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Principles of Physiological Measurement

Principles of Physiological Measurement

Principles of Physiological Measurement

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Basic Concepts & Errors - Measure Matters

  • Units: Standardized measures (e.g., SI units).
  • Accuracy: Closeness to true value.
  • Precision: Reproducibility of measurements.
  • Resolution: Smallest detectable change.
  • Sensitivity: $\frac{\Delta Output}{\Delta Input}$; ratio of change in output to change in input.
  • Linearity: Output directly proportional to input.
  • Hysteresis: Output depends on input's previous value/direction.
FeatureAccuracyPrecision
DefinitionCloseness to true valueReproducibility of readings
Affected bySystematic errorsRandom errors
Error TypeDescriptionMinimization
SystematicConsistent, directional (e.g., calibration error)Calibration, technique
RandomUnpredictable, bidirectional (e.g., noise)Averaging multiple readings

Transducers & Electrodes - Signal Starters

  • Transducers: Convert physiological signals (non-electrical) into measurable electrical signals.
    • Types & Principles:
      Transducer TypePrincipleExample Application(s)
      ResistiveChange in resistanceStrain gauge (pressure), Thermistor (temp)
      CapacitiveChange in capacitancePressure, Displacement
      Inductive (LVDT)Change in inductanceDisplacement, Flow
      PiezoelectricMechanical stress → voltageUltrasound, Arterial pulse
      PhotoelectricLight intensity → current/voltagePulse oximeter (SpO2)
  • Electrodes: Interface between body & recording device to pick up biopotentials (ECG, EEG, EMG).
    • Common Type: Silver-Silver Chloride (Ag/AgCl) - widely used for surface recording.
      • Properties: Non-polarisable, low noise, stable.
    • Other Types: Needle (EMG), Microelectrodes (intracellular).
    • Ideal properties: Low impedance, biocompatibility.

⭐ Ag/AgCl electrodes are preferred for biopotential measurements as they are non-polarisable, meaning they resist changes in potential due to current flow, ensuring stable recordings.

Ag/AgCl electrode diagram and equivalent circuit

Signal Processing & Recording - Wave Wranglers

  • Objective: Convert raw physiological signals to usable data.
  • Signal Chain:
  • Amplification:
    • Boosts low-amplitude biopotentials (µV-mV).
    • Uses differential amplifier (high gain & input Z, low output Z).
    • Gain: $A = V_{out} / V_{in}$.
    • CMRR: $CMRR = A_d / A_c$. Higher is better.

    ⭐ High Common Mode Rejection Ratio (CMRR) is crucial in bio-amplifiers for rejecting common-mode noise (e.g., power line interference), ensuring signal clarity.

  • Filtering: Removes noise/artifacts.
    • Low-pass: Below cutoff (ECG: < 150 Hz).
    • High-pass: Above cutoff (ECG: > 0.05 Hz; EEG: > 0.5 Hz).
    • Band-pass: Specific range (EMG: 10-1000 Hz).
    • Notch: Specific freq. (50/60 Hz line noise).
  • Analog-to-Digital Conversion (ADC):
    • Sampling (Nyquist: rate > $2 \times f_{max}$).
    • Quantization (amplitude levels).
  • Artifacts: Movement, EMG, line noise. Mitigate: Shielding (metal enclosures), proper grounding, patient stillness.

Biopotential measurement system block diagram

Data Interpretation & Validity - Trust The Test

  • Accuracy: Overall correctness. Formula: $ (TP+TN) / (TP+FP+FN+TN) $.
  • Precision (Reliability): Consistency/reproducibility of results on repeat testing.

2x2 table for diagnostic test accuracy metrics

Key diagnostic test metrics (TP=True Positive, FP=False Positive, FN=False Negative, TN=True Negative):

MetricDefinitionFormulaMnemonic
SensitivityCorrectly IDs patients with disease$TP / (TP + FN)$📌 SNOUT
SpecificityCorrectly IDs patients without disease$TN / (TN + FP)$📌 SPIN
PPVProb. of disease if test +ve$TP / (TP + FP)$
NPVProb. of no disease if test -ve$TN / (TN + FN)$

Predictive values are calculated using:

High‑Yield Points - ⚡ Biggest Takeaways

  • Validity reflects accuracy (true value); Reliability reflects precision (reproducibility).
  • Sensitivity (True Positive Rate) detects disease; Specificity (True Negative Rate) confirms absence.
  • PPV & NPV are critically dependent on disease prevalence.
  • Bias (systematic error) impacts accuracy; random error impacts precision.
  • Blinding (e.g., double-blind) is crucial to reduce observer bias.
  • Instrument calibration is essential for maintaining measurement accuracy.
  • Standard Deviation (SD) measures data spread; SEM indicates mean's precision.

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