WO2002040094A3 - Curvature-based feature selection for electrophysiologic signals - Google Patents

Curvature-based feature selection for electrophysiologic signals Download PDF

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Publication number
WO2002040094A3
WO2002040094A3 PCT/US2001/045763 US0145763W WO0240094A3 WO 2002040094 A3 WO2002040094 A3 WO 2002040094A3 US 0145763 W US0145763 W US 0145763W WO 0240094 A3 WO0240094 A3 WO 0240094A3
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WO
WIPO (PCT)
Prior art keywords
curvature
feature selection
based feature
cardiac signal
electrophysiologic signals
Prior art date
Application number
PCT/US2001/045763
Other languages
French (fr)
Other versions
WO2002040094A2 (en
Inventor
Robert J Sweeney
Eric G Lovett
Original Assignee
Cardiac Pacemakers Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Cardiac Pacemakers Inc filed Critical Cardiac Pacemakers Inc
Priority to EP01984963A priority Critical patent/EP1330289A2/en
Priority to AU2002233960A priority patent/AU2002233960A1/en
Publication of WO2002040094A2 publication Critical patent/WO2002040094A2/en
Publication of WO2002040094A3 publication Critical patent/WO2002040094A3/en

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • 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
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/35—Detecting specific parameters of the electrocardiograph cycle by template matching
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/362—Heart stimulators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method for curvature based complex identification and classification comprises sensing a cardiac signal and computing curvatures at sample points on the sensed cardiac signal, extracting features from the computed curvatures, comparing the extracted features with a set of predetermined templates, and classifying the sensed cardiac signal based on the outcome of the comparison.
PCT/US2001/045763 2000-10-31 2001-10-26 Curvature-based feature selection for electrophysiologic signals WO2002040094A2 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP01984963A EP1330289A2 (en) 2000-10-31 2001-10-26 Curvature-based feature selection for electrophysiologic signals
AU2002233960A AU2002233960A1 (en) 2000-10-31 2001-10-26 Curvature-based feature selection for electrophysiologic signals

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US09/703,269 US6684100B1 (en) 2000-10-31 2000-10-31 Curvature based method for selecting features from an electrophysiologic signals for purpose of complex identification and classification
US09/703,269 2000-10-31

Publications (2)

Publication Number Publication Date
WO2002040094A2 WO2002040094A2 (en) 2002-05-23
WO2002040094A3 true WO2002040094A3 (en) 2003-01-16

Family

ID=24824709

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2001/045763 WO2002040094A2 (en) 2000-10-31 2001-10-26 Curvature-based feature selection for electrophysiologic signals

Country Status (4)

Country Link
US (2) US6684100B1 (en)
EP (2) EP2269691A3 (en)
AU (1) AU2002233960A1 (en)
WO (1) WO2002040094A2 (en)

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