Resources

Various resources such as publications, online references, and documentation to support our research and development efforts are mentioned here.

Our PCG Signal Analysis project aims to improve the quality of heart sound recordings using advanced digital signal processing techniques. By applying adaptive filtering algorithms, we're working to reduce noise and enhance the clarity of phonocardiogram signals.

About the Project
PCG Signal Analysis with Adaptive Filtering
Image by Thierry Fousse

Tools & Libraries

Comprehensive overview of the technologies, tools, and resources used in this PCG Signal Analysis project, organized by purpose, technology stack, and workflow stage.

Organized by Purpose

Signal Processing & Analysis

MATLAB logo
MATLAB

Primary tool for PCG signal processing and adaptive filtering implementation. Used for audio processing, signal analysis, and algorithm development.

Signal Processing ToolboxDSP System Toolbox
NumPy

Fundamental Python library for numerical computing. Used in combination with MATLAB for array processing and mathematical operations on PCG signals.

SciPy

Scientific computing library for Python. Provides additional signal processing capabilities and statistical analysis functions for PCG data.

Data Visualization

Matplotlib

Python plotting library used for creating visualizations of PCG signals, filter responses, and analysis results.

Recharts
Website

React charting library used for interactive data visualizations on this website.

Web Development

Website Only
Next.js
Website

React framework used for building this website. Provides server-side rendering and optimal performance.

Tailwind CSS
Website

Utility-first CSS framework used for styling this website.

Radix UI
Website

Accessible component library providing the UI primitives for this website.

Research & Academic Resources

IEEE Xplore logo
IEEE Xplore

Digital library providing access to IEEE journals and publications. Primary source for research papers on signal processing and biomedical engineering.

IEEE Signal Processing Magazine

Academic journal providing cutting-edge research and techniques in signal processing applications.

SRMIST Resources

SRM Institute of Science and Technology provides institutional support including library access, research databases, laboratory equipment, and faculty guidance for this project.

  • •Digital library access
  • •Research paper databases
  • •Laboratory equipment and facilities
  • •Faculty guidance and mentorship
  • •Institutional research support

Note: Tools marked as “Website Only” are used exclusively for building this documentation website and are not part of the core PCG signal processing pipeline.

Organized by Technology Stack

Organized by Workflow Stage

PhysioNet Dataset

Source of PCG recordings from PhysioNet/CinC Challenge 2016 dataset.

MATLAB Audio I/O

Functions for reading and importing PCG audio files in various formats.

Institutional & Academic Support

SRM Institute of Science and Technology

SRMIST provides comprehensive institutional support for this research project, including access to academic resources, laboratory facilities, and faculty mentorship.

  • ✓Digital library and research databases
  • ✓IEEE and academic journal access
  • ✓Signal processing laboratory equipment
  • ✓Faculty guidance and technical mentorship
  • ✓Research infrastructure and support
Visit SRMIST Website
IEEE logo

IEEE Research Resources

IEEE provides access to cutting-edge research papers, journals, and publications in signal processing and biomedical engineering.

IEEE Xplore

Comprehensive digital library for technical literature in engineering and technology.

Visit IEEE Xplore
IEEE Signal Processing Magazine

Leading publication for signal processing research, techniques, and applications.

View Magazine

Dataset

PhysioNet/CinC Challenge 2016

This project uses the PhysioNet/CinC Challenge 2016 Dataset for PCG signal analysis. The dataset contains phonocardiogram recordings from multiple clinical locations, providing a diverse set of heart sound samples for testing and validation.

The dataset includes normal and abnormal heart sound recordings, making it suitable for developing and evaluating adaptive filtering techniques for cardiac assessment.

View Dataset