Hybrid in-phase and continuous auditory stimulation significantly enhances slow wave activity during sleep

Garcia-Molina, Gary; Tsoneva, Tsvetomira; Neff, Adam; Salazar, J. D.; Bresch, Erik; Großekathöfer, Ulf; Pastoor, Sander; Aquino, Antonio

doi:10.1109/embc.2019.8857678

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…The CLNS phase targeting accuracy (phase error (mean = 3.8°, SD = 58°, VAR = 29.25°) in this study) offers very precise targeting of SO. Additionally, most previous studies delivered pairs or trains of acoustic stimuli, with only the first one targeted based on the analysis of EEG, and subsequent stimuli following the initial one at fixed 1 s intervals 43,44 . However, phase targeting accuracy seems to be a crucial component for boosting SO and any deviation could instead lead to hinderance of SO.…”

Section: Discussionmentioning

confidence: 99%

The looping lullaby: closed-loop neurostimulation decreases sleepers’ sensitivity to environmental noise

Pathak

Juan

Goot

et al. 2021

Preprint

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Study Objective: Sleep is critical for physical and mental health. However, sleep disruption due to noise is a growing problem, causing long-lasting distress and fragilizing entire populations mentally and physically. Here for the first time, we tested an innovative and non-invasive potential countermeasure for sleep disruptions due to noise. Methods: We developed a new, modeling-based, closed-loop acoustic neurostimulation procedure (CLNS) to precisely phase-lock stimuli to slow oscillations (SO). We used CLNS to align, soft sound pulses to the start of the SO positive deflection to boost SO and sleep spindles during non-rapid eye movement (NREM) sleep. Participants underwent three overnight EEG recordings. The first night served to determine each participant ′s individual noise arousal threshold. The remaining two nights occurred in counterbalanced order: in the Disturbing night, loud, real-life noises were repeatedly presented; in the Intervention night, similar loud noises were played while using the CLNS to boost SO. All experimental manipulations were performed in the first three hours of sleep; participants slept undisturbed for the rest of the night. Results: In contrast to the Disturbing night, the probability of arousals caused by noise was significantly decreased in the Intervention night. Moreover, the CLNS intervention increased NREM duration and sleep spindle power across the night. Conclusions: These results show that our CLNS procedure can effectively protect sleep from disruptions caused by noise. Remarkably, even in the presence of loud environmental noise, CLNS ′ soft and precisely timed sound pulses played a beneficial role in protecting sleep continuity. This represents the first successful attempt at using CLNS in a noisy environment.

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Section: Discussionmentioning

confidence: 99%

The looping lullaby: closed-loop neurostimulation decreases sleepers’ sensitivity to environmental noise

Pathak

Juan

Goot

et al. 2021

Preprint

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“…Two studies have been performed in the sleep-disordered population using frontal channels in wearable EEG, both showing κ = .67 agreement levels (Lucey et al, 2016;Levendowski et al, 2017). It should be noted that also lower inter-rater reliability for manual scoring of the recordings is reported (κ = .69 in Garcia-Molina et al, 2019;κ = .70 in Levendowski et al, 2017;κ = .78 in Popovic et al, 2014), suggesting an upper limit to the sleep staging information extracted from wearable frontal electrodes.…”

Section: Single-channel (Wearable) Eegmentioning

confidence: 99%

Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches

Van Der Aar,

Van Den Ende,

Fonseca

et al. 2024

Front. Physiol.

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Introduction: Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice. However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG).Methods: We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N = 94). Performance of the different training strategies was evaluated using Cohen’s Kappa (κ) in eight smaller target datasets consisting of healthy subjects (N = 60), patients with obstructive sleep apnea (OSA, N = 60), insomnia (N = 60), and REM sleep behavioral disorder (RBD, N = 22), combined with two EEG channels, F3-M2 and F3-F4.Results: No differences in performance between the training strategies was observed in the age-matched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ = .17 on average.Discussion: We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training.

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“…The current device did not equip EOG electrodes to assess REM sleep, relying on the automatic sleep stager to classify REM sleep. The performance of the automatic sleep stager in the device was compared to manual scoring of the devices' raw EEG data in (Garcia-Molina et al, 2019). This study showed kappa scores of 0.65 to 0.67, compared to an inter-rater agreement of 0.69, indicating that the device's automated stager performed equally to manual scoring.…”

Section: Discussionmentioning

confidence: 90%

Waves of Change: Sex Hormones, Depression and Sleep

Morssinkhof

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Depression is twice as common in women compared to men, and the risk of insomnia is 1.5 times higher in women than in men during the reproductive age. It has been suggested that sex hormones, including estrogen, testosterone and progesterone, may partly explain this difference in prevalence. Changes in these hormones may possibly contribute to more depressive symptoms and more insomnia. One way to examine the effects of sex hormones on depression and sleep is to investigate situations where individuals start taking exogenous hormonal medications. Therefore, this study aimed to examine the effects of oral contraceptives (OCs) and the effects of gender-affirming hormone therapy (GAHT). We examined whether the use of OCs and GAHT affected depressive symptoms, sleep quality, sleep architecture and chronotype. The main research questions in this thesis were: 1. Are OC use and GAHT use associated with changes in depression? 2. Are OC use and GAHT use associated with changes in sleep? 3. What are the effects of sex hormones on the association between depression and sleep? 1. Sex hormones and depression Our findings show that OC use could be associated with increases in depressive symptoms and increased risk of depression, but that there might be strong individual differences in the effects of OCs on mood. Our findings in transgender people who start GAHT suggest that masculinizing and feminizing sex hormones have different effects on depressive symptoms, although it must be noted that we find only small to modest changes. 2. Sex hormones and sleep We find that use of OCs could possibly have very small effects on sleep, and that this could be related to the day-night rhythm of the hormone cortisol. In our studies on GAHT, we see that transgender people on masculinizing GAHT show no clinically significant changes in insomnia or sleep quality, but they show shorter slow wave (deep) sleep and slightly longer and earlier REM (dream) sleep after starting GAHT, as well as a later chronotype. After starting feminizing GAHT, transgender people show a shorter subjective sleep onset latency, no changes in sleep architecture and an earlier chronotype. 3. Sex hormones, depression and sleep Results from our systematic review and our studies on OCs and GAHT do not provide substantive support for the hypothesis that sex hormones affect the relationship between depression and sleep. The research results in this thesis show that changes in sex hormones can lead to small changes in depressive symptoms that are in line with prevalence differences in the population: feminizing hormones, including OAC and GAHT, can lead to modest increases in depressive symptoms and masculinizing GAHT can lead to modest decreases in depression symptoms. The effects of sex hormones on subjective sleep vary, and GAHT was found to have strongest effects on sleep architecture and chronotype. The hypothesis that sex hormones cause poor sleep, which would then lead to increases in depressive symptoms, was not supported by our findings. For future studies on mood, I emphasize the importance of examining short-term changes in mood, since changes in daily moods and mood variability have been described after sex hormone changes. For future studies on sleep, I recommend to further study the role of previous and current sex hormone status (i.e., OC use, menopause status) on sleep architecture and chronotype. Lastly, I advocate for the inclusion of sex and sex hormones in psychiatric research and in clinical practice, to provide knowledge and techniques for prevention, early detection and treatment of sex hormone-related problems in sleep and mood.

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Hybrid in-phase and continuous auditory stimulation significantly enhances slow wave activity during sleep

Cited by 8 publications

References 18 publications

The looping lullaby: closed-loop neurostimulation decreases sleepers’ sensitivity to environmental noise

The looping lullaby: closed-loop neurostimulation decreases sleepers’ sensitivity to environmental noise

Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches

Waves of Change: Sex Hormones, Depression and Sleep

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