TY - GEN
T1 - Real-time prediction of cardiovascular diseases using reservoir-computing and fusion with electronic medical record
AU - Sadasivuni, Sudarsan
AU - Damodaran, Vasundhara
AU - Banerjee, Imon
AU - Sanyal, Arindam
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Cardiovascular diseases (CVDs) are a leading cause of death in USA and globally, but many people suffering from CVDs are asymptomatic in the early stages leading to reduced awareness, and less chances of managing the disease. This work presents a potential solution for at-home monitoring by leveraging predictive power of artificial intelligence (AI) for developing a fusion framework that combines patient electrocardiogram (ECG) and electronic medical record (EMR) for predicting risk of CVDs at an early stage. To improve energy-efficiency of wearable ECG sensor, in-sensor analog reservoir-computing is proposed that precludes need for front-end digitization and transmission of raw sensor data. The fusion framework predicts ischemic heart disease (I20-I25 ICD codes) with area under the receiver operating characteristic (AUROC) of 0.91, and other heart diseases (I30-I52 ICD codes) with AUROC of 0.95 which is better than state-of-the-art while not requiring laboratory test results.
AB - Cardiovascular diseases (CVDs) are a leading cause of death in USA and globally, but many people suffering from CVDs are asymptomatic in the early stages leading to reduced awareness, and less chances of managing the disease. This work presents a potential solution for at-home monitoring by leveraging predictive power of artificial intelligence (AI) for developing a fusion framework that combines patient electrocardiogram (ECG) and electronic medical record (EMR) for predicting risk of CVDs at an early stage. To improve energy-efficiency of wearable ECG sensor, in-sensor analog reservoir-computing is proposed that precludes need for front-end digitization and transmission of raw sensor data. The fusion framework predicts ischemic heart disease (I20-I25 ICD codes) with area under the receiver operating characteristic (AUROC) of 0.91, and other heart diseases (I30-I52 ICD codes) with AUROC of 0.95 which is better than state-of-the-art while not requiring laboratory test results.
KW - artificial intelligence
KW - artificial neural network
KW - cardiovascular disease
KW - data fusion
KW - in-memory computing
KW - reservoir-computer
UR - http://www.scopus.com/inward/record.url?scp=85139027848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139027848&partnerID=8YFLogxK
U2 - 10.1109/AICAS54282.2022.9869980
DO - 10.1109/AICAS54282.2022.9869980
M3 - Conference contribution
AN - SCOPUS:85139027848
T3 - Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
SP - 58
EP - 61
BT - Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
Y2 - 13 June 2022 through 15 June 2022
ER -