pyActigraphy: open-source python package for actigraphy data visualisation and analysis

Hammad, G. H.; Reyt, Mathilde; Beliy, Nikita; Baillet, Marion; Deantoni, Michele; Lesoinne, Alexia; Muto, Vincenzo; Schmidt, Christina

doi:10.1101/2020.12.03.400226

Cited by 6 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To cancel out miniscule movements that might not be representative of nocturnal arousal, we decided to first derive epoch-wise sleep versus wake estimates to differentiate between moments of no or little movement from moments of high wrist movement. We obtained epoch-wise sleep versus wake estimates using the open-source package PyActigraphy [ 26 ] implementing a device-tuned version of the Cole-Kripke algorithm provided by actigraph manufacturer Condor Instruments. Epochs were analyzed within sleep diary-derived bedtimes and get-up times.…”

Section: Methodsmentioning

confidence: 99%

Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia

Rösler

Lande

Leerssen

et al. 2022

Sleep

View full text Add to dashboard Cite

Study Objectives The objective assessment of insomnia has remained difficult. Multisensory devices collecting heart rate (HR) and motion are regarded as the future of ambulatory sleep monitoring. Unfortunately, reports on altered average HR or heart rate variability (HRV) during sleep in insomnia are equivocal. Here, we evaluated whether the objective quantification of insomnia improves by assessing state-related changes in cardiac measures. Methods We recorded electrocardiography, posture, and actigraphy in 33 people without sleep complaints and 158 patients with mild to severe insomnia over four days in their home environment. At the microscale, we investigated whether HR changed with proximity to gross (body) and small (wrist) movements at nighttime. At the macroscale, we calculated day-night differences in HR and HRV measures. For both timescales, we tested whether outcome measures were related to insomnia diagnosis and severity. Results At the microscale, an increase in HR was often detectable already 60 seconds prior to as well as following a nocturnal chest, but not wrist, movement. This increase was slightly steeper in insomnia and was associated with insomnia severity, but future EEG recordings are necessary to elucidate whether these changes occur prior to or simultaneously with PSG-indicators of wakefulness. At the macroscale, we found an attenuated cardiac response to sleep in insomnia: patients consistently showed smaller day-night differences in HR and HRV. Conclusions Incorporating state-related changes in cardiac features in the ambulatory monitoring of sleep might provide a more sensitive biomarker of insomnia than the use of cardiac activity averages or actigraphy alone.

show abstract

Section: Methodsmentioning

confidence: 99%

Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia

Rösler

Lande

Leerssen

et al. 2022

Sleep

View full text Add to dashboard Cite

show abstract

“…Within this perspective, efforts have been made to develop opensource toolboxes that allow users to preprocess, visualize and quantify various properties of the rest−activity rhythms using actigraphy data from various brands of devices, as well as to perform more advanced signal processing analyses (Hammad et al, 2020 In addition, although recently there has been remarkable ad-…”

Section: Challeng E S and Opp Ortunitie S For The Eni G Ma-s Leep Work Ing G Roupmentioning

confidence: 99%

“…Actigraphy has been widely used to study rest−activity patterns and sleep, with a growing interest in the analysis of large population‐based samples. Within this perspective, efforts have been made to develop open‐source toolboxes that allow users to preprocess, visualize and quantify various properties of the rest−activity rhythms using actigraphy data from various brands of devices, as well as to perform more advanced signal processing analyses (Hammad et al., 2020). However, actigraphy has low specificity and tends to overestimate sleep time in comparison to PSG and, unlike PSG, cannot discriminate between different stages of sleep (Park & Choi, 2019), for which an alternative is multimodal wearable sleep sensors (Boe et al., 2019).…”

Section: Challenges and Opportunities For The Enigma‐sleep Working Groupmentioning

confidence: 99%

ENIGMA‐Sleep: Challenges, opportunities, and the road map

Tahmasian

Alemán

Andreassen

et al. 2021

Journal of Sleep Research

Self Cite

View full text Add to dashboard Cite

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful col-How to cite this article: Tahmasian M, Aleman A, Andreassen OA, et al. ENIGMA-Sleep: Challenges, opportunities, and the road map.

show abstract

“…(2015). The algorithm was originally implemented in C ++ in the software ChronoSapiens ( © 2020 Chronsulting UG; Roenneberg et al., 2015), but has also been included in the Python package pyActigraphy (Hammad et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep–wake patterns from activity recordings

Loock

Sullivan

Reis

et al. 2021

Journal of Sleep Research

View full text Add to dashboard Cite

Periods of sleep and wakefulness can be estimated from wrist‐locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm. The Munich Actimetry Sleep Detection Algorithm uses a moving 24–h threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. The Munich Actimetry Sleep Detection Algorithm was validated against sleep logs and polysomnography. Sleep‐log validation was performed on two field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing one night of polysomnography. Epoch‐by‐epoch analyses were conducted and comparisons of sleep measures carried out via Bland‐Altman plots and correlations. Compared with sleep logs, the Munich Actimetry Sleep Detection Algorithm classified sleep with a median sensitivity of 80% (interquartile range [IQR] = 75%–86%) and specificity of 91% (87%–92%). Mean onset and offset times were highly correlated (r = .86–.91). Compared with polysomnography, the Munich Actimetry Sleep Detection Algorithm reached a median sensitivity of 92% (85%–100%) but low specificity of 33% (10%–98%), owing to the low frequency of wake episodes in the night‐time polysomnographic recordings. The Munich Actimetry Sleep Detection Algorithm overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically differently from polysomnography in other sleep parameters. These results demonstrate the validity of the Munich Actimetry Sleep Detection Algorithm in faithfully estimating sleep–wake patterns in field studies. With its good performance across daytime and night‐time, it enables analyses of sleep–wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.

show abstract

pyActigraphy: open-source python package for actigraphy data visualisation and analysis

Cited by 6 publications

References 38 publications

Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia

Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia

ENIGMA‐Sleep: Challenges, opportunities, and the road map

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep–wake patterns from activity recordings

Contact Info

Product

Resources

About