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PPG-Based Cardiovascular Disease Predictor Using Artificial Intelligence
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Author(s): Dhanalakshmi S. (Avinashilingam Institute for Home Science and Higher Education for Women, India), Gayathiridevi B. (Avinashilingam Institute for Home Science and Higher Education for Women, India), Kiruthika S. (Avinashilingam Institute for Home Science and Higher Education for Women, India)and E Smily Jeya Jothi (Avinashilingam Institute for Home Science and Higher Education for Women, India)
Copyright: 2022
Pages: 22
Source title:
Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death
Source Author(s)/Editor(s): Pradeep Nijalingappa (Bapuji Institute of Engineering and Technology, Davangere, India), Sandeep Kumar Kautish (Lord Buddha Education Foundation, Nepal), Mangesh M. Ghonge (Sandip Institute of Technology and Research Centre, India)and Renjith V. Ravi (MEA Engineering College, India)
DOI: 10.4018/978-1-7998-8443-9.ch010
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Abstract
Heart disease is estimated to be the major cause of death among the middle-aged population worldwide. Researchers assess huge volumes of medical data using a variety of statistical, machine learning, and deep learning methods, supporting healthcare practitioners in predicting heart illness. This work aims to predict the likelihood of people developing heart disease using a wearable wristband that can record photoplethysmography (PPG) signals. Cardiovascular features extracted from the PPG signal are used to train the prediction algorithm. It enables the patient to self-monitor their health and take precautionary measures and treatment at the onset of symptoms of the disease. Random forest, convolutional neural network, long short-term memory networks are trained using publicly available databases comprising both affected and standard parameters and thereby used for comparison with the acquired sensor data for predictive analysis.
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