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We present an innovative technique that leverages electromyography (EMG) signal features to identify various heart diseases. These include heart failure (HF), arrhythmia or irregular heart rhythms (AHR), cardiomyopathy, heart muscle disease (HMD), heart valve disease (HVD), heart attack (HA), and coronary artery disease (CAD), which are some of the earliest detectable types of heart conditions. Heart valve disease arises when one or more valves in the heart do not operate correctly. Some people are born with this condition, referred to as congenital heart valve disease. It may exist independently or alongside other congenital heart disease. We introduce a Bayesian Network model that diagnoses heart valve disease (HVD) based on EMG signals. The Bayesian Network enables the construction of a more accurate diagnostic model.

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A Heart Disease Diagnosis Utilizing a Bayesian Network-Based Model on EMG Data

  • 会议论文

  • 发表日期：

    2025年

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  • Man Singh,
  • Bireshwar Dass Mazumdar,
  • Chetan Vyas

摘要

We present an innovative technique that leverages electromyography (EMG) signal features to identify various heart diseases. These include heart failure (HF), arrhythmia or irregular heart rhythms (AHR), cardiomyopathy, heart muscle disease (HMD), heart valve disease (HVD), heart attack (HA), and coronary artery disease (CAD), which are some of the earliest detectable types of heart conditions. Heart valve disease arises when one or more valves in the heart do not operate correctly. Some people are born with this condition, referred to as congenital heart valve disease. It may exist independently or alongside other congenital heart disease. We introduce a Bayesian Network model that diagnoses heart valve disease (HVD) based on EMG signals. The Bayesian Network enables the construction of a more accurate diagnostic model.