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Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data

Journal of Computer Science Research 2023年第3期5卷 15-21页

作者： Erdem Tuncer Faculty of Technology Biomedical Eng.Department of Kocaeli UniversityKocaeli41001Turkey

In this study,a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea(OSA)from the analysis of single-channel ECG recordings.Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study.In the feature extraction stage,dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm.In the classification phase,OSA patients and normal ECG recordings were classified using Random Forest(RF)and Long Short-Term Memory(LSTM)classifier algorithms.The performance of the classifiers was evaluated as 90% training and 10%testing.According to this evaluation,the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%.Considering the results obtained,it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods.The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead.

关键词： ECG Sleep apnea Classification Dynamic time warping median frequency

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The effect of 630-nm light stimulation on the sEMG signal of forearm muscle

The effect of 630-nm light stimulation on the sEMG signal of...

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2010亚洲光电子会议保健和生物医学光学分会场

作者： Dan D.Yang 1,W.sheng Hou 1,Xiao Y.Wu 1,Xiao L.Zheng 1,Jun Zheng 2,Ying T.Jiang 3 1 Key Laboratory of Biorheological Science and Technology Chongqing University,Ministry of Education Chongqing 400044,China 2 Department of Computer Science,New Mexico Institute of Mining and Technology,Socorro,NM 87801,USA 3 Department of Electrical and Computer Engineering,University of Nevada,Las Vegas,NV 89154,USA.

This study aimed to explore if the red light irradiation can affect the electrophysiology performance of flexor digitorum superficialis （FDS） and fatigue recovery.Four healthy volunteers were randomly divided into two groups.In the designed force-tracking tasks,all subjects performed the four fingertip isometric force production except thumb with a load of 30% of the maximum voluntary contraction （MVC） force until exhaustion.Subsequently,for the red light group,red light irradiation （640 nm wavelength,0.23J/cm 2,20 min） was used on the right forearm;for the control group,the subjects relaxed without red light irradiation.Then subjects were required to perform fatigue trail again,and sEMG signal was collected simultaneously from FDS during finger force production.Average rectified value （ARV） and median frequency （MF） of sEMG were calculated.Compared to the control group,the red light irradiation induced more smoother value of ARV between 30% and 40%,and the value of MF was obviously large and smooth.The above electrophysiological markers indicated that recovery from muscle fatigue may be positively affected by the red light irradiation,suggesting that sEMG would become a power tool for exploring the effect of red light irradiation on local muscle fatigue.

关键词： red light muscle fatigue sEMG median frequency

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