TY - GEN
T1 - Electro-Mechanical Data Fusion for Heart Health Monitoring
AU - Yakut, Kemal
AU - Usman, Muhammad
AU - Xue, Wei
AU - Haas, Francis M.
AU - Hirsh, Robert A.
AU - Boothby, Joseph
AU - Zhao, Xinghui
AU - Petty, Tyler
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Heart disease is a major public health problem and one of the leading causes of death worldwide. Therefore, cardiac monitoring is of great importance for early detection and prevention of adverse conditions. Recently, there has been extensive research interest in long-term, continuous, and non-invasive cardiac monitoring using wearable technology. Here we introduce a wearable device for monitoring heart health. This prototype consists of three sensors to monitor electrocardiogram (ECG), phonocardiogram (PCG), and seismocardiogram (SCG) signals, and a microcontroller module with Bluetooth wireless connectivity. Our preliminary results show that the device can record all three signals in real time. In our initial attempt at signal processing, a recurrent neural network (RNN) based machine learning algorithm, Long Short-Term Memory (LSTM), is used to monitor and identify key features in the ECG data. The next phase of our research will include cross-examination of all three sensor signals, development of machine learning algorithms on PCG and SCG signals, and continuous improvement of the wearable device.
AB - Heart disease is a major public health problem and one of the leading causes of death worldwide. Therefore, cardiac monitoring is of great importance for early detection and prevention of adverse conditions. Recently, there has been extensive research interest in long-term, continuous, and non-invasive cardiac monitoring using wearable technology. Here we introduce a wearable device for monitoring heart health. This prototype consists of three sensors to monitor electrocardiogram (ECG), phonocardiogram (PCG), and seismocardiogram (SCG) signals, and a microcontroller module with Bluetooth wireless connectivity. Our preliminary results show that the device can record all three signals in real time. In our initial attempt at signal processing, a recurrent neural network (RNN) based machine learning algorithm, Long Short-Term Memory (LSTM), is used to monitor and identify key features in the ECG data. The next phase of our research will include cross-examination of all three sensor signals, development of machine learning algorithms on PCG and SCG signals, and continuous improvement of the wearable device.
UR - http://www.scopus.com/inward/record.url?scp=85139030379&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139030379&partnerID=8YFLogxK
U2 - 10.1109/ICHI54592.2022.00057
DO - 10.1109/ICHI54592.2022.00057
M3 - Conference contribution
AN - SCOPUS:85139030379
T3 - Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
SP - 357
EP - 362
BT - Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Healthcare Informatics, ICHI 2022
Y2 - 11 June 2022 through 14 June 2022
ER -