Authors: Jo Woon Chong Duy K Dao S M A Salehizadeh David D McManus Chad E Darling Ki H Chon Yitzhak Mendelson
Publish Date: 2014/08/05
Volume: 42, Issue: 11, Pages: 2238-2250
Abstract
Motion and noise artifacts MNA are a serious obstacle in utilizing photoplethysmogram PPG signals for realtime monitoring of vital signs We present a MNA detection method which can provide a clean vs corrupted decision on each successive PPG segment For motion artifact detection we compute four timedomain parameters 1 standard deviation of peaktopeak intervals 2 standard deviation of peaktopeak amplitudes 3 standard deviation of systolic and diastolic interval ratios and 4 mean standard deviation of pulse shape We have adopted a support vector machine SVM which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them We apply several distinct features of the PPG data to enhance classification performance The algorithm we developed was verified on PPG data segments recorded by simulation laboratorycontrolled and walking/stairclimbing experiments respectively and we compared several wellestablished MNA detection methods to our proposed algorithm All compared detection algorithms were evaluated in terms of motion artifact detection accuracy heart rate HR error and oxygen saturation SpO2 error For laboratory controlled finger forehead recorded PPG data and dailyactivity movement data our proposed algorithm gives 944 934 and 937 accuracies respectively Significant reductions in HR and SpO2 errors 23 bpm and 27 were noted when the artifacts that were identified by SVMMNA were removed from the original signal than without 173 bpm and 54 The accuracy and error values of our proposed method were significantly higher and lower respectively than all other detection methods Another advantage of our method is its ability to provide highly accurate onset and offset detection times of MNAs This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed which is the subject of the companion paper to this article Finally our MNA detection algorithm is realtime realizable as the computational speed on the 7s PPG data segment was found to be only 7 ms with a Matlab code
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