Implementation of Fuzzy-rule based Activity Classification and Optimized Adaptive Filter-set for Wearable ECG Recording
نویسندگان
چکیده
An enhanced and optimized adaptive filter with optimal filter coefficients selection is proposed and implemented to resolve motion artifact issue in wearable ECG Recording. A two-electrode small size chest belt ECG system mounted with 3-axis accelerometer is implemented for ECG and activity ubiquitous recording in daily life. In ubiquitous ECG recording, ECG signal is often distorted due to different state of activity. Body movement incurs activity noise in ubiquitous ECG recording, and causing low accuracy in R-peak (heart beat) detection. Thus, a new adaptive filter methodology is proposed in this paper to look for an optimized filter coefficients base on different state of activity. A simple fuzzy rulebased algorithm is suggested for activity state classification and a set of high pass filter coefficient is applied base on different state of activity. In the case of low activity state, low high pass filter coefficient is used, whereas, in the case of high activity state, a high pass filter coefficient is used. The experiment result shows significant improvement of R-peak detection accuracy during fast movement activity state.
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