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A real-time ECG monitoring application that streams live cardiac data from Polar H10 chest straps and performs intelligent arrhythmia detection using advanced signal processing algorithms.

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πŸ«€ Advanced ECG Arrhythmia Classification Platform

Real-time cardiovascular signal processing and machine learning-based PVC detection using Polar H10 biomedical sensors

MIT License Python Next.js TypeScript Signal Processing Machine Learning

A sophisticated real-time electrocardiographic monitoring platform that leverages advanced digital signal processing, statistical analysis, and machine learning algorithms for automated detection and classification of premature ventricular contractions (PVCs) in clinical-grade ECG waveforms.

🎯 Project Vision & Objectives

Current Implementation

This platform currently provides real-time ECG acquisition, visualization, and preliminary arrhythmia detection using R-R interval variability analysis. The system demonstrates professional-grade signal processing capabilities with medical device-inspired interface design.

Strategic Roadmap

The ultimate goal is to develop a comprehensive machine learning-based PVC classification system incorporating:

  • Deep Learning Models: Convolutional Neural Networks (CNNs) for morphological feature extraction
  • Advanced Statistical Methods: Wavelet transforms, HRV analysis, and spectral domain features
  • Clinical-Grade Classification: Multi-class arrhythmia detection (PVC, PAC, VT, SVT)
  • Real-time Inference: Edge computing optimization for sub-100ms classification latency
  • Federated Learning: Privacy-preserving distributed model training across patient populations

πŸ”¬ Technical Architecture & Signal Processing Pipeline

Current System Components

Real-time ECG Monitoring Interface Figure 1: Real-time ECG waveform visualization with integrated arrhythmia detection and clinical-grade scaling controls

The platform implements a sophisticated multi-tier architecture for biomedical signal acquisition and processing:

1. Biomedical Data Acquisition Layer

  • Hardware: Polar H10 chest-mounted biomedical sensor
  • Sampling Rate: 130 Hz (7.69ms temporal resolution)
  • ADC Resolution: 24-bit signed integer amplitude encoding
  • Signal Range: Β±3.3mV with ΞΌV precision
  • Communication Protocol: Bluetooth Low Energy (BLE) with GATT services

2. Signal Processing Backend (Python)

  • Real-time Streaming: Asynchronous WebSocket server with <50ms latency
  • Data Preprocessing: Digital filtering and artifact rejection algorithms
  • Feature Extraction: R-peak detection using adaptive thresholding
  • Statistical Analysis: R-R interval variability computation and outlier detection

3. Clinical Visualization Frontend (TypeScript/React)

  • Medical-Grade Display: Chart.js-powered ECG rendering with clinical scaling
  • Interactive Controls: Time/voltage scale adjustment (mm/s, mm/mV standards)
  • Real-time Analytics: Live heart rate computation and arrhythmia counting

ECG Control Interface Figure 2: Advanced control panel featuring medical device-standard time/voltage scaling and configurable visualization parameters

πŸ“Š Current Feature Set

Real-time Signal Processing

  • Adaptive R-Peak Detection: Morphology-based peak identification with noise immunity
  • Heart Rate Variability (HRV): Statistical analysis of R-R interval distributions
  • Arrhythmia Detection: Rule-based PVC identification using temporal pattern analysis
  • Signal Quality Assessment: Real-time SNR estimation and electrode contact monitoring

Clinical Interface Design

  • Medical Device Standards: ECG paper-equivalent scaling (25mm/s, 10mm/mV)
  • Navigation Controls: Temporal scrolling through historical waveform data
  • Live Monitoring Mode: Auto-scrolling real-time display with configurable time windows
  • Multi-scale Visualization: Adjustable time/voltage ranges for detailed morphology analysis

Setup Instructions Figure 3: Comprehensive setup workflow with integrated system status monitoring and troubleshooting guidance

πŸ€– Machine Learning Development Roadmap

Phase 1: Data Pipeline & Feature Engineering

  • Signal Preprocessing: Bandpass filtering (0.5-40Hz), baseline wandering removal
  • Morphological Features: QRS complex width, amplitude ratios, template matching
  • Temporal Features: RR-interval statistics, HRV parameters (SDNN, RMSSD, pNN50)
  • Spectral Analysis: Power spectral density, frequency domain HRV metrics

Phase 2: Classical ML Baseline Models

  • Support Vector Machines: RBF kernel optimization for non-linear classification
  • Random Forest Ensembles: Feature importance ranking and overfitting prevention
  • Gradient Boosting: XGBoost/LightGBM with hyperparameter optimization
  • Performance Metrics: Sensitivity, specificity, PPV, NPV with clinical validation

Phase 3: Deep Learning Implementation

  • 1D CNNs: Raw ECG waveform classification with learnable filters
  • LSTM/GRU Networks: Temporal sequence modeling for rhythm analysis
  • Transformer Architecture: Attention mechanisms for long-range dependencies
  • Transfer Learning: Pre-trained models from large ECG databases (MIT-BIH, PTB-XL)

Phase 4: Advanced Statistical Methods

  • Wavelet Transform Analysis: Multi-resolution time-frequency decomposition
  • Independent Component Analysis: Artifact separation and signal enhancement
  • Empirical Mode Decomposition: Non-linear signal component extraction
  • Bayesian Classification: Uncertainty quantification and confidence intervals

πŸ₯ Clinical Applications & Validation

Target Use Cases

  • Continuous Cardiac Monitoring: ICU and telemetry ward applications
  • Sports Cardiology: Athletic performance and arrhythmia screening
  • Telemedicine: Remote patient monitoring with automated alerting
  • Research Applications: Clinical trials and epidemiological studies
  • Educational Tools: Medical student training and ECG interpretation learning

Validation Framework

  • Clinical Dataset Integration: MIT-BIH Arrhythmia Database, AHA Database
  • Cross-validation Protocols: Patient-independent evaluation strategies
  • Regulatory Compliance: FDA 510(k) pathway considerations for medical devices
  • Performance Benchmarking: Comparison with commercial Holter monitors

πŸ› οΈ Technical Implementation Stack

Backend Infrastructure

# Core Technologies
- Python 3.8+ (Asyncio concurrency)
- WebSocket Protocol (RFC 6455 compliance)
- Bleak BLE Stack (Cross-platform Bluetooth)
- NumPy/SciPy (Scientific computing)
- Scikit-learn (ML pipeline)
- PyTorch/TensorFlow (Deep learning)

Frontend Architecture

// Modern Web Technologies
- Next.js 15 (React framework with SSR)
- TypeScript (Static type checking)
- Chart.js (Hardware-accelerated rendering)
- Tailwind CSS (Utility-first styling)
- WebSocket Client (Real-time communication)
- Web Workers (Background processing)

Data Processing Pipeline

Polar H10 β†’ BLE β†’ Python Server β†’ WebSocket β†’ React Client
   130Hz      ↓       ↓             ↓           ↓
 Raw ECG β†’ Filtering β†’ R-peak β†’ Feature β†’ Visualization
          Detection  Extraction  Classification

πŸ“ˆ Performance Specifications

Metric Current Performance Target (ML Enhanced)
Sampling Rate 130 Hz 130 Hz (maintained)
Detection Latency <100ms <50ms
R-peak Accuracy >95% >99%
PVC Sensitivity 85% (rule-based) >95% (ML-based)
False Positive Rate 5-10% <2%
Processing Throughput Real-time Real-time + batch

πŸ”¬ Research & Development Priorities

Immediate Development (Q2 2025)

  1. Enhanced Signal Processing: Advanced filtering and noise reduction algorithms
  2. Feature Engineering: Comprehensive morphological and temporal feature extraction
  3. Dataset Collection: Integration with public ECG databases and annotation tools
  4. Model Development: Implementation of baseline ML classification algorithms

Medium-term Goals (Q3-Q4 2025)

  1. Deep Learning Pipeline: CNN/LSTM hybrid architectures for ECG classification
  2. Real-time Inference: Model optimization and edge deployment strategies
  3. Clinical Validation: Performance evaluation on diverse patient populations
  4. Regulatory Preparation: Documentation for medical device approval processes

Long-term Vision (2026+)

  1. Multi-lead ECG Support: 12-lead clinical ECG integration capabilities
  2. Federated Learning: Privacy-preserving distributed model training
  3. Clinical Decision Support: Integration with electronic health records (EHR)
  4. Commercial Deployment: SaaS platform for healthcare institutions

πŸš€ Getting Started

Prerequisites

  • Hardware: Polar H10 chest strap sensor
  • Software: Python 3.8+, Node.js 18+, Bluetooth 4.0+ adapter
  • OS Compatibility: Windows 10+, macOS 10.15+, Ubuntu 18.04+

Quick Installation

# Clone repository
git clone https://github.com/mirkovicdev/Polar-H10-ECG-Arrythmia-classifier.git
cd Polar-H10-ECG-Arrythmia-classifier

# Setup Python environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Setup Node.js dependencies
npm install

# Launch system
python ecg_websocket_server.py  # Terminal 1
npm run dev                     # Terminal 2

System Validation

  1. Navigate to http://localhost:3000
  2. Follow setup instructions for Polar H10 pairing
  3. Verify real-time ECG waveform display
  4. Test arrhythmia detection with controlled breathing patterns

πŸ“Š Data Science & Analytics

Feature Engineering Pipeline

The platform implements a comprehensive feature extraction framework for ECG signal characterization:

  • Morphological Features: QRS duration, T-wave amplitude, ST-segment analysis
  • Temporal Features: RR-intervals, heart rate variability metrics
  • Frequency Domain: Power spectral density, spectral entropy
  • Nonlinear Dynamics: Approximate entropy, sample entropy, fractal dimensions

Model Development Strategy

# Example ML Pipeline Structure
ECGClassifier = Pipeline([
    ('preprocessor', ECGSignalProcessor()),
    ('feature_extractor', MorphologicalFeatures()),
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier(n_estimators=100))
])

πŸ”’ Medical & Regulatory Compliance

⚠️ IMPORTANT MEDICAL DISCLAIMER: This platform is currently designed for research and educational purposes only. It is not approved as a medical device and should not be used for clinical diagnosis or treatment decisions. All cardiac rhythm abnormalities require evaluation by qualified healthcare professionals.

Regulatory Pathway

  • FDA Class II Medical Device: Planned 510(k) submission pathway
  • CE Marking: European medical device regulation compliance
  • ISO 13485: Quality management system for medical devices
  • IEC 60601: Medical electrical equipment safety standards

🀝 Contributing to Cardiovascular Technology

We welcome contributions from the biomedical engineering, machine learning, and clinical communities:

Development Areas

  • Signal Processing: Advanced filtering and artifact rejection algorithms
  • Machine Learning: Novel architectures for ECG classification
  • Clinical Validation: Performance evaluation on diverse patient populations
  • User Interface: Medical device usability and accessibility improvements

Research Collaboration

  • Academic Partnerships: University research collaborations welcome
  • Clinical Studies: IRB-approved validation studies with healthcare institutions
  • Open Science: Commitment to reproducible research and open-source development

πŸ“š Scientific References & Validation

This platform builds upon established research in cardiovascular signal processing and machine learning:

  • ECG Analysis: Pan-Tompkins algorithm, Hamilton-Tompkins method
  • Arrhythmia Classification: MIT-BIH benchmark studies, AHA guidelines
  • Machine Learning: State-of-the-art CNN architectures for biomedical signals
  • Clinical Validation: Sensitivity/specificity benchmarks from peer-reviewed literature

πŸ“„ License & Legal

This project is licensed under the MIT License, promoting open-source collaboration while maintaining intellectual property protections for derivative commercial applications.


Advancing Cardiovascular Care Through Intelligent Signal Processing

Developed with ❀️ for the global biomedical research community

Star History Research Impact Medical Innovation

ecg-webapp
β”œβ”€ app
β”‚  β”œβ”€ favicon.ico
β”‚  β”œβ”€ globals.css
β”‚  β”œβ”€ layout.tsx
β”‚  └─ page.tsx
β”œβ”€ components
β”‚  β”œβ”€ BurdenChart.tsx
β”‚  β”œβ”€ ECGChart.tsx
β”‚  └─ TrainingDebug.tsx
β”œβ”€ ecg_websocket_server.py
β”œβ”€ eslint.config.mjs
β”œβ”€ img1.png
β”œβ”€ img2.png
β”œβ”€ img3.png
β”œβ”€ LICENSE
β”œβ”€ next.config.ts
β”œβ”€ package-lock.json
β”œβ”€ package.json
β”œβ”€ postcss.config.mjs
β”œβ”€ public
β”‚  β”œβ”€ file.svg
β”‚  β”œβ”€ globe.svg
β”‚  β”œβ”€ next.svg
β”‚  β”œβ”€ vercel.svg
β”‚  └─ window.svg
β”œβ”€ README.md
β”œβ”€ requirements.txt
β”œβ”€ tsconfig.json
└─ utils
   β”œβ”€ beatHistory.ts
   β”œβ”€ burden.ts
   β”œβ”€ MorphologyTrainer.ts
   β”œβ”€ PVCDetector.ts
   └─ temporalBurden.ts

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A real-time ECG monitoring application that streams live cardiac data from Polar H10 chest straps and performs intelligent arrhythmia detection using advanced signal processing algorithms.

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