An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model
S. Pushpalatha1 , Shrishail Math2
1S. Pushpalatha, Research Scholar, Department of ISE, Dr. AIT.
2Dr. Shrishail Math, Professor, Department Of CSE, SKIT.
Manuscript received on November 13, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 285-292 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6176129219/2019©BEIESP | DOI: 10.35940/ijitee.B6176.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Obtaining an exact measurement of oxygen saturation (SpO2) using a finger-probe based pulse oximeter is dependent on both artifact-free infrared (IR) and red (R) Photoplethysmographic signals. However, in actual real-time environment condition, these Photoplethysmographic signals are corrupted due to presence of motion artifact (MA) signal that is produced due to the movement/motion from either hand or finger. To address this motion artifacts interference, the cause of the contamination of Photoplethysmographic signals by the motion artifacts signal is observed using GAIT. Motion and noise artifacts enforce constraints on the usability of the Photoplethysmographic, predominantly in the setting of sleep disorder detection and ambulatory monitoring. Motion and noise artifacts can alter Photoplethysmographic, resulting wrong approximation of physiological factors such as arterial oxygen saturation and heart rate. For overcoming issues and problems, this manuscript presented a new approach for detection of artifacts. First, present an adaptive filter and adaptive threshold model to detect artifact and obtain derivative of correlation coefficient (CC) for labelling artifacts, respectively. Lastly, Improved Support Vector Machine Model is presented to perform classification. Experiment are conducted on real-time dataset. Our approach attain significant performance in term of accuracy, sensitivity, specificity and positive prediction.
Keywords: Adaptive filter, Ambulatory Monitoring, Gait Analysis, Machine learning, Motion and Noise Artifacts, Obstructive Sleep Apnea..
Scope of the Article: Machine learning