A Convolutional Neural Network model achieved up to 95% training accuracy in classifying ECG signals, with 100% accuracy for sudden death and 92.5% for normal sinus waveforms.
A CNN-based deep learning model embedded in an NVIDIA Jetson Nanoprocessor can accurately classify and predict four types of ECG signals in real-time.
This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms.
Caesarendra et al. (Thu,) conducted a other in ECG signal classification. Convolutional Neural Network (CNN) model was evaluated on Classification accuracy. A Convolutional Neural Network model achieved up to 95% training accuracy in classifying ECG signals, with 100% accuracy for sudden death and 92.5% for normal sinus waveforms.