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Abstract-Segmenting electrocardiogram (ECG) into its important components is crucial to the field of cardiology and pharmaceutical studies, because analyses of ECG segments can be used to predict heart symptoms and the effects of cardiac medications. For each study, thousands of ECG signal points need to be analyzed and segmented. Despite of the success of using deep learning (DL) methods in multiple studies on classifying the heart condition, there are still lacking DL-based methods to characterize ECG temporal features. This paper describes a novel ECG segmentation method based on the recurrent neural network (RNN) with long short-term memory (LSTM) layers. In this model, each ECG sample is classified into one of the four categories: P-wave, QRS-wave, T-wave, and neutral (others). Our work shows that DL sequence learning methods outperform a traditional Markov model in terms of accuracy and using simple local features instead of complicated features, such as wavelet encoding. Particularly on T-wave segmentation, our approach can achieve an accuracy of 90%, compared to that of 74.2% using Markov models.
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I. INTRODUCTION
Analyzing critical segments of the ECG waveform in ECG signals is crucial in determining the conditions of heart for diagnosing diseases or analyzing the effects of cardiac medications. All these analytical tasks require finding a large quantity of waveform patterns that can be identified as abnormalities in ECGs. Many of the pattern features are subtle and would require an expert clinician to recognize and spot them in ECG wave intervals. Utilizing clinicians is not economically feasible on a large scale. Thus, an automated approach to segment ECG signals is very promising. In addition, an accurate automated ECG segmentation method would provide more cost-effective and reliable results for patient diagnostics, especially for mass screening applications and in drug development.
A cardiac complex is composed of several wave components, and three of them are of high significance. These three waves are the P-wave, the QRS-wave, and the T-wave, as shown in Figure 1. The main goal of ECG segmentation is to detect and localize the QRS-wave, meanwhile, other segments such as T-wave and P-wave are also of high importance. For instance, one of the symptoms of sudden cardiac arrest and eventual death relies on finding the T-wave inversion, where the...