3.4 Commonly used biomedical signals

Biomedical signals like ECG, EEG, and EMG offer crucial insights into our body's electrical activities. These signals, originating from the heart, brain, and muscles, have unique characteristics in terms of frequency and amplitude ranges, reflecting specific physiological processes.

Analyzing these signals involves time and frequency domain techniques. From simple amplitude measurements to complex Fourier transforms, these methods help interpret the wealth of information hidden in biomedical signals, aiding in diagnosis and monitoring of various health conditions.

Biomedical Signal Characteristics and Analysis

Characteristics of biomedical signals

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• Electrocardiogram (ECG)
  • Measures electrical activity of the heart
  • Consists of P, QRS, and T waves
    • P wave represents atrial depolarization
    • QRS complex represents ventricular depolarization
    • T wave represents ventricular repolarization
  • Typical frequency range spans 0.05-100 Hz
  • Amplitude range varies from 0.5-5 mV
• Electroencephalogram (EEG)
  • Measures electrical activity of the brain
  • Consists of various brain wave patterns
    • Delta waves (0.5-4 Hz) occur during deep sleep
    • Theta waves (4-8 Hz) indicate drowsiness or meditation
    • Alpha waves (8-13 Hz) appear during relaxation with closed eyes
    • Beta waves (13-30 Hz) signify active thinking and concentration
    • Gamma waves (30-100 Hz) relate to higher cognitive functions
  • Amplitude range falls between 10-100 μV
• Electromyogram (EMG)
  • Measures electrical activity of muscles
  • Consists of motor unit action potentials (MUAPs)
    • MUAP represents the electrical signal generated by a single motor unit
  • Frequency range extends from 20-2000 Hz
  • Amplitude range spans 50 μV-30 mV

Physiological origins of signals

• ECG
  • Origin stems from the electrical activity of cardiac muscle cells
  • Clinical significance includes:
    • Diagnosis of cardiac abnormalities (arrhythmias, ischemia, infarction)
    • Monitoring of heart rate and rhythm
• EEG
  • Origin arises from the synchronized activity of cortical neurons
  • Clinical significance involves:
    • Diagnosis of neurological disorders (epilepsy, sleep disorders, brain tumors)
    • Monitoring of anesthesia depth and brain function
• EMG
  • Origin comes from the electrical activity of skeletal muscle fibers
  • Clinical significance encompasses:
    • Diagnosis of neuromuscular disorders (myopathies, neuropathies)
    • Assessment of muscle function and fatigue

Analysis in time and frequency domains

• Time domain analysis examines
  • Amplitude
  • Duration
  • Morphology (shape) of waveforms
  • Statistical measures (mean, variance, skewness, kurtosis)
• Frequency domain analysis utilizes
  • Fourier transform
    • Decomposes signal into sinusoidal components
    • Provides frequency spectrum of the signal
  • Power spectral density (PSD)
    • Describes power distribution across frequencies
  • Spectral features (peak frequency, bandwidth, power in specific frequency bands)

Signal Processing Techniques for Biomedical Signals

Signal processing for biomedical data

• Filtering techniques
  • Remove noise and artifacts
  • Types of filters include
    1. Low-pass filter removes high-frequency components
    2. High-pass filter removes low-frequency components
    3. Band-pass filter retains components within a specific frequency range
    4. Notch filter removes a narrow frequency band
• Wavelet analysis provides
  • Multi-resolution analysis
  • Decomposes signal into time-frequency components
  • Useful for non-stationary signals and transient detection
• Adaptive filtering
  • Adjusts filter parameters based on signal characteristics
  • Useful for removing time-varying noise and interference
• Feature extraction derives
  • Time domain features (amplitude, duration, area under the curve)
  • Frequency domain features (spectral power, spectral entropy)
  • Non-linear features (fractal dimension, Lyapunov exponents)
• Pattern recognition and classification employ
  • Machine learning algorithms (support vector machines, neural networks)
  • Classify signals into different categories (normal vs abnormal, different disease states)