TY - GEN
T1 - Robust Assessment of Photoplethysmogram Signal Quality in the Presence of Atrial Fibrillation
AU - Pereira, Tania
AU - Gadhoumi, Kais
AU - Ma, Mitchell
AU - Colorado, Rene
AU - Keenan, Kevin J.
AU - Meisel, Karl
AU - Hu, Xiao
PY - 2018/9
Y1 - 2018/9
N2 - A great deal of algorithms currently available to assess the quality of photoplethysmogram (PPG) signals is based on the similarity between pulses to derive signal quality indices. This approach has limitations when pulse morphology become variable due to the presence of some arrhythmia as in the case of atrial fibrillation (AFib). AFib is a heart arrhythmia characterized in the electrocardiogram mainly by an irregular irregularity. This arrhythmicity is reflected on PPG pulses by the presence of non-uniform pulses and poses challenges in the evaluation of the signal quality. In this work, we first test the performance of few algorithms from the body of methods reported in literature using a dataset of PPG records with AFib, and demonstrate their limitation. Second, we present a novel SVM-based classifier for PPG quality assessment in 30s-long segments of PPG records extracted from pulse oximetry data of 13 stroke patients admitted to the UCSF medical center neuro ICU. 40 time-domain, frequency domain and non-linear features were extracted from all segments. Using an independent test set, the classifier reached a 0.94 accuracy, 0.95 sensitivity and 0.91 specificity. These results demonstrate the robustness of the proposed method in properly evaluating PPG signal quality in the presence of atrial fibrillation.
AB - A great deal of algorithms currently available to assess the quality of photoplethysmogram (PPG) signals is based on the similarity between pulses to derive signal quality indices. This approach has limitations when pulse morphology become variable due to the presence of some arrhythmia as in the case of atrial fibrillation (AFib). AFib is a heart arrhythmia characterized in the electrocardiogram mainly by an irregular irregularity. This arrhythmicity is reflected on PPG pulses by the presence of non-uniform pulses and poses challenges in the evaluation of the signal quality. In this work, we first test the performance of few algorithms from the body of methods reported in literature using a dataset of PPG records with AFib, and demonstrate their limitation. Second, we present a novel SVM-based classifier for PPG quality assessment in 30s-long segments of PPG records extracted from pulse oximetry data of 13 stroke patients admitted to the UCSF medical center neuro ICU. 40 time-domain, frequency domain and non-linear features were extracted from all segments. Using an independent test set, the classifier reached a 0.94 accuracy, 0.95 sensitivity and 0.91 specificity. These results demonstrate the robustness of the proposed method in properly evaluating PPG signal quality in the presence of atrial fibrillation.
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U2 - 10.22489/CinC.2018.254
DO - 10.22489/CinC.2018.254
M3 - Conference contribution
AN - SCOPUS:85068753740
T3 - Computing in Cardiology
BT - Computing in Cardiology Conference, CinC 2018
PB - IEEE Computer Society
T2 - 45th Computing in Cardiology Conference, CinC 2018
Y2 - 23 September 2018 through 26 September 2018
ER -