sound-sleep 0.6

SoundSleep is a Python package for extracting comprehensive sleep features from wearable or sleep stage-labeled data.

SoundSleep 😴

SoundSleep is a Python package for extracting comprehensive sleep features from wearable or sleep stage-labeled data. It enables detailed analysis of sleep architecture, continuity, timing, and efficiency across nights and individuals. Designed for researchers, developers, and health data enthusiasts, it simplifies feature extraction from raw segmented data.

Installation

You can install directly from PyPI:

pip install sound-sleep

Quickstart

from sound_sleep import (
    SleepFeaturesExtractor,
    load_csv_file,
    build_dict_all_day_level_features,
    build_dict_day_level_features,
    build_dict_all_participant_level_features,
    build_dict_participant_level_features,
)
from sound_sleep.io.exports import save_feature_dicts_to_csv

# Load your data
df = load_csv_file("path/to/your/sleep_data.csv")

# Initialize the feature extractor
extractor = SleepFeaturesExtractor(df, verbose=True)

# Extract day-level features for all participants
all_day_level = build_dict_all_day_level_features(extractor)
save_feature_dicts_to_csv(all_day_level, "all_day_level_features.csv")

# Extract day-level features for one participant
participant_day_level = build_dict_day_level_features(extractor, participant_id=101)
save_feature_dicts_to_csv(participant_day_level, "participant_101_day_features.csv")

# Extract participant-level summary features for all participants
all_participant_level = build_dict_all_participant_level_features(extractor)
save_feature_dicts_to_csv(all_participant_level, "all_participant_level_features.csv")

# Extract participant-level summary for one participant
participant_summary = build_dict_participant_level_features(extractor, participant_id=101)
save_feature_dicts_to_csv([participant_summary], "participant_101_summary.csv")

See the "CSV format" section below for instructions on how your data must be formatted.

Features Extracted

Sleep Duration and Timing

• Bedtime: Clock time of going to bed.
• Risetime: Clock time of waking up.
• Time in Bed (TIB): Duration between Bedtime and Risetime.
• Sleep Onset Time: First time falling asleep.
• Wake Time: Final time waking up from sleep.
• Total Sleep Time (TST): Total time asleep (excluding awakenings).
• Midpoint of Sleep: Midpoint between sleep onset and wake.
• Sleep Onset Latency (SOL): Duration between Bedtime and Sleep Onset.
• Time Between Wake Time and Risetime: Duration spent awake in bed before actually getting up.

Sleep Architecture

• Time in Light Sleep
• Time in Deep Sleep
• Time in REM Sleep
• % Light Sleep
• % Deep Sleep
• % REM Sleep
• Longest Light Sleep Stage Period
• Longest Deep Sleep Stage Period
• Longest REM Sleep Stage Period

Sleep Continuity (Fragmentation)

• Wake After Sleep Onset (WASO): Time awake after sleep starts
• Number of Awakenings
• Average Duration of Awakenings
• Sleep Fragmentation Index: Awakenings / TST
• Sleep Fragmentation Ratio: Awakenings / Num Sleep Stages
• Ratio between Wake After Sleep Onset time and Total Sleep Time
• Ratio between Sleep Onset Latency and Time in Bed
• Average Total Sleep Time

Sleep Efficiency and Regularity

• Sleep Efficiency: (TST / TIB) × 100
• Sleep Onset Regularity: Circular std dev of onset time across days
• Wake Time Regularity: Circular std dev of wake time across days
• Bedtime Regularity
• Midpoint Sleep Regularity

CSV Format

The input CSV must include the following columns:

Column	Type	Description
participant_id	int or str	Unique ID per participant
day_number	int	Study or recording day number
sleep_stage_state	str or int	Stage label (e.g., "light", "rem")
start_time	timestamp	Segment start datetime
end_time	timestamp	Segment end datetime
duration_minutes	float	Duration of sleep segment in minutes
start_date	date	Date of start_time
end_date	date	Date of end_time
start_time_of_day	time	Clock time of start_time
end_time_of_day	time	Clock time of end_time

Use the load_csv_file(filepath) utility from sound_sleep to correctly parse, format, and clean the dataset. Note: This function automatically removes duplicate rows using drop_duplicates() to prevent inflated sleep durations due to accidental row duplication.

To export feature dictionaries to CSV:

from sound_sleep.io.exports import save_feature_dicts_to_csv
save_feature_dicts_to_csv(list_of_dicts, "output.csv")

Contributing

We welcome improvements, bug reports, and feature requests! Please open an issue or submit a pull request.

To test locally:

pytest tests/

or use sample usage from the Quickstart with synthetic data.

If you wish to develop in your own environment:

git clone https://github.com/theBigTao/SoundSleep.git
cd sound-sleep
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e .[dev]

Developing

Extending Features

1. Create a new method in SleepFeaturesExtractor (in sleep_features.py).
2. Add documentation.
3. Optionally include it in a dictionary in aggregators.py to compute features across days or participants.
4. Add corresponding tests in tests/.

Aggregators

High-level aggregators live in sound_sleep/aggregators.py. These functions group together multiple low-level feature functions into:

• Day-level aggregators:

  • build_dict_day_level_features(extractor, participant_id)
  • build_dict_all_day_level_features(extractor)

• Participant-level aggregators:

  • build_dict_participant_level_features(extractor, participant_id)
  • build_dict_all_participant_level_features(extractor)

• Modular one-day functions:

  • compute_all_features(extractor, participant_id, day_number)
  • compute_sleep_timing_functions(...)
  • compute_sleep_architecture_features(...)
  • compute_sleep_continuity_features(...)
  • compute_sleep_efficiency_and_regularity(...)

You can import and use any individual feature function or dictionary-building aggregator directly from the sound_sleep namespace.

New Device Support

Write a parsing function like load_<device>_data(filepath) that reformats your device's data into the expected schema. Place it in the sound_sleep/io/ module.

Example:

from sound_sleep.io import load_fitbit_data

df = load_fitbit_data("fitbit.json")

Running the Package

You can run the complete extraction pipeline using the provided main.py script:

python src/sound_sleep/main.py

Make sure the input file path is correctly specified in main.py. You can modify main.py to run only day-level or only participant-level pipelines.