preprocessing_physics

🫀 ECG Wave Segmentation with Physics-Informed Preprocessing

This repository explores multiple physics-based preprocessing techniques—Euler Differentiation, Hilbert Transform, and Gauss-Legendre Integration—to enhance the segmentation of ECG waveforms (P, QRS, and T) using an LSTM model.

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🔬 Overview

Electrocardiogram (ECG) signals contain three primary waveforms:

• P-wave: low-frequency, low-amplitude
• QRS-complex: sharp, high-frequency
• T-wave: smooth, moderate amplitude

Each wave is preprocessed using a tailored mathematical method to match its physical characteristics before being segmented using an LSTM model.

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⚙️ Preprocessing Methods

Method	Description	Best for
Euler Differentiation	Highlights sharp slope changes	QRS-complex
Hilbert Transform	Enhances amplitude & phase info (envelope)	P & T waves
Gauss-Legendre	Smooth integration, retains morphology	All segments
High-pass Filtering	Removes baseline drift	All

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🧪 Model & Training

• Architecture: 2-layer LSTM
• Input: Preprocessed 1D ECG signal
• Output: Predicted wave labels (P, QRS, T)
• Evaluation Metrics: Accuracy, Test Loss, Inference Time

Figure: Loss convergence across preprocessing techniques

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🕒 Runtime Comparison

Method	CPU Time (ms)	GPU Memory (MB)
No Preprocessing	40.2 ms	110 MB
Euler	41.5 ms	112 MB
Hilbert	43.0 ms	115 MB
Gauss-Legendre	45.0 ms	120 MB

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📁 File Guide

File	Description
src/preprocessing.py	All signal transformation methods
src/model_lstm.py	LSTM-based segmentation model
main.py	Run complete pipeline (preprocess → segment → plot)

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📦 Installation

git clone https://github.com/username/ecg-segmentation-preprocessing.git
cd ecg-segmentation-preprocessing
pip install -r requirements.txt