A Real-Time Abnormal Beat Detection Method Using a Template Cluster for the ECG Diagnosis of IoT Devices

Currently, the use of the Internet of Things (IoT) devices is expanding, and research on bio-signal monitoring systems is increasing. This paper proposes an abnormal beat detection algorithm for electrocardiogram signals that is suitable for embedded devices. A typical single template-based detection method requires a great deal of memory to generate a template, and abnormal beats make it difficult to generate a normal beat template. As such, this paper proposes a reliable method of generating a normal beat template using a template cluster with Pearson similarity. The proposed method uses the weighted mean to minimize memory usage in the template cluster generation step. The results of the experiment indicate that the proposed algorithm can measure P-wave deformation shapes, which are difficult to detect, using the partial template in the P-wave region.Furthermore, the average detection time is 0.39 seconds for a 30-minute signal, confirming the algorithm's potential for real-time operation in lightweight embedded devices.