ECG Signal Classification Algorithm Based on Fusion Features

Abstract

Cardiovascular disease is a common and frequently-occurring disease in clinic. Cardiovascular disease detection has become the primary concern of people’s health and the mortality is very high. In order to improve the accuracy and universality of the current ECG signal classification algorithm under multi-classification conditions, an ECG signal classification algorithm based on fusion features is proposed. The algorithm uses multi-layer Convolution Neural Network and Recurrent Neural Networks. The spatial and temporal features of ECG signals are extracted automatically. Finally, the accurate classification of ECG signals is realized by using multi-fusion features. In order to verify the accuracy of the algorithm, using MIT-BIH arrhythmia database as the standard test data, the classification accuracy of the algorithm is 99.42%. Compared with the ECG classification algorithm in document[1] [2], the algorithm proposed in this paper abandons the complexity of the traditional ECG algorithm in the process of data feature extraction and solving the problem of insufficient feature extraction of single convolution neural network for one-dimensional ECG signal leads to poor classification effect of small probability samples. The classification sensitivity of ECG classification is improved from 83.00% of single convolution network to 97.07% of class S (supraventricular abnormal beats) classification sensitivity, The classification sensitivity of Class F (Fused Rhythm) is increased from 74.48% to 97.24%, which significantly improves the classification sensitivity of small probability abnormal rhythms, and increases the universality of ECG classification algorithm in multi-class beat recognition.