Data abnormal detection and classification in wireless body area networks using Convolutional Neural Networks

Abstract

Wirekess body area network(WBAN) has become a promising type of networks to efficiently collecting as well as analyzing human physiological information. These collected data are then transformed to outside database support real-time monitoring or other health applications. However, due to the resource constrained of the networks, the monitored data needs to be analyzed for automatic detection of physiological changes indeed trigger an alarm of patient health degradation. Moreover, false alarm result by abnormal changes or faulty measurements should be classified to reduce unnecessary medical intervention.. In this paper, we propose a novel framework for data abnormal detection and classification on real-time ECG signals to distinguish faulty measurements form clinical emergency. Our work is based on adaptive 1-D convolutional neural networks(CNNs). We use small common ECG dataset to build a dedicated CNN models offline, which can be used to classify possibly long ECG data stream in a fast and accurate manner, such a solution can conveniently be used for real-time monitoring and early alert system in WBANs.The state-of-the-art performance on efficiency and accuracy for ECG classification over real dataset is achieved by the proposed method.