🫀 ECG Signal Filtering Project

📕 Table of Contents

• 📋 Project Overview
• 🎯 Project Objectives
• 🛠️ Technical Implementation
• 🚀 Getting Started
• 📋 Menu Options
• 📁 Project Structure
• 🔬 Signal Processing Theory
• 📊 Results & Analysis

📋 Project Overview

This project implements digital signal processing techniques to filter real electrocardiographic (ECG) signals for improved readability and automatic processing. The system applies high-pass and low-pass filters to remove noise and artifacts from ECG recordings, making them suitable for subsequent peak detection and classification tasks.

👥 Team Members

• Osaid Nur - 1210733
• Salah Sami - 1210722
• Waleed Rimawi - 1211491

🎯 Project Objectives

The main goals of this project are to:

1. Data Visualization: Display and analyze real ECG signals sampled at 360 Hz
2. Signal Filtering: Apply digital filters to improve signal quality
3. Filter Analysis: Analyze filter characteristics including frequency response and zero-pole plots
4. Comparative Analysis: Compare different filtering approaches and their effects

📊 Features

🔍 Signal Analysis

• Original Signal Display: Visualize raw ECG1 and ECG2 signals
• Real-time Column Integration: Time axis generation for proper signal representation
• Interactive Plotting: High-quality matplotlib visualizations with customizable parameters

🔧 Digital Filtering

• High-Pass Filter (HPF): Removes baseline drift and low-frequency noise
• Low-Pass Filter (LPF): Eliminates high-frequency artifacts and muscle noise
• Cascaded Filtering: Apply multiple filters in sequence for enhanced signal quality

📈 Filter Characterization

• Frequency Response Analysis: Visualize filter magnitude response
• Zero-Pole Plot Generation: Analyze filter stability and characteristics
• Transfer Function Computation: Mathematical representation of filter behavior

🛠️ Technical Implementation

High-Pass Filter Specifications

Transfer Function: H_HP(z) = (-1/32 + z^-16 - z^-17 + z^-32/32) / (1 - z^-1)
Type: IIR (Infinite Impulse Response)
Purpose: Baseline drift removal, DC component elimination

Low-Pass Filter Specifications

Transfer Function: H_LP(z) = (1 - 2z^-6 + z^-12) / (1 - 2z^-1 + z^-2)
Type: IIR (Infinite Impulse Response)  
Purpose: High-frequency noise suppression, anti-aliasing

🚀 Getting Started

Prerequisites

pip install numpy matplotlib pandas scipy

Required Files

• Data_ECG_raw.xlsx - Contains ECG1 and ECG2 signal data
• DSP_project.py - Main application script

Installation & Usage

1. Clone the repository:

git clone https://github.com/yourusername/Filtering-of-ECG-Signals.git
cd Filtering-of-ECG-Signals

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python DSP_project.py

📋 Menu Options

The interactive application provides the following options:

1. Display Original Signals 📊

   • View raw ECG1 and ECG2 recordings
   • Analyze signal characteristics before filtering

2. Filter Frequency Response 📈

   • Visualize HPF and LPF magnitude responses
   • Understand filter behavior in frequency domain

3. Zero-Pole Analysis 🎯

   • Plot filter zeros and poles
   • Assess filter stability and performance

4. Single Filter Application 🔧

   • Apply HPF or LPF to individual signals
   • Compare filtered vs. original signals

5. Cascaded Filter Analysis 🔄

   • Apply HPF→LPF or LPF→HPF sequences
   • Analyze order-dependent filtering effects

📁 Project Structure

Filtering-of-ECG-Signals/
├── DSP_project.py          # Main application script
├── Data_ECG_raw.xlsx       # Raw ECG signal data
├── Project Report.pdf      # Detailed technical report
├── Project_Description.pdf # Project requirements and specifications
├── README.md              # Project documentation
└── requirements.txt       # Python dependencies

🔬 Signal Processing Theory

ECG Signal Characteristics

• Sampling Rate: 360 Hz
• Signal Components:
  • P-wave, QRS complex, T-wave
  • Baseline drift (< 0.5 Hz)
  • Muscle artifacts (> 100 Hz)
  • Power line interference (50/60 Hz)

Filtering Strategy

1. High-Pass Filtering: Removes baseline wander and respiratory artifacts
2. Low-Pass Filtering: Eliminates high-frequency noise and EMG interference
3. Cascaded Approach: Combines both filters for optimal signal conditioning

📊 Results & Analysis

Filter Performance

• HPF Effectiveness: Successfully removes baseline drift while preserving ECG morphology
• LPF Characteristics: Maintains clinical ECG features while suppressing noise
• Order Independence: HPF→LPF vs. LPF→HPF comparison reveals filtering sequence effects

Signal Quality Metrics

• SNR Improvement: Quantifiable enhancement in signal-to-noise ratio
• Morphology Preservation: Clinical ECG features remain intact
• Computational Efficiency: Real-time processing capability

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