TY - GEN
T1 - Detection of Left Ventricular Ejection Fraction Abnormality Using Fusion of Acoustic and Biopotential Characteristics of Precordium
AU - Shokouhmand, Arash
AU - Wen, Haoran
AU - Khan, Samiha
AU - Puma, Joseph A.
AU - Patel, Amisha
AU - Green, Philip
AU - Ayazi, Farrokh
AU - Tavassolian, Negar
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This study develops a wearable monitoring platform for the detection of abnormal left ventricular ejection fraction (LVEF) using a fusion of an accelerometer contact microphone (ACM) and an electrocardiogram (ECG) sensor. Two signal processing chains are designed to annotate ACM and ECG recordings. Afterwards, the pre-ejection period (PEP) and left ventricular ejection time (LVET) are estimated as the time difference between the first heart sound (S1) and the R-peak in ECG signals, and the time difference between the first and second heart sounds (S1 and S2), respectively. The ratio of PEP to LVET is then utilized to differentiate between healthy and abnormal-LVEF groups. The model is evaluated on 15 subjects (8 healthy subjects and 7 subjects with LVEF abnormality) where the ground truth values are the LVEF parameter acquired by the echocardiography machine. An average (± standard deviation) accuracy of 84.47% (± 17.58%) is obtained for the detection of LVEF abnormality for a total of 5989 heartbeats. It is demonstrated that the proposed method is capable of LVEF abnormality detection with accuracies within the range of 54.35% - 100%.
AB - This study develops a wearable monitoring platform for the detection of abnormal left ventricular ejection fraction (LVEF) using a fusion of an accelerometer contact microphone (ACM) and an electrocardiogram (ECG) sensor. Two signal processing chains are designed to annotate ACM and ECG recordings. Afterwards, the pre-ejection period (PEP) and left ventricular ejection time (LVET) are estimated as the time difference between the first heart sound (S1) and the R-peak in ECG signals, and the time difference between the first and second heart sounds (S1 and S2), respectively. The ratio of PEP to LVET is then utilized to differentiate between healthy and abnormal-LVEF groups. The model is evaluated on 15 subjects (8 healthy subjects and 7 subjects with LVEF abnormality) where the ground truth values are the LVEF parameter acquired by the echocardiography machine. An average (± standard deviation) accuracy of 84.47% (± 17.58%) is obtained for the detection of LVEF abnormality for a total of 5989 heartbeats. It is demonstrated that the proposed method is capable of LVEF abnormality detection with accuracies within the range of 54.35% - 100%.
KW - accelerometer contact microphone
KW - electrocardiogram
KW - left ventricular ejection fraction (LVEF)
KW - left ventricular ejection time (LVET)
KW - pre-ejection period
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85144046844&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144046844&partnerID=8YFLogxK
U2 - 10.1109/SENSORS52175.2022.9967355
DO - 10.1109/SENSORS52175.2022.9967355
M3 - Conference contribution
AN - SCOPUS:85144046844
T3 - Proceedings of IEEE Sensors
BT - 2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
T2 - 2022 IEEE Sensors Conference, SENSORS 2022
Y2 - 30 October 2022 through 2 November 2022
ER -