ISSN:
1741-0444
Keywords:
Adaptive resonance theory
;
Electrocardiogram
;
Neural networks
;
Personal computer
;
ST-segment
Source:
Springer Online Journal Archives 1860-2000
Topics:
Biology
,
Chemistry and Pharmacology
,
Medicine
Notes:
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 (Ton). The recogniser determines the respective ranges in which the J and Ton points lie, based on the locations addressed. Within those ranges, the recogniser finds the exact locations of the J and Ton 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 Ton 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02446186
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