TY - GEN
T1 - A New Deep Learning Method for Accurate Cardiac Heart Failure Prediction from RR Interval Measurements
AU - Mishahira, N.
AU - Nair, Gayathri Geetha
AU - Houkan, Mohammad Talal
AU - Sadasivuni, Kishor Kumar
AU - Geetha, Mithra
AU - Al-Maadeed, Somaya
AU - Albusaidi, Asiya
AU - Subramanian, Nandhini
AU - Yalcin, Huseyin Cagatay
AU - Ouakad, Hassen M.
AU - Bahadur, Issam
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - cardiovascular diseases are the major cause of death worldwide. Early detection of heart failure will assist patients and medical professionals in taking better precautions to reduce risks. The objective of this study is to find a technique that can reliably forecast the risk of cardiovascular illnesses. With the help of the training data we offer, deep learning algorithms like Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) make these predictions. Prediction accuracy will be reduced by a lack of medical data. As a part of our study, we examined DNN architectures to forecast cardiac failure. Over the training data, existing deep learning methods were employed. A new deep learning method that can predict heart failure using RR interval measurements is developed by comparing the accuracy performance of the proposed and existing models. The Physiobank NSR-RR and CHF-RR databases were used to compile the findings. The new model, which was based on experimental findings using these two free RR interval databases, attained a 94% accuracy rate compared to the existing model's 93.1% accuracy rate.
AB - cardiovascular diseases are the major cause of death worldwide. Early detection of heart failure will assist patients and medical professionals in taking better precautions to reduce risks. The objective of this study is to find a technique that can reliably forecast the risk of cardiovascular illnesses. With the help of the training data we offer, deep learning algorithms like Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) make these predictions. Prediction accuracy will be reduced by a lack of medical data. As a part of our study, we examined DNN architectures to forecast cardiac failure. Over the training data, existing deep learning methods were employed. A new deep learning method that can predict heart failure using RR interval measurements is developed by comparing the accuracy performance of the proposed and existing models. The Physiobank NSR-RR and CHF-RR databases were used to compile the findings. The new model, which was based on experimental findings using these two free RR interval databases, attained a 94% accuracy rate compared to the existing model's 93.1% accuracy rate.
KW - Deep learning
KW - Heart Failure
KW - Time series
KW - Time-LeNet. Database
UR - http://www.scopus.com/inward/record.url?scp=85154557391&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85154557391&partnerID=8YFLogxK
U2 - 10.1109/ASSIC55218.2022.10088409
DO - 10.1109/ASSIC55218.2022.10088409
M3 - Conference contribution
AN - SCOPUS:85154557391
T3 - ASSIC 2022 - Proceedings: International Conference on Advancements in Smart, Secure and Intelligent Computing
BT - ASSIC 2022 - Proceedings
A2 - Mohanty, Jnyana Ranjan
A2 - Tripathy, Hrudaya Kumar
A2 - Mishra, Sambit Kumar
A2 - Mishra, Sushruta
A2 - Sahoo, Kshira Sagar
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022
Y2 - 19 November 2022 through 20 November 2022
ER -
