The CAP Sleep Database is a collection of 108 polysomnographic recordings registered at the Sleep Disorders Center of the Ospedale Maggiore of Parma, Italy. The waveforms (contained in the .edf files of the database) include at least 3 EEG channels (F3 or F4, C3 or C4 and O1 or O2, referred to A1 or A2), EOG (2 channels), EMG of the submentalis muscle, bilateral anterior tibial EMG, respiration signals (airflow, abdominal and thoracic effort and SaO2) and EKG. Additional traces include EEG bipolar traces, according to the 10-20 international system (Fp1-F3, F3-C3, C3-P3, P3-O1 and/or Fp2-F4, F4-C4, C4-P4, P4-O2).

The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature. Corresponding hypnograms (sleep patterns) were manually scored by well-trained technicians according to the Rechtschaffen and Kales manual, and are also available.

Dataset

This dataset was created by saidur_abir

Released under MIT

Contents

Online Sleep Survey dataThese data were obtained over the course of several years. The primary purpose was to build a database of subjects from which I could recruit for my Sleep and Decision Making research studies. Data included are basic demographics some self report sleep data, a validated short form measure of morningness/eveningness preferences, and screener questions for anxiety and depressive disorder (as well as self-reported sleep disorder).

Comprehensive Sleep and Health Metrics

A Detailed Overview of Synthetic Sleep Patterns and Influencing Factors

Dataset Introduction

The Comprehensive Sleep and Health Metrics dataset is a fully synthetic collection designed to provide an extensive overview of how various factors might influence sleep quality and overall health. This dataset is created to simulate a wide range of scenarios and conditions, offering a robust foundation for predictive modeling and analytical studies. By utilizing synthetic data, the dataset ensures a comprehensive representation of potential variations and interactions in sleep and health metrics.

Dataset Description

The dataset includes a diverse array of synthetic measurements, covering:

• Heart Rate Variability: Simulated variability in time intervals between heartbeats.
• Body Temperature: Artificially generated body temperature in degrees Celsius.
• Movement During Sleep: Synthetic data on the amount of movement while sleeping.
• Sleep Duration Hours: Total hours of sleep generated through simulation.
• Sleep Quality Score: A synthetic score representing the quality of sleep.
• Caffeine Intake (mg): Amount of simulated caffeine consumption in milligrams.
• Stress Level: An index of simulated stress levels.
• Bedtime Consistency: Simulated consistency of bedtime routine. 0-1 scale, where lower values indicate more inconsistency.
• Light Exposure Hours: Synthetic hours of light exposure during the day. Reflects typical daylight exposure hours.

The use of synthetic data in this dataset allows for a controlled and varied exploration of how different factors might interact to influence sleep quality, which can be particularly useful for developing and testing predictive models.

Prediction Task

The goal is to predict the Sleep Quality Score based on the other synthetic health and sleep metrics. This predictive analysis aims to uncover patterns and relationships within the simulated data, providing insights into factors that could impact sleep quality and guiding the development of strategies for improving sleep health.

The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~15K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting. This data is being used to develop CAISR (Complete AI Sleep Report), a collection of deep neural networks, rule-based algorithms, and signal processing approaches designed to provide better-than-human detection of conventional PSG scoring metrics, including sleep stages, arousals, apnea and hypopnea events and their subtypes, and periodic limb movements. Beyond conventional scoring, the HSP dataset is intended to support research seeking to identify "hidden" information within the brain's activity during sleep that can be used to directly measure brain health. These brain health indicators include measures of risk for common neurologic diseases, including cerebrovascular disease, and Alzheimer's disease and related neurodegenerative diseases of aging; and indicators of response to therapies, including lifestyle interventions (e.g. diet, meditation, exercise) and pharmacologic interventions. These data are shared via the BDSP (Brain Data Science Platform, a resource developed by an international coalition of investigators that aggregates and harmonizes a wide range of large-scale human clinical neuroscience data to support research aimed at improving diagnosis, treatment, and prevention of neurologic disease, and promotion of brain health. The summary data provided here are released for the benefit of the wider scientific community without restriction on use.

Sleep EEG dataset from 8 subjects in European Data Format (EDF) including original recordings and their hypnograms as described in B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery��. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000). The recordings were obtained from Caucasian males and females (21 - 35 years old) without any medication; they contain horizontal EOG, FpzCz and PzOz EEG, each sampled at 100 Hz. The sc* recordings also contain the submental-EMG envelope, oro-nasal airflow, rectal body temperature and an event marker, all sampled at 1 Hz. The st* recordings contain submental EMG sampled at 100 Hz and an event marker sampled at 1 Hz. The 4 sc* recordings were obtained in 1989 from ambulatory healthy volunteers during 24 hours in their normal daily life, using a modified cassette tape recorder. The 4 st* recordings were obtained in 1994 from subjects who had mild difficulty falling asleep but were otherwise healthy, during a night in the hospital, using a miniature telemetry system with very good signal quality.

Dataset Overview: The Sleep Health and Lifestyle Dataset comprises 15000 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.

Key Features of the Dataset: Comprehensive Sleep Metrics: Explore sleep duration, quality, and factors influencing sleep patterns. Lifestyle Factors: Analyze physical activity levels, stress levels, and BMI categories. Cardiovascular Health: Examine blood pressure and heart rate measurements. Sleep Disorder Analysis: Identify the occurrence of sleep disorders such as Insomnia and Sleep Apnea.

Dataset Columns: Person ID: An identifier for each individual. Gender: The gender of the person (Male/Female). Age: The age of the person in years. Occupation: The occupation or profession of the person. Sleep Duration (hours): The number of hours the person sleeps per day. Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10. Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily. Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10. BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight). Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure. Heart Rate (bpm): The resting heart rate of the person in beats per minute. Daily Steps: The number of steps the person takes per day. Sleep Disorder: The presence or absence of a sleep disorder in the person (Healthy, Insomnia, Sleep Apnea).

Details about Sleep Disorder Column:

Healthy: The individual does not exhibit any specific sleep disorder. Insomnia: The individual experiences difficulty falling asleep or staying asleep, leading to inadequate or poor-quality sleep. Sleep Apnea: The individual suffers from pauses in breathing during sleep, resulting in disrupted sleep patterns and potential health risks.

The data includes polysomnography (PSG) data collected from both children and adults during full night, restricted night and alternate sleep schedules. Data includes standard recording of electroencephalography, electromyography and electrooculography, and in some cases a further combination of electrocardiography and respiratory measures.

QUT Research Data Respository Dataset and Resources

A collection of 151 whole-night polysomnographic (PSG) sleep recordings (85 Male, 66 Female, mean Age of 53.9 ± 15.4) collected during 2018 at the Haaglanden Medisch Centrum (HMC, The Netherlands) sleep center. Patient recordings were randomly selected and include a heterogeneous population which was referred for PSG examination on the context of different sleep disorders. The dataset contains electroencephalographic (EEG), electrooculographic (EOG), chin electromyographic (EMG), and electrocardiographic (ECG) activity, as well as event annotations corresponding to scoring of sleep patterns (hypnogram) performed by sleep technicians at HMC. The dataset was collected as part of a study evaluating the generalization performance of an automatic sleep scoring algorithm across multiple heterogeneous datasets.

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Dataset Overview: The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.

Key Features of the Dataset: Comprehensive Sleep Metrics: Explore sleep duration, quality, and factors influencing sleep patterns. Lifestyle Factors: Analyze physical activity levels, stress levels, and BMI categories. Cardiovascular Health: Examine blood pressure and heart rate measurements. Sleep Disorder Analysis: Identify the occurrence of sleep disorders such as Insomnia and Sleep Apnea.

Dataset Columns: Person ID: An identifier for each individual. Gender: The gender of the person (Male/Female). Age: The age of the person in years. Occupation: The occupation or profession of the person. Sleep Duration (hours): The number of hours the person sleeps per day. Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10. Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily. Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10. BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight). Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure. Heart Rate (bpm): The resting heart rate of the person in beats per minute. Daily Steps: The number of steps the person takes per day. Sleep Disorder: The presence or absence of a sleep disorder in the person (None, Insomnia, Sleep Apnea).

Details about Sleep Disorder Column:

None: The individual does not exhibit any specific sleep disorder. Insomnia: The individual experiences difficulty falling asleep or staying asleep, leading to inadequate or poor-quality sleep. Sleep Apnea: The individual suffers from pauses in breathing during sleep, resulting in disrupted sleep patterns and potential health risks.

Context

Sleep is an extremely important physiological process that aids in the restoration of the body's functional systems and the maintenance of cognitive functions. One of the common sleep problems is sleep-disordered breathing (SDB), the prevalence of which increases due to various factors such as overweight and obesity, reduced physical activity, and others. SDB can lead to serious cardiovascular and neurological disorders. The diagnosis of SDB requires a full polysomnography and its interpretation by specialists, making the process labor-intensive and complex, especially in the early stages when symptoms are mild.

This dataset consists of readings from 40 patients: 6 channels of electroencephalography and some tabular attributes.

Attribute Information

patients.csv

1. user_id - Unique user id [Range 1-40]
2. night_id - Night id of the row [Range 1-2]
3. age - Age of the patient [Years]
4. sex - sex of the patient [F: Female, M: Male]
5. height - Height of the patient [Centimeters]
6. weight - Weight of the patient [Kilograms]
7. pulse - Pulse of the patient [Beats per minute]
8. BPsys/BPdia - Systolic blood pressure / diastolic blood pressure of the patient [Millimeters of mercury/Millimeters of mercury]
9. ODI - Oxygen desaturation index of the patient for the night. For some healthy patients will be none [Continuous range 0-...]
10. NAp - Number of cases apnoe of the patient for the night. For healthy patients is none [Range 0-...]
11. NHyp - Number of cases hyperapnoe of the patient for the night. For healthy patients is none [Range 0-...]
12. AI - Apnoe index of the patient for the night. Calculated by the formula: NAp / TimeOfRecordInHours. For healthy patients is none [Continuous range 0-...]
13. HI - Hyperapnoe index of the patient for the night. Calculated by the formula: NHyp / TimeOfRecordInHours. For healthy patients is none [Continuous range 0-...]
14. AHI - Apnoe-hyperapnoe index of the patient for the night. Calculated by the formula: (NAp+NHyp) / TimeOfRecordInHours. For healthy patients is none [Continuous range 0-...]

polysomnographics/User-{user_id}-Night-{night_id}.npy

80 Records, 2 night per patient. NPY is format is the standard binary file format in NumPy for persisting a single arbitrary NumPy array on disk

Each file is a two-dimensional array, with the rows representing various channels of the patient's electroencephalogram.

Sampling rate for all channels is: 200 Hz

Description of array: [Row number] - Information about channel

1. Fp1-M2
2. C3-M2
3. O1-M2
4. Fp2-M1
5. C4-M1
6. O2-M1

In order to accelerate research on pediatric sleep and its connection to health, Nationwide Children's Hospital (NCH) and Carnegie Mellon University (CMU) introduce the NCH Sleep DataBank. This dataset has 3,984 pediatric sleep studies on 3,673 unique patients conducted at NCH in Columbus, Ohio, USA between 2017 and 2019, along with the patient's longitudinal clinical data. The published Polysomnography (PSG) contains the patient's physiological signals as well as the technician's assessment of the sleep stages and descriptions of additional irregularities.

The novelties of this dataset include: (1) Size: Its large size is suitable for discovering new scientific insights via data mining, (2) Patient population: It explicitly focuses on pediatric patients, (3) Clinical setting: The sleep studies were gathered in the real-world clinical setting at NCH as opposed to, for example, in a controlled clinical trial, and (4) Rich set of clinical data: The accompanying 5.6 million records of clinical data are extracted from the Electronic Health Record (EHR), and are separated into encounters, medications, measurements (e.g. body mass index), diagnoses, and procedures.

The NCH Sleep DataBank is a valuable resource for advancing automatic sleep scoring and real-time sleep disorder prediction, among many other potential scientific discoveries. Accompanying code in Python to assist users in interacting with the dataset is published on GitHub.

The DREAMS Databases and Assessment algorithm

During the DREAMS project funded by Région Wallonne (Be), we collected a large amount of polysomnographic recordings (PSG) to tune, train and test our automatic detection algorithms.

These recordings were annotated in microevents or in sleep stages by several experts. They were acquired in a sleep laboratory of a belgium hospital using a digital 32-channel polygraph (BrainnetTM System of MEDATEC, Brussels, Belgium). The standard European Data Format (EDF) was used for storing.

In order to facilitate future research and performance comparision, we decided to publish these data on Internet. Therefore, eight DREAMS databases are available according to the annotation carried out (click on the link to open):

• The DREAMS Subjects Database: 20 whole-night PSG recordings coming from healthy subjects, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Patients Database: 27 whole-night PSG recordings coming from patients with various pathologies, annoted in sleep stages according to both the Rechtschaffen and Kales criteria and the new standard of the American Academy of Sleep Medicine;

• The DREAMS Artifacts Database: 20 excerpts of 15 minutes of PSG recordings annoted in artifacts (cardiac interference, slow ondulations, muscle artifacts, failing electrode, 50/60Hz main interference, saturations, abrupt transitions, EOG interferences and artifacts in EOG) by an expert;

• The DREAMS Sleep Spindles Database: 8 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in sleep spindles; PLEASE NOTICE THAT EXPERT 1's SCORED SPINDLE COUNTS WERE CUT OFF AFTER 1000 SECONDS. THIS MAKES IT DIFFICULT TO USE COUNTS FOR COMPARISON.

• The DREAMS K-complexes Database: 5 excerpts of 30 minutes of central EEG channel (extracted from whole-night PSG recordings), annotated independently by two experts in K-complexes;

• The DREAMS REMs Database: 9 excerpts of 30 minutes of PSG recordings in which rapid eye movements were annotated by an expert;

• The DREAMS PLMs Database: 10 whole-night PSG recordings coming from patients in which one of the two tibialis EMG was annoted in periodic limb movements by an expert;

• The DREAMS Apnea Database: 12 whole-night PSG recordings coming from patients annoted in respiratory events (central, obstructive and mixed apnea and hypopnea) by an expert.

We also developped and tested several automatic procedures to detect micro-events such as sleep spindles, K-complexes, REMS, etc. and provide the source codes for them in the DREAMS Assessment Algorithm package.

(MORE INFORMATION ON EACH DBA CAN BE FOUND in pdf file in this repository)

All our publications on this subject can be found in : https://www.researchgate.net/scientific-contributions/35338616_S_Devuyst

These are the decision data from a risky choice experimental task from a 2008 funded National Science Foundation Project examining sleep and risky choice.

Data contained in this collection includes polysomnography (PSG) data collected from both children and adults during full night, restricted night and alternate sleep schedules. Data includes standard recording of electroencephalography, electromyography and electrooculography, and in some cases a further combination of electrocardiography and respiratory measures.

Sleep data summarized by minutes in each stage

Content

This dataset shows the relationship between lifestyle choices and their effects on overall sleep health. It includes detailed information on people's physological factors, lifestyle choices, and sleep health, including blood pressure, heart rate, physical activity, body mass index (BMI), daily steps, stress levels, sleep duration, sleep quality, and the presence of sleep disorder.

Context

The dataset, which offers insights into how daily routines and health indicators impact sleep quality and disorders, is intended for research, analysis, and instructional purposes. Data analysts, wellness resarchers, and medical professionals that want to investigate relationship between lifestyle factors and sleep health outcomes will find it helpful.

File Information

File Name: Sleep_health_and_lifestyle_dataset.csv

File Type: CSV (Comma-Separated Values)

Number of Rows: 374

Number of Columns: 13

Data Domain: Health, Sleep, and Lifestyle

Key Variables: Sleep duration, sleep quality, stress level, BMI, heart rate, blood pressure, physical activity, daily steps, and sleep disorders

Use Cases: Data analysis, research, visulization, healthcare insights, and predictive modeling

The SS2 subset of the Montreal Archive of Sleep Studies (MASS) cohort is an open-access database of laboratory-based polysomnography (PSG) recordings defined as : 19 subjects (age 23.6±3.7 years, age range: 18-33 years) 8 males (age 24.3±4.2 years, age range: 19-33 years) 11 females (age 23.2±3.5 years, age range: 18-30 years) 19 PSG recordings (whole night) "* PSG.edf" 19 electrodes in the EEG montage reference is computed linked-ear (CLE) 4 EOG channels 1 bipolar EMG 1 ECG channel Respiratory thermistance 19 Sleep staging files "* Base.edf" Sleep stage scoring rules : R&K Page size (s) : 20