hypnofunk 🌙

In next release (June 2026)

1. Use edf reader instead of heavy mne to read edf annotations
2. Better handling of transitions.py function to report sleep compactness correctly even when say N1 stage is not present
3. Better documentation for onset (for example R onset is calculated from onset of a sleep stage and not from the start of the hypnogram)
4. Improve annotation reading and autofixes when there are non sleep annotations like movements, ?s, other labels like leg movement or apnea

hypnofunk is a high-performance toolkit for sleep researchers. It calculates 40+ macrostructure parameters, performs first-order Markov-chain transition analysis, and detects sleep cycles—all from simple hypnogram sequences.

Installation

# Core package
pip install hypnofunk

# Full installation — includes Lempel-Ziv complexity, plotting, and EDF support
pip install hypnofunk[full]

Supported Input Formats

Hypnogram data (in-memory)

hypnofunk accepts standard AASM sleep stage labels (W, N1, N2, N3, R) as:

• Python lists, NumPy arrays, or Pandas Series.

File formats (via example workflow)

The included polyman_analysis.py provides a turnkey solution for:

• EDF / EDF+: Reads Polyman-style annotations directly.
• CSV: Processes exported spreadsheets with epoch-by-epoch scoring.

Standard Analysis Parameters

hypnofunk uses industry-standard defaults, all of which are configurable via function arguments:

Sleep Cycle Detection Logic

Our detection algorithms follow standard clinical research criteria to ensure consistency across datasets:

NREM Cycles 🌙

A sequence is identified as a NREM cycle if:

1. It starts with N2 sleep.
2. It contains at least 15 minutes (30 epochs) of continuous NREM (N1, N2, or N3).
3. This prevents short "transitional" light sleep from being miscounted as a full cycle.

REM Cycles ⚡

REM detection handles the unique nature of early-night sleep:

1. First REM Cycle: Accepted at any length (standard research practice).
2. Subsequent REM Cycles: Must be at least 5 minutes (10 epochs) long.
3. This ensures that REM "fragments" commonly found in fragmented sleep don't artificially inflate cycle counts.

Markov-Chain Transition Analysis 🔄

hypnofunk provides a robust framework for quantifying sleep stability and fragmentation using first-order Markov chains:

• Full Transition Matrix: A 5×5 matrix of probabilities for transitions between every sleep stage (W, N1, N2, N3, R).
• Stage Persistence: The probability of remaining in a specific stage (diagonal nodes of the Markov chain).
• Awakening Probabilities: The specific likelihood of transitioning to Wake from each individual sleep stage.
• Sleep Compactness: A global consolidation index calculated as the mean persistence across all sleep stages.
• Fragility Metrics: Proportion of all transitions that result in awakening.

Quick Start

from hypnofunk import hypnoman, analyze_transitions

# 10 epochs Wake, 50 N2, 30 N3, 20 REM, 5 Wake
hypnogram = ["W"]*10 + ["N2"]*50 + ["N3"]*30 + ["R"]*20 + ["W"]*5

# Get 40+ parameters in one line (Macrostructure)
params = hypnoman(hypnogram, epoch_duration=30)
print(f"TST: {params['TST'].values[0]:.1f} min | SE: {params['Sleep_efficiency'].values[0]:.1f}%")

# Analyze stage transitions & Markov chain dynamics
trans = analyze_transitions(hypnogram)
print(f"Sleep Compactness: {trans['Sleep_Compactness'].values[0]:.3f}")
print(f"Prob. N2 Persistence: {trans['Persistence_N2'].values[0]:.3f}")

Core Functionality

Sleep Macrostructure — hypnoman()

Returns a single-row pd.DataFrame containing:

• Time metrics: TRT, TST, SPT, WASO, SOL.
• Efficiency: Sleep Efficiency (SE), Sleep Maintenance Efficiency (SME).
• Stage statistics: Duration, percentage, and onset latency for all stages.
• Streak analysis: Longest, mean, and median "runs" (streaks) for every stage.
• Information Theory: Lempel-Ziv complexity (LZc) — a non-linear measure of sleep stage variety (requires antropy).

Transition Analysis — analyze_transitions()

Performs the Markov-chain analysis described above, returning:

• Total transitions (fragmentation count).
• Probability of awakening.
• Sleep compactness index.
• Per-stage persistence and awakening probabilities.
• Complete transition matrix (25 probability values).

API Reference

hypnofunk.io

• read_edf_hypnogram(): Standardized loader for Polyman EDF and EDF+ files.

hypnofunk.core

• hypnoman(): The main entry point for macrostructure metrics.
• find_nremstretches() & find_rem_stretches(): Cycle detection engines.
• trim_terminal_wake(): Utility to clean extended wake at the end of recordings.

hypnofunk.transitions

• analyze_transitions(): Main entry point for fragmentation and Markov metrics.
• compute_transition_matrix(): Raw transition probability calculations.
• compute_sleep_compactness(): Statistical consolidated sleep index.

hypnofunk.visualization

• plot_hypnogram_with_cycles(): Clean hypnograms with cycle-overlay bars.
• plot_transition_matrix(): Heatmap visualization of stage dynamics (Markov matrix).

Citation

@software{hypnofunk2026,
  author = {Venugopal, Rahul},
  title  = {hypnofunk: A Python package for sleep analysis},
  year   = {2026},
  url    = {https://github.com/rahulvenugopal/hypnofunk}
}

License

MIT — see LICENSE for details. Developed by Rahul Venugopal.