Cardiac Arrhythmia Prediction and Prevention of Heart failure using PCG(PhonoCardioGram) and CNN

Abstract

Abstract: Arrhythmias are abnormal cardiac rhythms. According to WHO, in today's world 31% of deaths occur due to Cardiac Arrhythmia. Life-threatening arrhythmias including Ventricular Tachycardia (VT) and Ventricular Fibrillation are frequent causes of cardiac arrest (VF). The sinus node controls the heart's rhythm by triggering an electrical signal that goes through the heart, causing the heart to beat and circulate blood around throughout. The heart does not pump efficiently if there is too much electrical activity in the top or bottom chambers. Shortness of breath, fainting, an abrupt loss of heart function, and unconsciousness are the most common signs of Arrhythmia, which can result in death within minutes unless the victim receives emergency medical treatment to restart the heart. The purpose of this research is to use the CNN and VGG16 models in conjunction with data augmentation and picture pixel creation to diagnose cardiac arrhythmias using PCG signals. For greater efficiency, phonocardiography (PCG) is also investigated. The majority of arrhythmia detection and classification methods rely solely on surface ECG analysis. So, to improve the efficiency of heart diagnostics, an algorithm is devised that relates to wavelet analysis at several resolutions combining temporal and wavelet properties of Electrocardiogram and Phonocardiogram, as well as ElectrocardiogramPhonocardiogram interactions. We want to be able to classify phonocardiograms (PCGs) or heartbeat recordings. as "normal" or "abnormal" in order to identify individuals who will require further diagnosis. The main concept is to transform each cardiac sound recording (wav file) into a spectrogram image and train a CNN model on that picture. We will then be able to categorize a fresh PCG recording as normal or abnormal.