IEEE Transactions on Neural Networks · 1995 · 90 citations · 16 references
EngineeringBioengineeringBiomedical EngineeringBiomedical Signal ProcessingElectrophysiologyMachine LearningCardiac Signal ProcessingElectrocardiogram AnalysisElectrocardiogram InterpretationElectrocardiographyNeural Signal ProcessingPattern RecognitionArrhythmia DetectionEcg Feature ExtractionEcg SegmentationElectrocardiogram Feature ExtractionElectrocardiogram SegmentationPattern Recognition ErrorQrs Complex DetectionResonance Theory
The author has developed a self-organizing QRS-wave recognition system for electrocardiograms (ECGs) using neural networks. An ART2 (adaptive resonance theory) network was employed in this self-organizing neural-network system. The system consists of a preprocessor, an ART2, network, and a recognizer. The preprocessor detects R points in the ECG and divides the ECG into cardiac cycles. A QRS-wave is the part of the ECG that is between a Q point and an S point. The input to the ART2 network is one cardiac cycle from which the ART2 network indicates the approximate locations of both the Q and S points. The recognizer establishes search regions for the Q and S points. Then, it locates the Q and S points in each search region. The system uses this method to recognize a QRS-wave. Then, the ART2 network learns the new QRS-wave pattern from the incoming ECG. The ART2 network self-organizes in response to the input ECG. The average recognition error of the present system is less than 1 ms in the recognition of the Q and S points.
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