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Recently, as interest in electrocardiogram monitoring has increased, research on real-time ECG signal analysis in daily life using lightweight embedded devices has increased. Abnormal beat detections in ECG signal analysis are an important research area to reduce processing time and cost for cardiac arrhythmia diagnosis. Abnormal beat detections can be divided into feature-based detection and shape-based detection. Feature-based detection finds it difficult to detect reliable fiducial points, and shape-based detection has difficulty detecting abnormal beats that are similar to normal beats. In this paper, we propose template cluster generation and abnormal beat detection using both detection methods. The proposed method shows robust detection of distorted normal beats by generating a template cluster rather than a single template. Moreover, abnormal beats that have normal shape can be detected using the RR interval, which is a highly reliable feature. Experiment results using the MIT-BIH arrhythmia database, provided by Physionet, showed the average processing times to generate a template cluster and detect abnormal beats for the 30-minute signal length were 1.21 seconds and 0.14 seconds, respectively. With manually adjusted thresholds, the specificity and accuracy achieved 93.00% and 97.94%, respectively. In the case of group 1 records obtained relatively stably, the specificity and accuracy achieved 99.27% and 99.44%.

Efficient Template Cluster Generation for Real-Time Abnormal Beat Detection in Lightweight Embedded ECG Acquisition Devices