PPG (Photoplethysmography)

PPG is the most complete signal path in compressionKIT today. The toolkit includes published RVQ bundles, SPIHT/RVQ/hybrid comparison lanes, export artifacts, and a browser demo centered on this signal type.

What is PPG?

Photoplethysmography (PPG) measures blood volume changes in the microvascular bed of tissue using an optical sensor. It’s the technology behind:

• Pulse oximeters (SpO2 measurement)
• Smartwatch heart rate monitors
• Wearable health trackers

A PPG signal captures the pulsatile component of blood flow, producing a characteristic waveform with systolic peaks and diastolic troughs that repeat with each heartbeat.

Signal Characteristics

Property	Value
Typical sampling rate	50–500 Hz
Useful bandwidth	0.5–8 Hz
Default in compressionKIT	64 Hz
Default frame size	320 samples (5 seconds)
Morphology	Smooth, quasi-sinusoidal

PPG in compressionKIT

For a workflow-level view of what is supported today, see PPG Workflow. For measured 2x-32x RVQ metrics and SPIHT/RVQ/hybrid robustness tradeoffs, see PPG Models.

Data Source

The published v1 PPG goldens use the open unified PPG v1 cache built from BIDMC, BUT PPG, PPG-DaLiA, and WESAD. MESA remains a supported restricted source for custom experiments, but it is not used in the published v1 PPG goldens.

uv run python scripts/build_ppg_cache.py \
  --sources bidmc butppg ppg_dalia wesad

Preprocessing Pipeline

The PPG preprocessing pipeline uses heliaEDGE augmentation layers:

1. Random crop — Extract a frame_size window from the full segment
2. Layer normalization — Zero-mean, unit-variance per sample
3. Gaussian noise — Additive noise augmentation (training only)

from compressionkit.preprocessing.ppg import build_preprocessor, build_augmenter

preprocessor = build_preprocessor(frame_size=320, epsilon=1e-3)
augmenter = build_augmenter(noise_factor=(0.01, 0.1))

Optional Filtering

Bandpass filtering can be applied to inputs and/or targets independently:

data:
  input_filter:
    enabled: true
    low_hz: 0.5
    high_hz: 8.0
    order: 3
  target_filter:
    enabled: true
    low_hz: 0.5
    high_hz: 8.0
    order: 3

Synthetic PPG Generation

For data augmentation, compressionKIT can generate synthetic PPG signals via physioKIT:

data:
  synthetic_mix:
    enabled: true
    fraction: 0.1          # 10% synthetic data
    heart_rate_bpm: [50, 120]
    frequency_modulation: [0.1, 0.5]
    ibi_randomness: [0.02, 0.2]

Evaluation Metrics

PPG reconstruction quality is assessed using:

• MSE — Mean squared error
• PRD — Percent root-mean-square difference
• Cosine similarity — Waveform shape preservation
• Band-limited metrics — Metrics computed after bandpass filtering to 0.5–8 Hz
• physioKIT alignment — HR, RMSSD, SDNN comparison between original and reconstructed signals