Key result
A machine learning-based approach for three-class classification of non-constraint ECG quality demonstrated accuracies of 94.01%, 95.57%, and 96.94% for each channel of three sensors.
A machine learning algorithm can accurately classify the quality of non-constraint ECG signals and estimate user posture in wheelchair users.
May support automated ECG quality checks in wearables; leaves open prospective clinical validation before adoption.
Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.
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Kim et al. (2016) studied ECG quality classification for wheelchair users. Machine learning based approach for ECG quality classification was evaluated on Accuracy of quality level classification. A machine learning-based approach for three-class classification of non-constraint ECG quality demonstrated accuracies of 94.01%, 95.57%, and 96.94% for each channel of three sensors.
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