Heartbeat Prediction using Mel Spectrogram and MFCC Value

Heartbeat prediction is a critical area in healthcare, with early detection being essential for successful treatment. This project presents a heartbeat prediction model that utilizes Mel-Frequency Cepstral Coefficients (MFCC) and Mel Spectrogram values to classify heartbeat sounds as normal and abnormal. The Mel Spectrogram and MFCC values are extracted with the help of a python library named Librosa from the audio files of heartbeat sounds and used to train the CNN model. Convolution 2D layers are applied to train the model which is then tested on a dataset of heartbeat sounds to evaluate its performance. The model shows tremendous results in classifying heartbeat sounds, making it a valuable tool in the early detection and treatment of heart disease. The proposed model also has the potential to be incorporated into healthcare systems to automate the diagnosis and monitoring of cardiac disease in healthcare systems.