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A self-organizing QRS-wave recognition system using an ART2 neural network achieved an average recognition error of less than 1 ms for Q and S points.
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An ART2 neural network-based system can accurately recognize QRS waves in ECGs with an average error of less than 1 ms.
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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Yujiro Suzuki (1995) studied Electrocardiogram (ECG) analysis. Self-organizing QRS-wave recognition system (ART2 neural network) was evaluated on Average recognition error of Q and S points. A self-organizing QRS-wave recognition system using an ART2 neural network achieved an average recognition error of less than 1 ms for Q and S points.
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