TY - GEN
T1 - A Novel Smartphone-based Personalized Atrial Fibrillation Detection
T2 - 2022 IEEE-EMB Special Topic Conference on Healthcare Innovations and Point of Care Technologies, HI-POCT 2022
AU - Tabei, Fatemehsadat
AU - Abohelwa, Mostafa
AU - Davis, Daniel
AU - Sethi, Pooja
AU - Nugent, Kenneth
AU - Woon Chong, Jo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper aims to propose a novel system that can be used for personalized atrial fibrillation (AF) detection using smartphone photoplethysmogram (PPG) signals. First, we detect atrial fibrillation (AF) signals from normal heart rhythm signals, and then the AF smartphone PPG signals are used for personalized AF detection. We extracted 19 features from the fiducial and non-fiducial information of smartphone PPG signals. These features were used for both classifying AF signals from normal signals and personalized AF detection of each subject. The ensemble algorithms with the boosting and bagging functions were used for both the AF detection from normal and personalized AF detection processes. We achieved 100% accuracy for detecting AF signals from normal signals and 96.08%. for personalized AF detection. These preliminary results indicate that our proposed system can be used for personalized AF detection and management which has been recently gained attention from researchers.
AB - This paper aims to propose a novel system that can be used for personalized atrial fibrillation (AF) detection using smartphone photoplethysmogram (PPG) signals. First, we detect atrial fibrillation (AF) signals from normal heart rhythm signals, and then the AF smartphone PPG signals are used for personalized AF detection. We extracted 19 features from the fiducial and non-fiducial information of smartphone PPG signals. These features were used for both classifying AF signals from normal signals and personalized AF detection of each subject. The ensemble algorithms with the boosting and bagging functions were used for both the AF detection from normal and personalized AF detection processes. We achieved 100% accuracy for detecting AF signals from normal signals and 96.08%. for personalized AF detection. These preliminary results indicate that our proposed system can be used for personalized AF detection and management which has been recently gained attention from researchers.
UR - http://www.scopus.com/inward/record.url?scp=85128635467&partnerID=8YFLogxK
U2 - 10.1109/HI-POCT54491.2022.9744074
DO - 10.1109/HI-POCT54491.2022.9744074
M3 - Conference contribution
AN - SCOPUS:85128635467
T3 - Healthcare Innovations and Point of Care Technologies Conference, HI-POCT 2022
SP - 18
EP - 21
BT - Healthcare Innovations and Point of Care Technologies Conference, HI-POCT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 March 2022 through 11 March 2022
ER -