[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2021.07.001

CAB: Classifying Arrhythmias based on Imbalanced Sensor Data

Wang, Yilin (Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology)
Sun, Le (Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology)
Subramani, Sudha (Victoria University)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.7, 2021 , pp. 2304-2320 More about this Journal

Abstract

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

Keywords

Bi-LSTM; Data Augmentation; ECG; GAN; Heartbeat Classification; E-Health;

Citations & Related Records

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