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: https://heartbeat.kalkut.in/

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 duration
Model Size: Approximately 10-20 MB
Framework: PyTorch
Inference Time: < 1 second per audio file
Device Support: CPU and CUDA-enabled GPU

Classes

The model classifies heart sounds into four categories:

Class	Description	Clinical Significance
Normal	Regular heart sounds with clear S1/S2	Healthy cardiac function
Murmur	Abnormal whooshing sounds	May indicate valve disorders
Extra Heart Sounds	Additional sounds (S3, S4, clicks)	Possible heart failure or structural issues
Artifact	Poor signal quality or noise	Recording quality issue

Signal Processing Pipeline

Preprocessing Steps

Audio Loading: Load audio file at 2000 Hz sample rate
Median Filtering: Remove impulse noise
Bandpass Filtering: Extract 20-200 Hz range (primary heart sound frequencies)
Notch Filtering: Remove 50/60 Hz power line interference
Envelope Enhancement: Apply Hilbert transform for amplitude modulation
Normalization: RMS-based amplitude normalization
Smoothing: Savitzky-Golay filter for signal refinement

Feature Extraction

Heart Rate Detection: BioSPPy or peak detection algorithms
S1/S2 Peak Identification: First and second heart sound detection
Rhythm Analysis: Heart rate variability and irregularity scoring
Amplitude Analysis: S1/S2 amplitude ratios

Usage

Installation

pip install fastapi uvicorn torch librosa numpy scipy matplotlib biosppy

Quick Start

1. Download the Model

python

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="ai-mitra/heartbeat-anomaly-detector",
    filename="heartbeat-anomaly-detector-model.pt"
)

2. Run the API Server

bash

# Start the FastAPI server
uvicorn inference:app --host 0.0.0.0 --port 8000

3. Analyze Heart Sounds

Using cURL:

curl "http://localhost:8000/hb?filename=sounds/Heart_Failure_Sound.mp3"

Using Python:

python

import requests

response = requests.get(
    "http://localhost:8000/hb",
    params={"filename": "sounds/Normal_Heart_Sound.mp3"}
)
result = response.json()

print(f"Classification: {result['classification']['result']}")
print(f"Confidence: {result['classification']['confidence_percentage']}")
print(f"Heart Rate: {result['heart_rate']['average_bpm']} BPM")

Using JavaScript (React/Frontend):

javascript

fetch('http://localhost:8000/hb?filename=sounds/Normal_Heart_Sound.mp3')
  .then(response => response.json())
  .then(data => {
    console.log('Classification:', data.classification.result);
    console.log('Confidence:', data.classification.confidence_percentage);
    console.log('Heart Rate:', data.heart_rate.average_bpm, 'BPM');
  });

API Endpoints

GET /hb

Analyze a heart sound audio file.

Parameters:

filename (string, required): Path to the audio file

Response:

json

{
  "status": "OK",
  "analysis_info": {
    "target_file": "Normal_Heart_Sound",
    "analysis_timestamp": "2026-01-13T10:30:00",
    "analysis_mode": "Hybrid (AI + Rule-Based)",
    "plot_filename": "Normal_Heart_Sound_analysis_20260113_103000.png",
    "plot_path": "/plot/Normal_Heart_Sound_analysis_20260113_103000.png"
  },
  "ai_prediction": {
    "predicted_class": "normal",
    "confidence": 0.9245,
    "probabilities": {
      "normal": 0.9245,
      "murmur": 0.0432,
      "extrahs": 0.0221,
      "artifact": 0.0102
    }
  },
  "classification": {
    "result": "Normal Heart Sounds",
    "confidence": 0.9245,
    "confidence_percentage": "92.5%"
  },
  "heart_rate": {
    "average_bpm": 75.3,
    "hr_mean": 75.3,
    "hr_std": 3.2,
    "hr_min": 70.1,
    "hr_max": 81.5,
    "irregularity_score": 0.042
  },
  "heart_sounds": {
    "num_s1": 38,
    "num_s2": 37,
    "s1_peaks": [245, 1523, 2801, ...],
    "s2_peaks": [876, 2154, 3432, ...],
    "s1_s2_ratio": 1.027
  },
  "detailed_findings": [
    {
      "condition": "AI Classification: Normal",
      "description": "Deep learning model prediction with 92.5% confidence",
      "severity": "Low",
      "recommendation": "AI Model: Normal detected",
      "source": "AI Model"
    }
  ],
  "recommendations": [
    "Analysis Method: Hybrid (AI + Rule-Based)",
    "AI-Rule Agreement: High"
  ]
}

GET /health

Check API health status.

Response:

json

{
  "status": "OK",
  "model_loaded": true,
  "biosppy_available": true,
  "timestamp": "2026-01-13T10:30:00"
}

GET /plot/{filename}

Retrieve generated analysis plot image.

Response: PNG image file

Clinical Features

Arrhythmia Detection

The system detects various cardiac rhythm abnormalities:

Tachycardia: Heart rate > 100 BPM
Severe Tachycardia: Heart rate > 150 BPM
Bradycardia: Heart rate < 50 BPM
Severe Bradycardia: Heart rate < 40 BPM
Irregular Rhythm: High heart rate variability (possible atrial fibrillation)

Heart Attack Risk Assessment

Multi-factor risk scoring based on:

Heart rate extremes (tachycardia/bradycardia)
Rhythm irregularity
Multiple high-severity findings
Combined risk factors

Risk Levels:

HIGH: Risk score ≥ 6 - Immediate medical attention required
MODERATE: Risk score 3-5 - Urgent cardiology consultation needed
LOW-MODERATE: Risk score 1-2 - Follow-up recommended
LOW: Risk score 0 - Continue preventive care

Training Details

Dataset

The model was trained on a custom dataset of heart sound recordings including:

Normal heart sounds
Murmurs (various types)
Extra heart sounds (S3, S4, clicks)
Artifacts and noise

Training Configuration

Sample Rate: 2000 Hz
Audio Duration: 30 seconds
Batch Normalization: Applied in all convolutional layers
Dropout Rates: 0.2-0.5 for regularization
Loss Function: Cross-entropy loss
Optimizer: Adam (typical for CNN training)

Performance Baselines

Training statistics for different conditions:

Condition	Avg Heart Rate	Irregularity Score	S1/S2 Ratio
Normal	104.5 ± 7.6 BPM	0.317 ± 0.074	1.171 ± 0.244
Murmur	87.5 ± 27.5 BPM	0.185 ± 0.150	0.733 ± 0.092
Extra HS	82.3 ± 9.2 BPM	0.570 ± 0.068	0.836 ± 0.151

Limitations and Disclaimers

Important Medical Disclaimer

This is an automated analysis tool for educational and research purposes only
Results should NOT be used for medical diagnosis or treatment decisions
Always consult qualified healthcare professionals for medical concerns
The analysis may produce false positives/negatives
Professional medical evaluation is required for all cardiac concerns

Technical Limitations

Audio Quality Dependent: Requires clear recordings without excessive noise
Limited Pathology Coverage: Trained on specific condition types
No Real-time Monitoring: Designed for recorded audio analysis
Context Required: Best used as part of comprehensive cardiac assessment
Regional Variations: May not account for all demographic differences

Use Cases

Appropriate Use

Educational: Medical training and learning
Research: Cardiac audio analysis studies
Screening: Initial triage in low-resource settings
Monitoring: Tracking changes over time with physician oversight
Development: Building advanced cardiac diagnostic systems

Inappropriate Use

Primary diagnostic tool without physician review
Emergency medical decision-making
Replacement for echocardiography or ECG
Self-diagnosis or self-treatment

Model Files

heartbeat-anomaly-detector-model.pt - PyTorch model weights (main file)
inference.py - FastAPI server with inference code
requirements.txt - Python dependencies

Dependencies

txt

fastapi>=0.68.0
uvicorn>=0.15.0
torch>=1.9.0
librosa>=0.9.0
numpy>=1.19.0
scipy>=1.7.0
matplotlib>=3.3.0
biosppy>=0.8.0 (optional but recommended)

Citation

If you use this model in your research, please cite:

bibtex

@misc{heartbeat-anomaly-detector,
  author = {AI Mitra},
  title = {Heartbeat Anomaly Detector: CNN-based Heart Sound Classification},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ai-mitra/heartbeat-anomaly-detector}}
}

License

This project is licensed under the MIT License. See LICENSE file for details.

Contact and Support

For questions, issues, or contributions:

Hugging Face: https://huggingface.co/ai-mitra/heartbeat-anomaly-detector
Issues: Report bugs or request features via the repository issues page
Email: tathagata.mitra@gmail.com

Interested in taking this project forward or collaborating? Feel free to reach out via email.

Acknowledgments

BioSPPy library for cardiac signal processing
PyTorch framework for deep learning
Medical professionals who provided domain expertise

Version History

v2.0.0 (2026-01-13): Initial release with hybrid AI + rule-based analysis
- 4-class CNN classification
- Heart rate and rhythm analysis
- Risk assessment module
- FastAPI integration with CORS support

Remember: This tool is for research and educational purposes. Always seek professional medical advice for health concerns.