mrpheus

Raw physiological signal analysis for biological rhythms research

R Package
Sleep
Neuroimaging
Open Source
Ingests and processes multi-modal physiological recordings — polysomnography (EDF/EDF+), MRI-concurrent physiological logs, and EEG — extracting cardiac, respiratory, and neural rhythm features, including automatic AASM sleep staging.
Published

July 22, 2026

About

mrpheus is the raw signal layer of the Circadia Lab R ecosystem. It ingests and processes multi-modal physiological recordings — polysomnography (EDF/EDF+), MRI-concurrent physiological logs (Philips PMU), and EEG — and extracts features across three biological rhythm domains: cardiac (QRS detection, HRV), respiratory (apnoea detection, respiratory metrics), and neural (sleep spindles, slow oscillations, automatic AASM sleep staging).

Staged hypnograms and derived metrics pass downstream to hypnoR and syncR for cross-modal linking.

The name is spelled as Morpheus but carries a silent m, pronounced as Orpheus — carrying both myths at once. Morpheus, god of dreams, gives the package its subject; Orpheus, who descended into the underworld to navigate the unconscious, gives it its spirit.

Features

  • EDF/EDF+ ingestion — read_edf(), prepare_psg(): channel inventory, epoch segmentation, bad-channel flagging
  • Preprocessing pipeline — preprocess_psg(): channel renaming, DC removal, powerline notch, per-channel-type bandpass filtering
  • EOG artefact correction — regression-based and ICA-based
  • Spectral analysis — band power, temporal band power, spectrograms (YASA-validated Welch PSD)
  • Sleep event detection — spindles and slow oscillations
  • Automatic AASM staging — stage_epochs(): pre-trained LightGBM model ported from YASA (Vallat & Walker, 2021), 77.8% epoch accuracy on Sleep-EDF Cassette
  • Respiratory metrics — apnoea detection, AHI, ODI
  • Cardiac rhythm — QRS detection (Pan-Tompkins), heart rate signal, HRV during sleep
  • Philips PMU ingestion — read_philips_physlog(), with event markers and scan-window alignment
  • Ecosystem handoffs — export_hypnogram() feeds directly into hypnoR

Workflow

library(mrpheus)

rec <- read_edf("psg_001.edf")
psg <- prepare_psg(rec)
psg <- preprocess_psg(psg)
psg <- correct_eog_regression(psg)

stages <- stage_epochs(psg)
bp     <- compute_band_power(psg, relative = TRUE)
sp     <- compute_spindles(psg)

hypnogram <- export_hypnogram(stages, start_time = rec$header$startTime)