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Photoplethysmography (PPG) Analysis

scientific-skills/neurokit2/references/ppg.md

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Photoplethysmography (PPG) Analysis

Overview

Photoplethysmography (PPG) measures blood volume changes in microvascular tissue using optical sensors. PPG is widely used in wearable devices, pulse oximeters, and clinical monitors for heart rate, pulse characteristics, and cardiovascular assessment.

Main Processing Pipeline

ppg_process()

Automated PPG signal processing pipeline.

python
signals, info = nk.ppg_process(ppg_signal, sampling_rate=100, method='elgendi')

Pipeline steps:

  1. Signal cleaning (filtering)
  2. Systolic peak detection
  3. Heart rate calculation
  4. Signal quality assessment

Returns:

  • signals: DataFrame with:
    • PPG_Clean: Filtered PPG signal
    • PPG_Peaks: Systolic peak markers
    • PPG_Rate: Instantaneous heart rate (BPM)
    • PPG_Quality: Signal quality indicator
  • info: Dictionary with peak indices and parameters

Methods:

  • 'elgendi': Elgendi et al. (2013) algorithm (default, robust)
  • 'nabian2018': Nabian et al. (2018) approach

Preprocessing Functions

ppg_clean()

Prepare raw PPG signal for peak detection.

python
cleaned_ppg = nk.ppg_clean(ppg_signal, sampling_rate=100, method='elgendi')

Methods:

1. Elgendi (default):

  • Butterworth bandpass filter (0.5-8 Hz)
  • Removes baseline drift and high-frequency noise
  • Optimized for peak detection reliability

2. Nabian2018:

  • Alternative filtering approach
  • Different frequency characteristics

PPG signal characteristics:

  • Systolic peak: Rapid upstroke, sharp peak (cardiac ejection)
  • Dicrotic notch: Secondary peak (aortic valve closure)
  • Baseline: Slow drift due to respiration, movement, perfusion

ppg_peaks()

Detect systolic peaks in PPG signal.

python
peaks, info = nk.ppg_peaks(cleaned_ppg, sampling_rate=100, method='elgendi',
                           correct_artifacts=False)

Methods:

  • 'elgendi': Two moving averages with dynamic thresholding
  • 'bishop': Bishop's algorithm
  • 'nabian2018': Nabian's approach
  • 'scipy': Simple scipy peak detection

Artifact correction:

  • Set correct_artifacts=True for physiological plausibility checks
  • Removes spurious peaks based on inter-beat interval outliers

Returns:

  • Dictionary with 'PPG_Peaks' key containing peak indices

Typical inter-beat intervals:

  • Resting adult: 600-1200 ms (50-100 BPM)
  • Athlete: Can be longer (bradycardia)
  • Stressed/exercising: Shorter (<600 ms, >100 BPM)

ppg_findpeaks()

Low-level peak detection with algorithm comparison.

python
peaks_dict = nk.ppg_findpeaks(cleaned_ppg, sampling_rate=100, method='elgendi')

Use case:

  • Custom parameter tuning
  • Algorithm testing
  • Research method development

Analysis Functions

ppg_analyze()

Automatically select event-related or interval-related analysis.

python
analysis = nk.ppg_analyze(signals, sampling_rate=100)

Mode selection:

  • Duration < 10 seconds → event-related
  • Duration ≥ 10 seconds → interval-related

ppg_eventrelated()

Analyze PPG responses to discrete events/stimuli.

python
results = nk.ppg_eventrelated(epochs)

Computed metrics (per epoch):

  • PPG_Rate_Baseline: Heart rate before event
  • PPG_Rate_Min/Max: Minimum/maximum heart rate during epoch
  • Rate dynamics across epoch time windows

Use cases:

  • Cardiovascular responses to emotional stimuli
  • Cognitive load assessment
  • Stress reactivity paradigms

ppg_intervalrelated()

Analyze extended PPG recordings.

python
results = nk.ppg_intervalrelated(signals, sampling_rate=100)

Computed metrics:

  • PPG_Rate_Mean: Average heart rate
  • Heart rate variability (HRV) metrics
    • Delegates to hrv() function
    • Time, frequency, and nonlinear domains

Recording duration:

  • Minimum: 60 seconds for basic rate
  • HRV analysis: 2-5 minutes recommended

Use cases:

  • Resting state cardiovascular assessment
  • Wearable device data analysis
  • Long-term heart rate monitoring

Quality Assessment

ppg_quality()

Assess signal quality and reliability.

python
quality = nk.ppg_quality(ppg_signal, sampling_rate=100, method='averageQRS')

Methods:

1. averageQRS (default):

  • Template matching approach
  • Correlates each pulse with average template
  • Returns quality scores 0-1 per beat
  • Threshold: >0.6 = acceptable quality

2. dissimilarity:

  • Topographic dissimilarity measures
  • Detects morphological changes

Use cases:

  • Identify corrupted segments
  • Filter low-quality data before analysis
  • Validate peak detection accuracy

Common quality issues:

  • Motion artifacts: abrupt signal changes
  • Poor sensor contact: low amplitude, noise
  • Vasoconstriction: reduced signal amplitude (cold, stress)

Utility Functions

ppg_segment()

Extract individual pulses for morphological analysis.

python
pulses = nk.ppg_segment(cleaned_ppg, peaks, sampling_rate=100)

Returns:

  • Dictionary of pulse epochs, each centered on systolic peak
  • Enables pulse-to-pulse comparison
  • Morphology analysis across conditions

Use cases:

  • Pulse wave analysis
  • Arterial stiffness proxies
  • Vascular aging assessment

ppg_methods()

Document preprocessing methods used in analysis.

python
methods_info = nk.ppg_methods(method='elgendi')

Returns:

  • String documenting the processing pipeline
  • Useful for methods sections in publications

Simulation and Visualization

ppg_simulate()

Generate synthetic PPG signals for testing.

python
synthetic_ppg = nk.ppg_simulate(duration=60, sampling_rate=100, heart_rate=70,
                                noise=0.1, random_state=42)

Parameters:

  • heart_rate: Mean BPM (default: 70)
  • heart_rate_std: HRV magnitude
  • noise: Gaussian noise level
  • random_state: Reproducibility seed

Use cases:

  • Algorithm validation
  • Parameter optimization
  • Educational demonstrations

ppg_plot()

Visualize processed PPG signal.

python
nk.ppg_plot(signals, info, static=True)

Displays:

  • Raw and cleaned PPG signal
  • Detected systolic peaks
  • Instantaneous heart rate trace
  • Signal quality indicators

Practical Considerations

Sampling Rate Recommendations

  • Minimum: 20 Hz (basic heart rate)
  • Standard: 50-100 Hz (most wearables)
  • High-resolution: 200-500 Hz (research, pulse wave analysis)
  • Excessive: >1000 Hz (unnecessary for PPG)

Recording Duration

  • Heart rate: ≥10 seconds (few beats)
  • HRV analysis: 2-5 minutes minimum
  • Long-term monitoring: Hours to days (wearables)

Sensor Placement

Common sites:

  • Fingertip: Highest signal quality, most common
  • Earlobe: Less motion artifact, clinical use
  • Wrist: Wearable devices (smartwatches)
  • Forehead: Reflectance mode, medical monitoring

Transmittance vs. Reflectance:

  • Transmittance: Light passes through tissue (fingertip, earlobe)
    • Higher signal quality
    • Less motion artifact
  • Reflectance: Light reflected from tissue (wrist, forehead)
    • More susceptible to noise
    • Convenient for wearables

Common Issues and Solutions

Low signal amplitude:

  • Poor perfusion: warm hands, increase blood flow
  • Sensor contact: adjust placement, clean skin
  • Vasoconstriction: environmental temperature, stress

Motion artifacts:

  • Dominant issue in wearables
  • Adaptive filtering, accelerometer-based correction
  • Template matching, outlier rejection

Baseline drift:

  • Respiratory modulation (normal)
  • Movement or pressure changes
  • High-pass filtering or detrending

Missing peaks:

  • Low-quality signal: check sensor contact
  • Algorithm parameters: adjust threshold
  • Try alternative detection methods

Best Practices

Standard workflow:

python
# 1. Clean signal
cleaned = nk.ppg_clean(ppg_raw, sampling_rate=100, method='elgendi')

# 2. Detect peaks with artifact correction
peaks, info = nk.ppg_peaks(cleaned, sampling_rate=100, correct_artifacts=True)

# 3. Assess quality
quality = nk.ppg_quality(cleaned, sampling_rate=100)

# 4. Comprehensive processing (alternative)
signals, info = nk.ppg_process(ppg_raw, sampling_rate=100)

# 5. Analyze
analysis = nk.ppg_analyze(signals, sampling_rate=100)

HRV from PPG:

python
# Process PPG signal
signals, info = nk.ppg_process(ppg_raw, sampling_rate=100)

# Extract peaks and compute HRV
hrv_indices = nk.hrv(info['PPG_Peaks'], sampling_rate=100)

# PPG-derived HRV is valid but may differ slightly from ECG-derived HRV
# Differences due to pulse arrival time, vascular properties

Clinical and Research Applications

Wearable health monitoring:

  • Consumer smartwatches and fitness trackers
  • Continuous heart rate monitoring
  • Sleep tracking and activity assessment

Clinical monitoring:

  • Pulse oximetry (SpO₂ + heart rate)
  • Perioperative monitoring
  • Critical care heart rate assessment

Cardiovascular assessment:

  • Pulse wave analysis
  • Arterial stiffness proxies (pulse arrival time)
  • Vascular aging indices

Autonomic function:

  • HRV from PPG (PPG-HRV)
  • Stress and recovery monitoring
  • Mental workload assessment

Remote patient monitoring:

  • Telemedicine applications
  • Home-based health tracking
  • Chronic disease management

Affective computing:

  • Emotion recognition from physiological signals
  • User experience research
  • Human-computer interaction

PPG vs. ECG

Advantages of PPG:

  • Non-invasive, no electrodes
  • Convenient for long-term monitoring
  • Low cost, miniaturizable
  • Suitable for wearables

Disadvantages of PPG:

  • More susceptible to motion artifacts
  • Lower signal quality in poor perfusion
  • Pulse arrival time delay from heart
  • Cannot assess cardiac electrical activity

HRV comparison:

  • PPG-HRV generally valid for time/frequency domains
  • May differ slightly due to pulse transit time variability
  • ECG preferred for clinical HRV when available
  • PPG acceptable for research and consumer applications

Interpretation Guidelines

Heart rate from PPG:

  • Same interpretation as ECG-derived heart rate
  • Slight delay (pulse arrival time) is negligible for rate calculation
  • Motion artifacts more common: validate with signal quality

Pulse amplitude:

  • Reflects peripheral perfusion
  • Increases: vasodilation, warmth
  • Decreases: vasoconstriction, cold, stress, poor contact

Pulse morphology:

  • Systolic peak: Cardiac ejection
  • Dicrotic notch: Aortic valve closure, arterial compliance
  • Aging/stiffness: Earlier, more prominent dicrotic notch

References

  • Elgendi, M. (2012). On the analysis of fingertip photoplethysmogram signals. Current cardiology reviews, 8(1), 14-25.
  • Elgendi, M., Norton, I., Brearley, M., Abbott, D., & Schuurmans, D. (2013). Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PloS one, 8(10), e76585.
  • Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological measurement, 28(3), R1.
  • Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014). Wearable photoplethysmographic sensors—past and present. Electronics, 3(2), 282-302.