Listen2YourHeart is a publically available, extendable framework for training Neural Networks via Contrastive SSL learning, for Phonocardiogram classification.

Public repository associated with: "honocardiogram classification using 1-dimensional inception time convolutional neural network"

Heart Sound Classification (PCG) — TensorFlow + FastAPI; live demo on HF Spaces

Final year capstone project for heart disease detection using a multi-modal approach. We trained separate ML models on ECG, PCG, and PPG signals and fused their confidence scores using a mathematical method to provide a more accurate and holistic risk assessment. Project Members: Anandakrishnan A and Aryan Matte

The main objective of this project is to design and implement an automatic classification method, CNN, to classify cardiovascular diseases (CVDs). Diseases covered include Mitral Stenosis (MS), Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Valve Prolapse (MVP), along with normal heart sound.

This project builds a deep-learning-based heartbeat sound classification system using MFCC features and multiple models including CNN, BiLSTM, and a Hybrid CNN–BiLSTM architecture. The system detects and classifies heart sounds into normal, murmur, and artifact categories, supporting early cardiac abnormality detection.

Heart Sound Segmenter implemented using a Convolutional Neural Network (CNN) on heterogeneus platforms (RISC-V included, through AIRISC architecture)

A Streamlit app for CWT & FFT signal analysis with PCG feature detection, built entirely from scratch.

FPGA design for extracting PSD, Hilbert, Wavelet, and Homomorphic envelograms from phonocardiogram (PCG) recordings.