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Recognition of ECG Using a High Order Neural Network

Liu Weifang. Ouyang Kai (Dept of BME, Capital University of Medical Sciences,Beijing 1 08054) Zhou Shen (Hohhot Railwaay Hospital, Hohhot 010050)  
The classification and recognition of electrocardiogram (ECG) are difficult to solve for a long time, especially when the starting point of selected ECG signal is shifted or the signal is rotated. It. such cases, it requires the recognition process to be translation-invariant and rotation-invariant. The authors introduce a high order (second order) neural network which is used to recognize five types of ECG signals (nomal group. higher QRS wave group, higher T wave group, inversed T wave group and arrhythmia group) with translation and rotation. The results showed that the network is very effective to distinguish the above mentioned 5 types of ECG. The signal could have been translated without any limit ation. The maximum of signal rotation is allowed 20% with respect to the original one. All the task was carried out with microcomputer. Our network is with properties of high sensitivity and short time for learning. It is of great value for clinical use.
【Fund】: 国家863计划、攀登计划 国家自然科学基金
【CateGory Index】: R314
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