ECG Signal Processing & Cardiac Feature Extraction

Version 1.0.0 • Stable Release

A MATLAB-based biomedical signal processing project that performs automatic ECG analysis using the MIT-BIH Arrhythmia Database. The system preprocesses ECG recordings, detects cardiac waves, extracts clinically significant cardiac features, performs frequency-domain analysis, identifies abnormal cardiac events, and automatically generates professional reports.

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Features

• ECG Signal Loading

• ECG Preprocessing & Filtering

• Automatic P, Q, R, S & T Wave Detection

• Heart Rate Analysis

• RR Interval Analysis

• Heart Rate Variability (HRV)

  • SDNN
  • RMSSD

• Cardiac Interval Measurement

  • PR Interval
  • QRS Duration
  • QT Interval
  • Corrected QT (QTc)

• FFT Analysis

• Power Spectral Density (PSD)

• Clinical Alert Detection

  • Bradycardia
  • Tachycardia
  • Long QT

• Automatic CSV Feature Table Generation

• Automatic Professional PDF Report Generation

• Timestamped Result Management

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Repository Structure

ECG Signal Processing & Cardiac Feature Extraction
│
├── 01 Documentation
│
├── 02 Datasets
│   ├── MIT-BIH Arrhythmia Database
│   └── README.md
│
├── 03 MATLAB
│   ├── src
│   └── functions
│
├── 04 Results
│   ├── ECG Plot
│   ├── FFT
│   ├── FFT Comparison
│   ├── Heart Rate
│   ├── PSD
│   ├── Reports
│   ├── RR Interval
│   └── Tables
│
├── LICENSE
└── README.md

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Quick Start

1. Clone this repository.

2. Follow the instructions inside:

02 Datasets/README.md

to download the MIT-BIH Arrhythmia Database.

3. Extract the ECG files into

02 Datasets/MIT-BIH Arrhythmia Database

4. Open MATLAB.

5. Open

03 MATLAB/src/main.m

6. Run

main

7. The application will automatically generate:

• ECG Plot
• FFT Spectrum
• FFT Comparison
• PSD Plot
• Heart Rate Trend
• RR Interval Plot
• CSV Feature Table
• PDF Analysis Report

All outputs are saved inside the 04 Results directory.

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Dataset

This project uses the MIT-BIH Arrhythmia Database.

The dataset is not included in this repository.

Please refer to:

02 Datasets/README.md

for download instructions and the required folder structure.

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Processing Pipeline

MIT-BIH ECG Record
        │
        ▼
Load ECG Signal
        │
        ▼
Preprocessing & Filtering
        │
        ▼
R-Peak Detection
        │
        ▼
P-Q-R-S-T Detection
        │
        ▼
Feature Extraction
        │
        ├── Heart Rate
        ├── RR Interval
        ├── HRV
        ├── PR Interval
        ├── QT Interval
        ├── QTc
        ├── QRS Width
        ├── ST Segment
        └── T Wave Amplitude
        │
        ▼
Frequency Domain Analysis
        ├── FFT
        └── PSD
        │
        ▼
Clinical Alert Detection
        │
        ▼
CSV Export
        │
        ▼
Professional PDF Report

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📊 Sample Outputs

ECG Plot	Heart Rate
Figure 1. ECG Signal with Detected PQRST Complexes	Figure 2. Heart Rate Trend

RR Interval	FFT
Figure 3. RR Interval Analysis	Figure 4. Fast Fourier Transform

FFT Comparison	PSD
Figure 5. FFT Comparison	Figure 6. Power Spectral Density

Generated PDF Report

Figure 7. Automatically Generated Professional PDF Analysis Report

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Technologies Used

• MATLAB R2025b
• MATLAB Signal Processing Toolbox
• MATLAB Report Generator
• MIT-BIH Arrhythmia Database
• PhysioNet

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Current Release

Version 1.0.0

Status:

• Stable
• Fully Functional
• Validated
• Ready for Academic and Research Use

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Future Improvements

• Machine Learning Based Arrhythmia Classification
• Real-Time ECG Processing
• Embedded System Implementation (STM32)
• Live Monitoring Dashboard
• Batch ECG Processing
• Automatic Diagnostic Recommendation

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References

Moody GB, Mark RG.

The Impact of the MIT-BIH Arrhythmia Database.

IEEE Engineering in Medicine and Biology Magazine, 20(3):45–50, 2001.

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al.

PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.

Circulation, 101(23):e215–e220, 2000.

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License

This project is released under the MIT License.

The MIT-BIH Arrhythmia Database remains the property of its respective authors and is distributed through PhysioNet under its own licensing terms.