🚀 Quick Start · 📊 Performance · 🔬 Algorithms · 📖 Docs

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🌍 Language: English | 简体中文 | 繁體中文

98.44% Test Accuracy

98.44% F1 Score

~44K Parameters

<10ms Inference Time

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📚 Project Background

🎓 Course Project

This project is a practical deliverable for the Production Internship Course, aiming to explore ECG-signal-based biometric identification technology.

💡 Motivation

As an emerging biometric modality, ECG offers unique advantages over traditional methods:

Feature	Advantage
🔐 Liveness Detection	ECG signals can only be captured from living subjects, naturally providing anti-spoofing capability.
🧬 Uniqueness	Each person has unique cardiac electrophysiological characteristics.
🛡️ Hard to Forge	Compared with fingerprints or faces, ECG is extremely difficult to replicate.
⏱️ Continuous Verification	Supports continuous identity monitoring and verification.

📋 Project Overview

• Data Collection: Collected ECG data from 6 subjects using an ADS1292R acquisition device.
• Sampling Specs: 250Hz sampling rate, 24-bit ADC precision, ~5 minutes of data per person.
• Core Technologies: Signal preprocessing + Pan-Tompkins R-peak detection + 1D-CNN deep learning classification.
• Final Result: Test accuracy reached 98.44%, meeting practical application requirements.

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✨ Highlights

🎯 High-Accuracy Identification Metric Value Test Accuracy 98.44% F1 Score 98.44% Specificity 99.68% Misclassifications 8/512	⚡ Lightweight & Efficient Feature Value Parameters ~44,000 Inference Time <10ms Model Size <1MB Real-time Verification ✅
🔬 Professional Algorithms 📈 Pan-Tompkins R-peak Detection 🌊 db6 wavelet adaptive denoising ❤️ HRV analysis (time-domain / frequency-domain / nonlinear) 🎵 MFCC frequency-domain feature extraction	🤖 AI-Powered Analysis 🧠 DeepSeek AI for assisted analysis 📝 Smart reports with automatic generation 📊 Interactive visualization interface 🎨 Academic-style professional UI

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🖼️ Interface Preview

📊 Data Analysis

_{Visualization of signal preprocessing, R-peak detection, and heartbeat segmentation}

🎯 Identity Recognition

_{Real-time identity recognition with confidence display}

📈 Model Evaluation

_{Confusion matrix, accuracy, F1 score, and other evaluation metrics}

🤖 AI-Powered Analysis

_{DeepSeek AI-assisted report generation}

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📊 Performance Metrics

🎯 Metric	📈 Value	🎯 Metric	📈 Value
Test Accuracy	98.44%	Validation Accuracy	99.22%
Precision	98.45%	Sensitivity/Recall	98.44%
F1 Score	98.44%	Specificity	99.68%

📋 Confusion Matrix

	A	B	C	D	E	F	Recall
A	72	0	1	0	0	0	98.6%
B	0	104	0	0	0	1	99.0%
C	2	0	103	0	0	0	98.1%
D	0	0	1	78	0	1	97.5%
E	0	0	1	0	72	0	98.6%
F	0	1	0	0	0	75	98.7%

_{Total test samples: 512 | Correct classifications: 504 | Misclassifications: 8}

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🚀 Quick Start

📋 Requirements

Dependency Version Python 3.10+ PyTorch 2.0+ CUDA 11.8+ (optional)

Required Setup Description DeepSeek API AI analysis feature .env file Store API keys

📥 Installation Steps

# 1️⃣ Clone repository
git clone https://github.com/Marways7/ECG_IdentificationX.git
cd ECG_IdentificationX

# 2️⃣ Install dependencies
pip install -r requirements.txt

# 3️⃣ Configure API key
echo "DEEPSEEK_API_KEY=your_api_key_here" > .env
echo "DEEPSEEK_BASE_URL=https://api.deepseek.com/v1" >> .env

# 4️⃣ Prepare ECG data (see details below)
# Put your ECG files into `ECG_data/`

# 5️⃣ Train model
python train.py --epochs 50 --model_type lightweight

# 6️⃣ Launch web interface
streamlit run app.py

📁 How to prepare ECG data?

⚠️ Privacy Notice: ECG data is personally identifiable biometric data. To protect privacy, this repository does not include raw ECG recordings.

Place your ECG files in ECG_data/ with the following format:

Field	Description
timestamp	Timestamp
Channel 1	ECG signal (24-bit ADC raw value)
Channel 2	Respiration signal (optional)

Example file structure:

ECG_data/
├── A.csv    # Data for subject A
├── B.csv    # Data for subject B
└── ...      # More subjects

CSV file format:

timestamp,Channel 1,Channel 2,Channel 3
1767759832,128363,11421861,652
1767759832,128774,11421871,652
...

📌 How to get a DeepSeek API Key?

1. Visit DeepSeek Platform
2. Sign up / log in
3. Create an API key
4. Copy it into .env

🌐 After startup, visit http://localhost:8501

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🏗️ System Architecture

flowchart LR
    subgraph A[" 📥 Acquisition "]
        A1["ADS1292R<br/>250Hz"]
    end
    
    subgraph B[" 🔧 Preprocessing "]
        B1["Filtering"] --> B2["Denoising"]
    end
    
    subgraph C[" 📊 Features "]
        C1["R-peak Detection"]
        C2["HRV/MFCC"]
    end
    
    subgraph D[" 🧠 Model "]
        D1["1D-CNN"]
    end
    
    subgraph E[" 📤 Output "]
        E1["Identity A-F"]
    end
    
    A1 --> B1
    B2 --> C1
    C1 --> C2
    C2 --> D1
    D1 --> E1
    
    style A fill:#e3f2fd
    style B fill:#fff3e0
    style C fill:#e8f5e9
    style D fill:#f3e5f5
    style E fill:#ffebee

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📁 Project Structure

📦 ECG_IdentificationX
 ┣ 📂 ECG_data/          # Raw data (A-F, total 2558 beats)
 ┣ 📂 src/
 ┃ ┣ 📂 preprocessing/   # Signal preprocessing
 ┃ ┣ 📂 feature_extraction/  # Feature extraction
 ┃ ┣ 📂 models/          # Deep learning models
 ┃ ┗ 📂 utils/           # Utility functions
 ┣ 📂 docs/              # Technical docs
 ┣ 📄 app.py             # Web interface
 ┣ 📄 train.py           # Training script
 ┗ 📄 .env               # API config

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🔬 Core Algorithms

Signal Preprocessing

flowchart LR
    A["Raw ADC"] --> B["DC removal"] --> C["50Hz notch"] --> D["Band-pass filtering"] --> E["Wavelet denoising"] --> F["Clean ECG"]
    style A fill:#ffcdd2
    style F fill:#c8e6c9

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Pan-Tompkins R-peak Detection

flowchart LR
    A["ECG"] --> B["Band-pass 5-15Hz"] --> C["Differentiation"] --> D["Squaring"] --> E["Integration"] --> F["Thresholding"] --> G["R peaks"]
    style A fill:#e3f2fd
    style G fill:#c8e6c9

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HRV Analysis System

⏱️ Time Domain	📊 Frequency Domain	🔀 Nonlinear
SDNN	VLF	SD1/SD2
RMSSD	LF	ApEn
pNN50	HF	SampEn
pNN20	LF/HF	DFA

LightweightCNN

Input[175] → Conv(32,k7) → Conv(64,k5) → Conv(128,k3) → GAP → FC(64) → Output[6]

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📦 Data Specifications

Parameter	Value	Parameter	Value
Chip	ADS1292R	Sampling rate	250Hz
ADC	24-bit	Gain	6x
Reference voltage	2.42V	Total beats	2,558

ADC conversion formula: $V_{mV} = \frac{(ADC - \overline{ADC}) \times 2 \times 2.42 \times 1000}{6 \times 2^{24}}$

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🛠️ Tech Stack

Category	Technology
🐍 Language	Python 3.10+
🧠 Deep Learning	PyTorch 2.0+
📊 Signal Processing	SciPy, NumPy, PyWavelets
🤖 AI	DeepSeek API
🌐 Web	Streamlit, Plotly

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📖 Documentation

📄 Document	📝 Content
📘 System Overview	Architecture, data specs
📗 Signal Preprocessing	Filtering, denoising
📙 R-peak Detection	Pan-Tompkins
📕 Feature Extraction	HRV, MFCC
📓 Deep Learning	CNN architecture
📔 Training & Evaluation	Training strategy
📒 Performance Summary	Result analysis

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📄 License

This project is licensed under the MIT License

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Star History

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⭐ If this project helps you, please give it a Star!

Made with ❤️ for ECG Biometric Research

_{© 2026 ECG-ID Project | @Marways7}