PPG (Photoplethysmography)¶

PPG is the most complete signal path in compressionKIT today. The toolkit includes a documented training flow, golden operating points, export artifacts, and a customer-ready live 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¶

PPG in compressionKIT¶

For a workflow-level view of what is supported today, see PPG Workflow. For the shipped 2×, 4×, 8×, and 16× examples, see PPG 2x-16x Examples.

Data Source¶

compressionKIT loads PPG data from EDF files (European Data Format), specifically targeting the Pleth channel commonly found in polysomnography recordings such as the MESA dataset.

from compressionkit.datasets.ppg import load_ppg_signal

signal = load_ppg_signal(
    edf_file,
    target_rate=64,
    target_label="Pleth",
    offset_samples=192,   # skip first 3 seconds
    num_samples=7680,      # 2 minutes at 64 Hz
)

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