Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform

Adamczyk, Marek; Genzel, Lisa; Dresler, Martin; Steiger, Axel; Frieß, Elisabeth

doi:10.3389/fnhum.2015.00624

Cited by 54 publications

(56 citation statements)

References 56 publications

(91 reference statements)

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“…Scoring Variability in Sleep Staging may be visually uncertain, that, in the absence of quantitative measurements, it is difficult to score them with certainty. There are numerous algorithms for detecting spindles [18][19][20] that can reduce these disagreements if incorporated as a prescoring module. For example, the events marked as "spindles?"…”

Section: Potential Approaches To Mitigate Inter-scorer Variabilitymentioning

confidence: 99%

Staging Sleep in Polysomnograms: Analysis of Inter-Scorer Variability

Younes

Raneri

Hanly

2016

Journal of Clinical Sleep Medicine

103

106

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Study Objectives: To determine the reasons for inter-scorer variability in sleep staging of polysomnograms (PSGs). Methods: Fifty-six PSGs were scored (5-stage sleep scoring) by 2 experienced technologists, (first manual, M1). Months later, the technologists edited their own scoring (second manual, M2) based upon feedback from the investigators that highlighted differences between their scoring. The PSGs were then scored with an automatic system (Auto) and the technologists edited them, epoch-by-epoch (Edited-Auto). This resulted in 6 different manual scores for each PSG. Epochs were classified as scorer errors (one M1 score differed from the other 5 scores), scorer bias (all 3 scores of each technologist were similar, but differed from the other technologist) and equivocal (sleep scoring was inconsistent within and between technologists). Results: Percent agreement after M1 was 78.9% ± 9.0% and was unchanged after M2 (78.1% ± 9.7%) despite numerous edits (≈40/PSG) by the scorers. Agreement in Edited-Auto was higher (86.5% ± 6.4%, p < 1E−9). Scorer errors (< 2% of epochs) and scorer bias (3.5% ± 2.3% of epochs) together accounted for < 20% of M1 disagreements. A large number of epochs (92 ± 44/PSG) with scoring agreement in M1 were subsequently changed in M2 and/or EditedAuto. Equivocal epochs, which showed scoring inconsistency, accounted for 28% ± 12% of all epochs, and up to 76% of all epochs in individual patients. Disagreements were largely between awake/NREM, N1/N2, and N2/N3 sleep. Conclusion: Inter-scorer variability is largely due to epochs that are difficult to classify. Availability of digitally identified events (e.g., spindles) or calculated variables (e.g., depth of sleep, delta wave duration) during scoring may greatly reduce scoring variability.

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Section: Potential Approaches To Mitigate Inter-scorer Variabilitymentioning

confidence: 99%

Staging Sleep in Polysomnograms: Analysis of Inter-Scorer Variability

Younes

Raneri

Hanly

2016

Journal of Clinical Sleep Medicine

103

106

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“…The first step for automatic SS and HVS detection during the 1 hr of NREM or REM sleep for each rat and experimental group was to filter the EEG signals using the 11–17 Hz band pass filter for SS, or the 4.1–10 Hz band pass filter for HVS. Then, we applied the continuous wavelet transform with the mother wavelet “cmorl‐2’’ MATLAB R2011a function, providing a complex Morlet wavelet with a determined central frequency f 0 = 2 (Adamczyk, Genzel, Dresler, Steiger, & Friess, ; Ciric et al, ). In addition, all the SS had a minimal duration of 0.5 s, whereas all the HVS had a minimal duration of 1 s. However, because we automatically detected many false positive or negative SS or HVS, we had to visually correct all the automatically detected SS or HVS for their final extraction from each NREM/REM sleep episode of each rat and each experimental group.…”

Section: Methodsmentioning

confidence: 99%

Sleep spindle dynamics during NREM and REM sleep following distinct general anaesthesia in control rats and in a rat model of Parkinson’s disease cholinopathy

Lazic

Ciric

Saponjic

2018

Journal of Sleep Research

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On the basis of our previous studies and the important role of the thalamo-cortical network in states of unconsciousness, such as anaesthesia and sleep, and in sleep spindles generation, we investigated sleep spindles (SS) and high-voltage sleep spindle (HVS) dynamics during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep following different types of general anaesthesia in both physiological controls and in a rat model of Parkinson's disease (PD) cholinopathy, to follow the impact of anaesthesia on post-anaesthesia sleep at the thalamo-cortical level through an altered sleep spindle dynamics. We recorded 6 hr of spontaneous sleep in all rats, both before and 48 hr after ketamine/diazepam or pentobarbital anaesthesia, and we used 1 hr of NREM or REM sleep from each to validate visually the automatically detected SS or HVS for their extraction and analysis. In the controls, SS occurred mainly during NREM, whereas HVS occurred only during REM sleep. Ketamine/diazepam anaesthesia promoted HVS, prolonged SS during NREM, induced HVS of increased frequency during REM, and increased SS/HVS densities during REM versus NREM sleep. Pentobarbital anaesthesia decreased the frequency of SS during NREM and the HVS density during REM sleep. Although the pedunculopontine tegmental nucleus lesion prolonged SS only during NREM sleep, in these rats, ketamine/diazepam anaesthesia suppressed HVS during both sleep states, whereas pentobarbital anaesthesia promoted HVS during REM sleep. The different impacts of two anaesthetic regimens on the thalamo-cortical regulatory network are expressed through their distinct sleep spindle generation and dynamics that are dependent on the NREM and REM state regulatory neuronal substrate.

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“…Namely, the first step for automatic SS detection during REM sleep was to concatenate all the extracted 10 s epochs of REM sleep from motor cortex for each rat, each experimental group, and then to filter the EEG signals using the 11 -17 Hz band pass filter. Then, we applied the Continuous Wavelet Transform with the mother wavelet "cmorl-2" MATLAB R2011a function, providing a complex Morlet wavelet with determined central frequency f 0 = 2 [27]. In addition, all SS had a minimal duration of 0.5 s.…”

Section: Sleep Recording and Data Analysismentioning

confidence: 99%

Sleep spindles as an early biomarker of REM sleep disorder in a rat model of Parkinson’s disease cholinopathy

Ciric¹,

Lazic²,

Petrović³

et al. 2017

Transl Brain Rhythmicity

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Rhythmic oscillations of neuronal populations, generated by different mechanisms, are present at several levels of the central nervous system and serve many important physiological or reflect pathological functions. Understanding the role of brain oscillations as possible biomarkers of brain function and plasticity is still a challenge, and despite extensive research, their role is still not well established.We recently demonstrated that the hallmarks of earlier aging onset during impaired thalamo-cortical cholinergic innervation (in a rat model of Parkinson's disease cholinopathy) were consistently expressed, from 3 and one half to 5 and one half months of age, through increased electroencephalographic (EEG) sigma activity amplitude during rapid eye movement (REM) sleep, as a unique aging induced REM sleep phenomenon. In addition, there was altered motor cortical drive during non-rapid-eye-movement (NREM) and REM sleep.In order to explain this new aging-induced REM sleep phenomenon, we analyzed possible differences between control REM sleep spindle activity and REM sleep spindle activity at the onset of REM sleep "enriched" with sigma activity (at 4 and one half months of age), following bilateral pedunculopontine tegmental nucleus (PPT) cholinergic neuronal loss in the rat. We analzyed differences in spindle density, duration, and frequency.We demonstrated in young adult Wistar rats with the severely impaired PPT cholinergic innervation the alterations in sleep spindle dynamics and pattern during REM sleep in the motor cortex as the earliest biomarkers for the onset of their altered aging processes.

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Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform

Cited by 54 publications

References 56 publications

Staging Sleep in Polysomnograms: Analysis of Inter-Scorer Variability

Staging Sleep in Polysomnograms: Analysis of Inter-Scorer Variability

Sleep spindle dynamics during NREM and REM sleep following distinct general anaesthesia in control rats and in a rat model of Parkinson’s disease cholinopathy

Sleep spindles as an early biomarker of REM sleep disorder in a rat model of Parkinson’s disease cholinopathy

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