A framework for cardiac arrhythmia detection from IoT-based ECGs

• Published in: World wide web (Bussum) Vol. 23; no. 5; pp. 2835 - 2850
• Main Authors: He, Jinyuan, Rong, Jia, Sun, Le, Wang, Hua, Zhang, Yanchun, Ma, Jiangang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer Nature B.V
• Subjects: Arrhythmia; Artificial neural networks; Cardiac arrhythmia; Classification; Computer Science; Convolution; Database Management; Engineering education; Information Systems Applications (incl.Internet); Internet of Things; Learning; Modules; Operating Systems; Physicians; Internet-of-Things; Deep learning; ECG; Cardiac arrhythmia detection; Dynamic ensemble selection; ResNet
• DOI: 10.1007/s11280-019-00776-9
• ISSN: 1386-145X

Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12 % of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework.