Development of a Knowledge Discovery Computing based wearable ECG monitoring system

ECG signals contain a lot of information related to cardiac activity and play a critical role in diagnosing of heart disease. However, relying on health information monitoring with simple ECG measurement can lead to errors in health information analysis. Therefore, for accurate diagnosis and analysis, an algorithm that can efficiently process user’s measurement environment and activity information is required. In this paper, we propose a wearable ECG monitoring system based on Knowledge Discovery Computing using 3-axis acceleration sensor. The proposed system measures real-time cardiac information and activity information simultaneously to minimize errors in health information analysis through Knowledge Discovery Computing between the user’s environment information and abnormal ECG according to the measurement environment in everyday life. In addition, we implemented a packet transmission protocol to effectively transmit large amounts of data analyzed through Knowledge Discovery Computing to the base station. First, arrhythmia detection was performed using R-peak detection preprocessing and pattern matching algorithm. Also, a classification algorithm was implemented to classify activity types by utilizing an accelerometer in order to recognize the context surrounding the user. Information on the user’s vital signs and activity information can be used for more accurately determine arrhythmia in daily life. Also, variable packet generation protocol was designed for an effective transmission of data packets increased exponentially by long-term measurements and wireless data transfer. The variable packet generation protocol is efficient in limited wireless network environments, because it generate packets of the entire data only with case of abnormal cardiac rhythm and transmits minimal information for normal cardiac rhythm. In order to evaluate the performance of ECG monitoring system based on Knowledge Discovery Computing, we designed a 2-lead ECG measurement belt manufactured with conductive fiber to minimize user discomfort, and assessed the system performance in data packet transmission, data recovery, and arrhythmia detection in dynamic states in daily life. In static states, the posture detection was 100%, heart rate detection 99.8%, and CR (Compression Ratio) was 193.99 and correlation coefficient of 0.95 with commercial systems. In dynamic states, 96% detection rate and 59.14 of CR is identified. If arrhythmia is determined based only on ECG signals, it is difficult to differentiate an actual abnormal cardiac rhythm from an ECG signal altered due to motion. The experiments conducted in this study confirmed that Knowledge Discovery is possible in the dynamic state through the proposed system in daily life.