Real-time ECG Monitoring System Based On Mobile Platform

With the deepening of the aging in globe and the high incidence of heart disease and cardiovascular disease in young people, the growing demand for health care is enhanced day by day. With the improvement of information technology and mobile internet, family health care have emerged. In this case, intelligent, low-cost and mobile internet based personal and family ECG real-time monitoring system has a broad market demand.ECG (Electrocardiogram) is the main foundation in angiocardiopathy diagnosis. This paper designs and implements a mobile ECG monitoring system with real-time QRS detection and activity recognition. Our work contains the following aspects.Firstly, an algorithm of composite wave filter and improved curve feature point selecting QRS detection algorithm is proposed to meet the need of real-time detection in mobile ECG monitoring system. The process of QRS detection is as follows:first, we use composite wave filter to reduce the influence of noise. After filtering, an improved curve feature point selecting algorithm is proposed to extract feature points from filtered signal. Finally, according to the features of QRS complex, adaptive threshold is adjusted periodically to adapt real-time detection.Secondly, we report on our efforts to recognize user activity from mobile accelerometer data. The process of activity recognition is as follows:first, we get the raw accelerometer data and carry out the smooth processing on it. And then, the features of accelerometer data such as mean, standard deviation, peak value and energy are extracted. Finally, after classification of accelerometer data by using WEKA, we use K-Nearest neighbor method to recognize activities.After all, we combine the QRS detection and activity recognition to implement the ECG real-time monitoring system based on mobile platform. The system not only can detect, review QRS wave, but also can recognize activities.