Heartbeat Anomaly Detector

--- license: mit library_name: pytorch pipeline_tag: audio-classification tags: - audio - heart-sound - medical - pytorch - classification - anomaly-detection - healthcare - pcg - heartbeat - medical-ai widget: - src: https://huggingface.co/ai-mitra/heartbeat-anomaly-detector/resolve/main/Normal_Heart_Sound.mp3 example_title: Normal Heart Sound - src: https://huggingface.co/ai-mitra/heartbeat-anomaly-detector/resolve/main/Heart_Failure_Sound.mp3 example_title: Heart Failure Sound language: - en --- # Heartbeat Anomaly Detector A deep learning-based heart sound classification system that analyzes phonocardiogram (PCG) recordings to detect cardiac anomalies. This model combines AI-powered classification with rule-based analysis to provide comprehensive heart sound assessment. ## Live Demo Try the model in action: <a href="https://heartbeat.kalkut.in/" target="_blank"><b>https://heartbeat.kalkut.in/</b></a> Upload your heart sound recording and get instant AI-powered analysis with classification, heart rate detection, and risk assessment. ## Model Description The Heartbeat Anomaly Detector is a CNN-based deep learning model designed for automated heart sound analysis. It processes audio recordings of heart sounds and classifies them into four categories while providing detailed cardiac metrics and risk assessments. ### Key Features - **Multi-Class Classification**: Identifies normal heart sounds, murmurs, extra heart sounds, and artifacts - **Hybrid Analysis**: Combines deep learning predictions with rule-based clinical logic - **Real-time Processing**: Fast inference suitable for clinical workflows - **Comprehensive Metrics**: Provides heart rate, rhythm analysis, and risk assessment - **RESTful API**: Easy integration via FastAPI endpoints ## Model Architecture ### Neural Network Design The model uses a 4-layer Convolutional Neural Network (CNN) architecture: ``` Input: [1, 60000] - 30 seconds of audio at 2000 Hz sample rate Layer 1: Conv1d(1→32) + BatchNorm + ReLU + MaxPool + Dropout(0.2) Layer 2: Conv1d(32→64) + BatchNorm + ReLU + MaxPool + Dropout(0.2) Layer 3: Conv1d(64→128) + BatchNorm + ReLU + MaxPool + Dropout(0.3) Layer 4: Conv1d(128→256) + BatchNorm + ReLU + AdaptiveAvgPool + Dropout(0.3) Fully Connected: 256 → 128 → 64 → 4 classes ``` ### Technical Specifications - **Input Format**: Mono audio, 2000 Hz sample rate, 30-second...

Source context: 12 downloads · 0 likes · Pipeline audio-classification · Library pytorch · Repo ai-mitra/heartbeat-anomaly-detector