Biomedical instrumentation and measurements are crucial in healthcare. They involve capturing, processing, and analyzing biological signals to diagnose and monitor patients. From ECGs to MRIs, these tools provide vital insights into human physiology.

This section explores the fundamentals of biosignal acquisition, advanced processing techniques, and medical imaging principles. It also covers data acquisition systems, calibration, and quality assurance, highlighting the importance of accurate and reliable measurements in biomedical engineering.

Biosignal Acquisition and Processing

Fundamentals of Biosignal Acquisition

• Biosignal acquisition involves capturing physiological signals from the human body
• Sensors and transducers convert biological phenomena into electrical signals
  • Electrodes detect electrical activity (heart, brain)
  • Pressure sensors measure blood pressure
  • Thermistors monitor body temperature
• Signal conditioning amplifies and filters raw biosignals
  • Amplification increases signal strength for easier processing
  • Filtering removes unwanted noise and interference
• Analog-to-digital conversion transforms continuous analog signals into discrete digital data
  • Sampling rate determines temporal resolution (Nyquist theorem)
  • Quantization assigns digital values to sampled amplitudes
• Noise reduction techniques improve signal quality
  • Digital filtering removes specific frequency components
  • Averaging reduces random noise in repetitive signals
  • Adaptive filtering adjusts to changing noise characteristics

Advanced Signal Processing Techniques

• Spectral analysis reveals frequency components of biosignals
  • Fast Fourier Transform (FFT) efficiently computes signal spectrum
  • Power spectral density estimates signal power across frequencies
• Time-frequency analysis examines how signal frequencies change over time
  • Short-time Fourier Transform (STFT) uses sliding window approach
  • Wavelet transform provides multi-resolution analysis
• Feature extraction identifies relevant characteristics in biosignals
  • Statistical features (mean, variance, skewness)
  • Morphological features (peak amplitude, duration)
• Machine learning algorithms classify or predict based on extracted features
  • Support Vector Machines (SVM) for binary classification
  • Neural networks for complex pattern recognition

Medical Imaging and Diagnostics

Principles of Medical Imaging

• Medical imaging creates visual representations of internal body structures
• X-ray imaging uses ionizing radiation to produce 2D projections
  • Attenuation differences create contrast between tissues
  • Digital radiography improves image quality and reduces radiation dose
• Computed Tomography (CT) generates 3D images from multiple X-ray projections
  • Hounsfield units quantify tissue density
  • Contrast agents enhance visibility of specific structures
• Magnetic Resonance Imaging (MRI) utilizes strong magnetic fields and radio waves
  • T1 and T2 relaxation times differentiate tissue types
  • Functional MRI (fMRI) measures brain activity through blood flow changes
• Ultrasound imaging employs high-frequency sound waves
  • Doppler ultrasound measures blood flow velocity
  • Elastography assesses tissue stiffness

Electrocardiography and Electroencephalography

• Electrocardiography (ECG) records electrical activity of the heart
  • Standard 12-lead ECG provides comprehensive cardiac assessment
  • QRS complex represents ventricular depolarization
  • ST segment changes indicate myocardial ischemia
• ECG signal processing enhances diagnostic capabilities
  • R-peak detection algorithms identify heart rate variability
  • Wavelet denoising improves signal-to-noise ratio
• Electroencephalography (EEG) measures brain electrical activity
  • Electrode placement follows international 10-20 system
  • Frequency bands (delta, theta, alpha, beta, gamma) correspond to different brain states
• EEG analysis techniques extract meaningful information
  • Event-related potentials (ERPs) reveal cognitive processes
  • Source localization estimates neural generators of scalp potentials

Instrumentation and Calibration

Data Acquisition Systems

• Data acquisition systems integrate hardware and software components
• Analog front-end circuitry conditions incoming biosignals
  • Instrumentation amplifiers provide high input impedance and common-mode rejection
  • Anti-aliasing filters prevent frequency folding during sampling
• Analog-to-digital converters (ADCs) digitize conditioned signals
  • Successive approximation ADCs balance speed and resolution
  • Sigma-delta ADCs offer high resolution for low-frequency signals
• Digital signal processors (DSPs) perform real-time computations
  • Fast Fourier Transform (FFT) algorithms for spectral analysis
  • Digital filtering implements various transfer functions
• Data storage and transmission protocols ensure data integrity
  • DICOM standard for medical imaging data
  • HL7 format for electronic health records

Calibration and Quality Assurance

• Calibration establishes relationship between instrument readings and known standards
• Static calibration verifies instrument accuracy at fixed points
  • Multi-point calibration improves linearity across measurement range
  • Traceability ensures calibration standards link to national references
• Dynamic calibration assesses instrument response to time-varying inputs
  • Step response characterizes system settling time and overshoot
  • Frequency response analysis reveals system bandwidth
• Quality assurance procedures maintain instrument performance over time
  • Regular preventive maintenance schedules
  • Statistical process control monitors long-term stability
• Uncertainty analysis quantifies measurement reliability
  • Type A uncertainty from statistical analysis of repeated measurements
  • Type B uncertainty from manufacturer specifications and calibration certificates