Abstract
This study observes one of the ECG signal abnormalities, which is the Premature Ventricular Contraction (PVC). Many studies applied a machine learning technique to develop a computer-aided diagnosis to classify normal and PVC conditions of ECG signals. The common process to obtain information from the ECG signal is by performing a feature extraction process. Since the ECG signal is a complex signal, there is a need to reduce the signal dimension to produce an optimal feature set. However, these processes can remove the information contained in the signal. Therefore, this study process the original ECG signal using a Convolutional Neural Network to avoid losing information. The input data were in the form of both one beat of normal ECG signal or PVC with size 1x200. The classification used four layers of convolutional neural network (CNN). There were eight 1x1 filters used in the input. Simultaneously, 16 and 32 of 1x1 filters were used in the second and the fourth convolutional layers, respectively. Thus the system produced a fully connected layer consisted of 512 neurons, while the output layer consisted of 2 neurons. The system is tested using 11361 beats of ECG data and achieved the highest accuracy of 99.59%, with the 10-fold cross-validation. This study emphasizes an opportunity to develop a wearable device to detect PVC since CNN can be implemented into an embedded system or an IoT based system.
Highlights
ECG signal is a biology signal resulted from the electrical activities of heart
We focus on classification of Premature Ventricular Contraction (PVC) based on ECG signal
This study proposes a PVC classification on the ECG signal without using a feature extraction process or dimension reduction to keep the information in the ECG signal intact
Summary
ECG signal is a biology signal resulted from the electrical activities of heart. The heart health in someone can be seen from ECG Signal that can be valued from the rhythm, form and orientation [1]. Wavelet entropy was calculated on five levels of wavelet packet decomposition and produced the highest accuracy of 94.9% using SVM as classifier Another feature extraction method for PVC classification was presented by Rizal and Wijayanto [10]. Along with the development of machine learning, various deep learning methods were used to classify ECG signals, especially in arrhythmia [11]. Hoang et al used a tensor-based feature extraction method and a convolutional neural network for PVC classification [12]. The whole PVC classification research described above used a feature extraction process or dimension reduction to process the ECG signal before entering the classifier. This research used the deep eearning method of convolutional neural network (CNN) as classifier for PVC on ECG signal classification [14]. The preprocessing stage hopefully increase the accuracy of PVC classification
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