An ECG classifier designed using modified decision based neural networks.
In this paper, a neural network based generalized software system is presented for automatic analysis of electrocardiograms (ECGs). The proposed system is capable of intuitively diagnosing the disease from the ECG using the knowledge acquired from the training. A modified decision based neural network which converges in a finite amount of time is employed. The training procedure used automatically varies the size of the network. The system is capable of being trained even without an expert's supervision. The physician can correct the network as and when a misclassification occurs, thus making the system less error-prone as time passes. The proposed system has been tested using an ECG data base representing different cardiological conditions such as bundle branch blocks and infarctions. The system is capable of detecting different types of arrhythmias also.
Simon
B P
BP
Department of Electrical Engineering, Indian Institute of Technology, Madras, India.
Eswaran
C
C
D000465:Algorithms; D002037:Bundle-Branch Block / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography / Q000145:classification* / Q000379:methods; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted