ECG Denoising Using Singular Value Decomposition (SVD)

This repository implements ECG signal denoising using Singular Value Decomposition (SVD). The experiment uses clean ECG signals from MIT-BIH Arrhythmia Database (mitdb) and baseline wander / muscle / electrode motion noise from the Noise Stress Test Database (nstdb).

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Key Features

• Uses real ECG and noise data from PhysioNet
• Sliding window (trajectory matrix) construction
• Automatic rank selection based on energy preservation
• Reconstruction via diagonal averaging
• Fully reproducible and configurable via command line

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Data Sources (PhysioNet)

• Clean ECG: MIT-BIH Arrhythmia Database
  https://physionet.org/content/mitdb/

• Noise: Noise Stress Test Database (NSTDB)
  https://physionet.org/content/nstdb/1.0.0/

No manual download required — data is fetched automatically using wfdb.

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Experimental Setup

Parameter	Value
Sampling rate (fs)	360 Hz
Channels	2 (only one used)
Signal duration	First 10 seconds
Noise type	BW / EM / MA
Amplitude unit	Normalized (zero mean, unit std)

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Installation

Clone the repository and install dependencies:

git clone https://github.com/gabriellaalubis/SVD-Denoising-for-ECG.git
cd src
pip install -r requirements.txt
python denoise.py