TY - CONF
T1 - From Pulses to Sleep Stages
T2 - World Sleep 2023
AU - Topalidis, Pavlos
AU - Heib, Dominik Philip Johannes
AU - Baron, Sebastian
AU - Eigl, Esther-Sevil
AU - Hinterberger, Alexandra
AU - Schabus, Manuel
PY - 2023/10/23
Y1 - 2023/10/23
N2 - Introduction: Although polysomnography (PSG) conducted
by human experts is considered the "gold standard" for measuring sleep,
PSG and manual sleep staging require significant personnel and time
resources, making thus monitoring an individual's sleep over extended
periods impractical. For this reason, we aspired to develop an
innovative, cost-effective and automated sleep staging four-class (Wake,
Light [N1 + N2], Deep, REM) approach, utilizing deep learning, solely
using inter-beat-interval (IBI) data.Materials and Methods:
This approach involves training a multi-resolution convolutional neural
network (MCNN) on IBI data from 8898 manually sleep-staged recordings
of full nights. Subsequently, we used transfer learning on the IBIs from
two affordable consumer wearables (an optical heart rate sensor - VS,
and a breast belt - H10) and evaluated the MCNN's sleep classification,
epoch by epoch (30 sec.), against PSG. In addition, using the H10 we
collected daily ECG data from 49 participants experiencing sleep
difficulties throughout a digital cognitive-behavioural therapy for
insomnia (CBT-I) program offered via the NUKKUAA™ App, to examine the
objective effects of sleep training on daily objective sleep measures.Results:
Regarding the evaluation of the MCNN sleep stage classification, we
observed an overall classification accuracy comparable to expert
inter-rater reliability for both devices (VS: 81%, Cohen's 𝜅 = 0.69;
H10: 80.3%, Cohen's 𝜅 = 0.69). Concerning the evaluation of the digital
CBT-i program, participants reported significant enhancements in
subjective sleep quality and sleep onset latency at the end of the
training. At the same time, there was a trend for objective sleep onset
latency improvement. Weekly sleep onset latency, wake time during sleep,
and total sleep time exhibited noteworthy correlations with the
subjective reports.Conclusions: Combining deep
learning with suitable wearables allows continuous and accurate sleep
monitoring in naturalistic settings with profound implications for
answering basic and clinical research questions.
AB - Introduction: Although polysomnography (PSG) conducted
by human experts is considered the "gold standard" for measuring sleep,
PSG and manual sleep staging require significant personnel and time
resources, making thus monitoring an individual's sleep over extended
periods impractical. For this reason, we aspired to develop an
innovative, cost-effective and automated sleep staging four-class (Wake,
Light [N1 + N2], Deep, REM) approach, utilizing deep learning, solely
using inter-beat-interval (IBI) data.Materials and Methods:
This approach involves training a multi-resolution convolutional neural
network (MCNN) on IBI data from 8898 manually sleep-staged recordings
of full nights. Subsequently, we used transfer learning on the IBIs from
two affordable consumer wearables (an optical heart rate sensor - VS,
and a breast belt - H10) and evaluated the MCNN's sleep classification,
epoch by epoch (30 sec.), against PSG. In addition, using the H10 we
collected daily ECG data from 49 participants experiencing sleep
difficulties throughout a digital cognitive-behavioural therapy for
insomnia (CBT-I) program offered via the NUKKUAA™ App, to examine the
objective effects of sleep training on daily objective sleep measures.Results:
Regarding the evaluation of the MCNN sleep stage classification, we
observed an overall classification accuracy comparable to expert
inter-rater reliability for both devices (VS: 81%, Cohen's 𝜅 = 0.69;
H10: 80.3%, Cohen's 𝜅 = 0.69). Concerning the evaluation of the digital
CBT-i program, participants reported significant enhancements in
subjective sleep quality and sleep onset latency at the end of the
training. At the same time, there was a trend for objective sleep onset
latency improvement. Weekly sleep onset latency, wake time during sleep,
and total sleep time exhibited noteworthy correlations with the
subjective reports.Conclusions: Combining deep
learning with suitable wearables allows continuous and accurate sleep
monitoring in naturalistic settings with profound implications for
answering basic and clinical research questions.
M3 - Poster
Y2 - 20 October 2023 through 25 October 2023
ER -