This repository contains signal processing analyses implemented in Python using Jupyter Notebook. The project focuses on electrocardiogram (ECG) signal processing and audio/sound signal analysis, applying fundamental Digital Signal Processing (DSP) techniques.
- Python 3
- Jupyter Notebook / JupyterLab
- Python libraries: NumPy, SciPy, Pandas, Matplotlib, Seaborn
This project demonstrates practical applications of digital signal processing, including:
- Signal visualization in the time domain
- Frequency domain analysis using Fourier Transform
- Signal filtering and noise reduction
- Spectrogram generation
- Processing biomedical signals (ECG)
- Audio signal analysis and transformation
The goal is to understand how raw signals can be transformed into meaningful information using computational methods.
The ECG notebook includes:
- Loading and visualizing ECG recordings
- Noise filtering using digital filters
- Peak detection
- Heart rate calculation
- Signal smoothing and analysis
This part focuses on biomedical signal interpretation and practical filtering techniques.
The audio notebook demonstrates:
- Loading audio files (e.g., WAV format)
- Waveform visualization
- Spectrogram generation
- Fourier Transform (FFT)
- Frequency spectrum analysis
- Time-frequency signal analysis