Abstract
An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension, which limits the CNNs to receive fixed-scale inputs. Signals of different scales are generally processed into the same size by segmentation and downsampling. If information loss occurs during a uniformly-sized process, the classification accuracy will ultimately be affected. To solve this problem, this paper constructs a new CNN framework spatial pyramid pooling (SPP) method, which solves the deficiency caused by the size of input data. The Massachusetts Institute of Technology-Biotechnology (MIT-BIH) arrhythmia database is employed as the training and testing data for the classification of heartbeat signals into six categories. Compared with the traditional method, which may lose a large amount of important information and easy to be over-fitted, the robustness of the proposed method can be guaranteed by extracting data features from different sizes. Experimental results show that the proposed architecture network can extract more high-quality features and exhibits higher classification accuracy (94%) than the traditional deep CNNs (90.4%).
Highlights
IntroductionAn electrocardiogram (ECG) is a pattern in which various forms of potential changes are extracted from the body surface via an electrocardiograph
The ECG has an important reference value for basic cardiac functions and related pathological research, and an experienced cardiologist can tell the arrhythmia according to the morphological pattern of the ECG signals
It is due to the time-varying dynamics and various profiles of the ECG signals that make the precision of the classification vary from patient to patient [1]
Summary
An electrocardiogram (ECG) is a pattern in which various forms of potential changes are extracted from the body surface via an electrocardiograph. The ECG has an important reference value for basic cardiac functions and related pathological research, and an experienced cardiologist can tell the arrhythmia according to the morphological pattern of the ECG signals. The computer-aided approaches to the morphological pattern recognition of the ECG signal are difficult to realize. It is due to the time-varying dynamics and various profiles of the ECG signals that make the precision of the classification vary from patient to patient [1]. Computer-aided approaches can improve the efficiency of diagnosis, and freeing physicians from cumbersome pattern recognition tasks. The development of pattern recognition of an ECG signal and
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