ⁇ w hj x h +S h (17 )
  • Wavelet-type neural networks Wavelet-type neural networks.
  • Each wavelet function is characterised by 2 characteristic parameters: b j , i.e. a translation; a- j , i.e. a scaling. Particularly, a Morelet-type wavelet is used, thereby the estimation of y(t) takes place through the following formula:
  • y(t) ( 19 )
  • Each node of the network hidden layer is a time- frequency characteristic of the evoked potential. To avoid that during the learning the nodes may compete for the higher-energy components, they shoould be trained separately and recursively.
  • the j th neuron is trained based on the residue ⁇ j (t) , i.e. the difference between the recording and the sum of outputs of the already trained neurons. This procedure is repeated until a preset constraint is respected, typically concerning the signal energy represented by the network.
  • a scaling, a translation and a reference frequency i.e. a re f,j, b re f,j e ⁇ re f,j, are obtained per each neuron.
  • a bandpass filter and a time window are obtained to be used for the corresponding neuron in the network which should extract the single- sweep potential, such that it can learn only specific time-frequency characteristics of the recording, the same characteristics being analysed by the equivalent thereof in the network trained with synchronous mean.
  • a signal ej i.e. ej filtered and limited over time
  • ej i.e. ej filtered and limited over time
  • the mean square error between ⁇ j and the output of the j th neuron is minimised.
  • a final learning simultaneous for all neurons is performed.
  • the output of the inventive neural network can be used as the reference for a FIR or
  • NARX non-linear ARX
  • a non-linear ARX filter may be applied, having the formula:
  • g tn indicates the t derivated of the activation function of the n th neuron such as described by Wang D., Lum K. -Y., Yang G. in the document "Parameter estimation of ARX/NARX model: a neural network based method" in Proceedings of the 9th International Conference on Neural Information Processing, 2002.
  • the selected activation functions are of the polynomial, exponential or sigmoid types.
  • the output of the neural network is used to supply the non-linear filter, as also in this case the performance is improved if the reference signal is more correlated with the signal to be extracted.
  • cord distress may not be as likely.
  • the cord functionality can be inspected in its anatomical wholeness by a non-invasive method which increases the monitoring reliability.
  • equipment 100 allows to extract the SEP evoked potential from the electroencephalographic potential EEG being detected following a single electric stimulus without having to average the EEG potentials detected in correspondence of a number of stimuli.
  • This equipment 100 integrates both the stimulation and the recording and processing functions within only one compact structure.
  • the inventive equipment 100 can be used also by operators who are not specialised in the neurophisiology field due to the easy positioning of the electrodes in standardized positions and to the visualization of the bio-signal waveforms.
  • it can be envisaged to apply the electric stimulus to two different stimulation sites, i.e. one per each leg in a non-simultaneous way.
  • two distinct groups of stimulation electrodes per each leg and two distinct groups of recording electrodes for the H-reflex potential should be used.

Abstract

Le procédé pour piloter la fonctionnalité de la moelle épinière d'une créature vivante comprend les étapes de : a) acquisition d'un signal électrique (S1) obtenu en réponse à une simulation électrique appliquée au corps de la créature; le signal comprend un premier signal (S) indiquant un potentiel évoqué somatosensorielle généré par ladite impulsion ; b) l'acquisition d'un second signal électrique (S2) indiquant un potentiel de réflexe H généré par ladite stimulation électrique n ; c) le traitement des premier et second signaux pour fournir simultanément des première et seconde informations qui sont en corrélation avec les premiers et seconds signaux électriques permettant ainsi de vérifier la fonctionnalité de la moelle épinière.
PCT/IT2004/000440 2004-08-05 2004-08-05 Procede et equipement pour piloter la fonctionalite de la moelle epiniere WO2006013585A1 (fr)

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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8594785B2 (en) 2007-02-01 2013-11-26 Boston Scientific Neuromodulation Corporation Neurostimulation system and method for measuring patient activity
WO2008130405A1 (fr) * 2007-04-19 2008-10-30 Medtronic, Inc. Système de délivrance de thérapie implantable muni de multiples capteurs de température
US7604629B2 (en) 2007-04-19 2009-10-20 Medtronic Inc. Multi-parameter infection monitoring
US7611483B2 (en) 2007-04-19 2009-11-03 Medtronic, Inc. Indicator metrics for infection monitoring
US7682355B2 (en) 2007-04-19 2010-03-23 Medtronic, Inc. Refined infection monitoring
US7734353B2 (en) 2007-04-19 2010-06-08 Medtronic Inc. Controlling temperature during recharge for treatment of infection or other conditions
US7766862B2 (en) 2007-04-19 2010-08-03 Medtronic, Inc. Baseline acquisition for infection monitoring
US8498697B2 (en) 2009-10-30 2013-07-30 The University Of Hong Kong Classification of somatosensory evoked potential waveforms
JP2021510094A (ja) * 2017-12-22 2021-04-15 バイオセレニティＢｉｏｓｅｒｅｎｉｔｙ 脳活動の指標を計算するためのシステム及び方法
CN116212236A (zh) * 2023-03-15 2023-06-06 马鞍山市人民医院 一种稳态体感诱发电位刺激系统及信号采集方法

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