ECG anomaly detection using wireless BAN and HEMFCM clustering

In recent days, elderly people living alone at home are steadily increasing throughout the world. This situation drives to develop a health care system for monitoring the health parameters of elderly people and help them to lead ahealthy independent life. This paper presents a system that uses wireless sensors for monitoring the health parameters without disturbing the normal activities of elderly people. The proposed system provides a wearable health care solution using the wireless Shimmer sensor device for collecting ECG data in home PC. ECG data anomaly is detected using rule based classifier. Classification rules are generated based on cluster centroids obtained using a novel scheme named Hybrid Expectation Maximization and Fuzzy C Means (HEMFCM) Clustering. The proposed method is validated using real data collected from different subjects and abnormal data readings from the MIT BIH database. Experimental results show that proposed method achieves 85% classification accuracy which is better than EM and FCM clustering methods.