LAUSR

This paper presents a smart detection technology for personal Electrocardiography (ECG) monitoring based on data integral-transform of chaotic system. First of all, a set of data-feeding system is developed, ECG data is technically converted into multiple-dimensional phase space, i.e., the dynamics of ECG data in time domain has been mapped into chaotic domain. Further, some effective and potential features in different sub-dimensional phase plane of the data, such as Euclidean Feature Values (EFV), Central Point Distribution (CPD), are captured, which indicates key biomarkers for different ECG states. In the final stage, following the key biomarkers, explicit boundary thresholds are defined for classification of different ECG states. Three ECG states given via open database-PhysioNet are validated, including normal sinus rhythm (NSR), congestive heart failure (CHF) and sleep apnea (SA). The experimental results show that the developed smart detection technology is effective and feasible for detecting and monitoring the states of such personal ECG states.