Description
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PPG signal is a useful tool for quick and critical diagnosis related to cardiovascular output via wearable or portable devices. Its drawback is unreliable during non-stationary states due to occurrences of frequency overlap of the desired and motion artifact signals. The accelerometer is usually used to reflect the motion artifact when the adaptive noise cancellation technique is implemented to address this obstacle, but it failed to predict the value of real induced noise accurately. In this work, we investigate a new concept that is capable of providing the entire motion artifact separately by recruiting twin photodetectors to formulate the influential signals. The main function of photo-detector (MPD) is to generate the corrupted PPG signal. While the second photo-detector (CPD) that covered up from the light effect, will be used to reflect the corruption effect that exists in both sources simultaneously by counting the generated dark photocurrent (GDPC). To validate the GDPC approach, experiments were executed to analyze the response of two methods during steady and motion state. Results showed resemblance responses for both methods regarding the’ amplitude fluctuations and high positive correlations in the time domain. Furthermore, the FFT peak plots in frequency domain indicated the potential of CPD to reflect all fundamental frequencies caused by motion, unlike the acceleration approach. Therefore, the proposed concept is a sure-fire method to obtain precise measurements at a lower cost. (2017-05-10)
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Notes
| A Novel Approach for Precise Motion Artefact Detection in Photoplethysmograph Signal Processing based on Dark Photocurrent Muhideen Abbas Hasan*,Department of Electronics, Technical Institute/Dour, Northern Technical University, Foundation of Technical Education, Iraq Fahmi Samsuri,Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, 26600, Pahang, Malaysia Kok Beng Gan,Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia |