Towards a Wearable Polysomnography Approach: A Dataset of Key Physiology and Body Position Metrics Validated Against In-Laboratory Polysomnography
TL;DR
A dataset from a wearable sensor that monitors respiration, electrocardiography, body position/actigraphy, and skin temperature, suitable for sleep studies is presented, and broad agreement between standard metrics used to assess sleep quality measured from the wearable sensor and the resident polysomnography system is shown.
Abstract
We present a dataset from a wearable sensor that monitors respiration, electrocardiography (one lead), body position/actigraphy, and skin temperature, suitable for sleep studies. We collected the dataset in parallel with standard in-laboratory polysomnography (PSG) and used it to validate ASPEN-derived metrics against clinical assessments. Data were collected from seven volunteers (mean age = 55.7 ± 12.5 years and body mass index (BMI) = 29.9 ± 3.78 kg/m2) for a total of 63 h during sleep studies at the Flinders University Sleep Lab. Our preliminary analysis shows broad agreement between standard metrics used to assess sleep quality measured from the wearable sensor and the resident (Compumedics) polysomnography system, such as respiratory rate, heart rate, and sleep posture. Therefore, we make this dataset of high-resolution ASPEN signals, along with synoptic participant data (age, gender, BMI), available to support the development and benchmarking of algorithms for wearable sleep monitoring.