TY - GEN
T1 - Prediction of LVEF using BiLSTM and Swarm Decomposition-based 24-h HRV Components
AU - Alkhodari, Mohanad
AU - Apostolidis, Georgios
AU - Jelinek, Herbert F.
AU - Hadjileontiadis, Leontios J.
AU - Khandoker, Ahsan H.
N1 - Funding Information:
This work was supported by a grant (award number: 8474000132) from the Healthcare Engineering Innovation Center (HEIC) at Khalifa University, Abu Dhabi, UAE, and by grant (award number: 29934) from the Department of Education and Knowledge (ADEK), Abu Dhabi, UAE.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this study, we investigated the effectiveness of using hourly Bi-Directional Long Short-Term Memory (BiLSTM) classifiers to predict left ventricle ejection fraction (LVEF) groups of CAD patients using their heart rate variability and Swarm Decomposition components. The 24-hour segmentation of patients' HRV data was performed using Cosinor Analysis. The novel Swarm Decomposition algorithm was then applied on the per-hour HRV data to extract the corresponding oscillatory components (HRV-OCs). The OCs represent the four bands in an HRV data, namely the ultra-low frequency (ULF), very-low frequency (VLF), low frequency (LF), and high frequency (HF). The training and classification process followed a leave-one-out scheme and was done for each per-hour HRV-OC. The highest prediction accuracy of LVEF was observed when using the VLF and HF components of HRV at an early morning hour (03-00-04:00 - average accuracy: 75.6%) and an evening hour (18:00-19:00 - average accuracy: 72.7%), respectively. In addition, the classifier achieved high sensitivity levels in predicting the borderline group (up to 76.7%), which is usually ambiguous and hard to diagnose in clinical practice.
AB - In this study, we investigated the effectiveness of using hourly Bi-Directional Long Short-Term Memory (BiLSTM) classifiers to predict left ventricle ejection fraction (LVEF) groups of CAD patients using their heart rate variability and Swarm Decomposition components. The 24-hour segmentation of patients' HRV data was performed using Cosinor Analysis. The novel Swarm Decomposition algorithm was then applied on the per-hour HRV data to extract the corresponding oscillatory components (HRV-OCs). The OCs represent the four bands in an HRV data, namely the ultra-low frequency (ULF), very-low frequency (VLF), low frequency (LF), and high frequency (HF). The training and classification process followed a leave-one-out scheme and was done for each per-hour HRV-OC. The highest prediction accuracy of LVEF was observed when using the VLF and HF components of HRV at an early morning hour (03-00-04:00 - average accuracy: 75.6%) and an evening hour (18:00-19:00 - average accuracy: 72.7%), respectively. In addition, the classifier achieved high sensitivity levels in predicting the borderline group (up to 76.7%), which is usually ambiguous and hard to diagnose in clinical practice.
UR - http://www.scopus.com/inward/record.url?scp=85123499039&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI53629.2021.9624338
DO - 10.1109/CISP-BMEI53629.2021.9624338
M3 - Conference contribution
AN - SCOPUS:85123499039
T3 - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
BT - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
A2 - Li, Qingli
A2 - Wang, Lipo
A2 - Wang, Yan
A2 - Li, Wenwu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Y2 - 23 October 2021 through 25 October 2021
ER -