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Assessment of Cardiac Dynamics and Risk Factor Analysis Using Deep Neural Nets
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Author(s): Jayanthi G. (Sri Ramachandra Institute of Higher Education and Research, India), Purushothaman R. (Siddartha Institute of Science and Technology, India), Sivant Moduguru (Sri Ramachandra Institute of Higher Education and Research, India), Harshini Senthil Kumaran (Sri Ramachandra Institute of Higher Education and Research, India), Siva Kumar Reddy C. H. V. (Sri Ramachandra Institute of Higher Education and Research, India), Aparna Shankar (Sri Ramachandra Institute of Higher Education and Research, India), Anbu Ezhilmathi Nambi (Sri Ramachandra Institute of Higher Education and Research, India)and Anantha Narayanan Sampath Varadharajan (Sri Ramachandra Institute of Higher Education and Research, India)
Copyright: 2022
Pages: 28
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.ch007
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Abstract
Cardiovascular disease (CVD) is a medical condition that leads to risk of heart disease such as stroke or cardiac arrest. Cardiac attack is a medical condition found in different age groups irrespective of gender. In a clinical study, there are many ways of interpreting the risk factors. The most common risk factors indicating sudden cardiac arrest are glucose, body mass index (BMI), and habitation such as smoking. The difficulties faced by the clinicians are the primary focus of this study. The complexity in clinical stages in examination of medical condition needs to be resolved considering the symptoms and other risk factors leading to sudden cardiac arrests and deaths. Thus, validation of clinical examination at times is a laborious and time-consuming process, while tracking patient history is voluminous over a period of time. This chapter presents the analysis of risk factors causing cardiovascular diseases. The statistical significance and clinical validation of the computer-assisted tool is presented in this study.
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