Personal computer system for ECG ST-segment recognition based on neural networks

Abstract

A personal computer system for electrocardiogram (ECG) ST-segment recognition is developed based on neural networks. The system consists of a preprocessor, neural networks and a recogniser. The adaptive resonance theory (ART) is employed to implement the neural networks in the system, which self-organise in response to the input ECG. Competitive and co-operative interaction among neurons in the neural networks makes the system robust to noise. The preprocessor detects the R points and divides the ECG into cardiac cycles. Each cardiac cycle is fed into the neural networks. The neural networks then address the approximate locations of the J point and the onset of the T-wave (T_on). The recogniser determines the respective ranges in which the J and T_on points lie, based on the locations addressed. Within those ranges, the recogniser finds the exact locations of the J and T_on points either by a change in the sign of the slope of the ECG, a zero slope or a significant change in the slope. The ST-segment is thus recognised as the portion of the ECG between the J and T_on points. Finally, the appropriateness of the length of the ST-segment is evaluated by an evaluation rule. As the process goes on, the neural networks self-organise and learn the characteristics of the ECG patterns which vary with each patient. The experiment indicates that the system recognises ST-segments with an average of 95·7 per cent accuracy within a 15 ms error and with an average of 90·8 per cent accuracy within a 10 ms error, and that characteristics of the ECG patterns are stored in the long term memory of the neural networks.