ECG Filtration and Normalization in MATLAB | MATLAB Digital Signal Processing

The video provides a tutorial on ECG signal filtration and normalization using MATLAB, focusing on Digital Signal Processing techniques applied to physiological data.

Key Technological Concepts and Procedures:

• Loading ECG Data:
  • The ECG data is sourced from the PhysioNet open-source dataset.
  • Data is loaded from a local file (100.dat) using MATLAB’s fopen, fread (with 16-bit integer format), and fclose functions.
  • Sampling frequency (Fs) is 360 Hz.
  • A time vector is created based on the sampling rate and signal length for plotting.
• Raw ECG Signal Visualization:
  • The raw ECG waveform is plotted for a 5-second segment to avoid clutter and improve interpretability.
• Bandpass Filtering:
  • A Butterworth bandpass filter is applied to remove noise outside the typical ECG frequency range.
  • Passband frequencies are set between 5 Hz (low cutoff) and 15 Hz (high cutoff).
  • MATLAB’s built-in butter function is used to compute filter coefficients.
  • The filter is applied using filtfilt for zero-phase filtering to avoid phase distortion.
• R-Peak Detection:
  • The filtered ECG signal is processed using MATLAB’s findpeaks function.
  • A minimum peak height threshold (e.g., 200) is set to detect R-peaks.
  • The number of detected beats is calculated and printed (e.g., ~17,000 beats).
• Normalization of ECG Signal:
  • The amplitude of the filtered ECG is normalized to a range of -1 to 1.
  • Normalization formula: Normalized = 2 × (signal - min(signal)) / (max(signal) - min(signal)) - 1
  • This standardizes the signal amplitude for easier analysis and comparison.
• Plotting Normalized Signal:
  • The normalized ECG signal is plotted for visualization.
  • The tutorial suggests using global variables to avoid repeating code for plotting multiple segments.
• Additional Notes:
  • The presenter briefly mentions simulating ECG signals using sine wave functions but does not delve into details.
  • Code snippets are made available on the presenter’s website with a link provided in the video description.
  • Viewers are encouraged to ask questions in the comments.

Summary of Tutorial Features:

• Step-by-step guide to load, filter, and normalize ECG signals in MATLAB.
• Use of PhysioNet dataset for real patient ECG data.
• Application of Butterworth bandpass filter to isolate ECG frequency components.
• Detection of R-peaks using peak-finding algorithms.
• Normalization of signal amplitude for standardized output.
• Visualization of raw, filtered, and normalized ECG signals.
• Mention of simulation techniques for synthetic ECG generation.

Main Speaker/Source:

• The tutorial is presented by an unnamed MATLAB instructor or content creator who demonstrates the MATLAB code live and explains each step in detail.