ECG Heartbeat Detection Project

Overview

Hello, fellow developers! I'm Krishanu Dev Sarma, and this repository houses my project for the study and implementation of a heartbeat detection technique from ECG signals using MATLAB. The primary focus is on signal processing techniques, including noise removal through low-pass and high-pass filters and R-peak detection.

Project Structure

1. Data: The database folder contains sample ECG signals for testing.
2. Code: MATLAB scripts for signal processing and R-peak detection.
   • data_plot.m: Loads the data and plots it in Matlab.
   • Low_pass_filter.m: Implements noise removal through low-pass filter.
   • High_pass_filter.m: Implements noise removal through high-pass filter.
   • R_peaks.m: Utilizes MATLAB for R-peak detection.
   • Calculation_of_heart_rate.m: Calculates the heart rates from the R-peaks.

Technical Details

Types of Noises in ECG Signal

1. Baseline Wander: Low-frequency variations caused by body movements.
2. Powerline Interference: High-frequency noise from power sources.
3. Muscle Artifacts: High-frequency noise caused by muscle movements.
4. Electrode Motion Artifacts: Abrupt changes due to electrode displacement.

Noise Removal Technique

1. Low-Pass Filter: Attenuates high-frequency noise, preserving the slower-changing ECG signal.

   % Example code for low-pass filtering
   ecg_signal_lowpass = lowpass(ecg_signal, cutoff_frequency, sampling_frequency);

2. High-Pass Filter: Eliminates baseline wander and low-frequency noise.

   % Example code for high-pass filtering
   ecg_signal_highpass = highpass(ecg_signal, cutoff_frequency, sampling_frequency);

R-Peak Detection Using MATLAB

MATLAB provides robust tools for peak detection. The findpeaks function is particularly useful.

% Example code for R-peak detection
[peaks, locations] = findpeaks(ecg_signal, 'MinPeakHeight', threshold);

Pros and Cons of MATLAB

Pros:

1. Rich Signal Processing Toolbox: MATLAB's extensive toolbox simplifies signal processing tasks.
2. Community Support: A large and active MATLAB community provides ample resources and solutions.
3. Versatility: MATLAB is well-suited for prototyping and research in signal processing applications.

Cons:

1. Cost: MATLAB is a commercial software, and licensing costs can be a factor for some users.
2. Learning Curve: For those unfamiliar with MATLAB, there might be an initial learning curve.
3. Limited Parallel Processing: MATLAB's parallel processing capabilities are limited compared to some other languages.

Advice for Developers

1. Understand ECG Signals: Gain a solid understanding of ECG signals and the nature of different types of noise.
2. Optimize Filtering Parameters: Fine-tune filter parameters based on your specific signal characteristics.
3. Explore MATLAB Documentation: MATLAB's documentation is a goldmine. Explore it for in-depth insights.

Conclusion

This project aims not only to detect heartbeats but to provide a learning platform for signal processing enthusiasts. Feel free to explore, experiment, and contribute. Let's keep the heartbeat of open source pulsating!

Happy coding! 🚀💓

Note: This readme page provides a high-level overview; specific technical details, code snippets, and results would be integrated into a comprehensive blog post for publication.