Success Rate and Technical Quality of Home Polysomnography With Self-Applicable Electrode Set in Subjects With Possible Sleep Bruxism

Using sleep laboratory polysomnography (PSG) is restricted for the diagnosis of only the most severe sleep disorders due to its low availability and high cost. Home PSG is more affordable, but applying conventional electroencephalography (EEG) electrodes increases its overall complexity and lowers the availability. Simple, self-administered single-channel EEG monitors on the other hand suffer from poor reliability. In this study, we aimed to quantify the reliability of self-administrated home PSG recordings conducted with a newly designed ambulatory electrode set (AES) that enables multichannel EEG, electrooculography, electromyography, and electrocardiography recordings. We assessed the sleep study success rate and technical quality of the recordings performed in subjects with possible sleep bruxism (SB). Thirty-two females and five males aged 39.6 +/- 11.6 years (mean +/- SD) with self-reported SB were recruited in the study. Self-administrated home PSG recordings with two AES designs were conducted (n = 19 and 21). The technical quality of the recordings was graded based on the proportion of interpretable data. Technical failure rate for AES (both designs) was 5% and SB was scorable for 96.9% of all recorded data. Only one recording failed due to mistakes in self-applying the AES. We found that the proportion of good quality self-administrated EEG recordings is significantly higher when multiple channels are used compared to using a single channel. Sleep study success rates and proportion of recordings with high quality interpretable data from EEG channels of AES were comparable to that of conventional home PSG. Self-applicable AES has potential to become a reliable tool for widely available home PSG.Peer reviewe

Self-Applied Electrode Set Provides a Clinically Feasible Solution Enabling EEG Recording in Home Sleep Apnea Testing

Home sleep apnea testing (HSAT) without electroencephalography (EEG) recording is increasingly used as an alternative to in-laboratory polysomnography for the diagnosis of obstructive sleep apnea (OSA). However, without EEG, electrooculography (EOG), and chin electromyography (EMG) recordings, the OSA severity may be significantly underestimated. Although several ambulatory EEG systems have been recently introduced, no patient-applied systems including EEG, EOG, and chin EMG suitable for home polysomnography are currently in clinical use. We have recently developed and pre-clinically tested a self-applied ambulatory electrode set (AES), consisting of frontal EEG, EOG, and EMG, in subjects with possible sleep bruxism. Now, in this clinical feasibility study, we investigated the signal scorability and usability of the AES as a self-administered sleep assessment approach supplementing the conventional HSAT device. We also investigated how the diagnostic parameters and OSA severity changed when utilizing the AES. Thirty-eight patients (61 % male, 25-78 years) with a clinical suspicion of OSA conducted a single-night, self-administered HSAT with a portable polysomnography device (Nox A1, Nox Medical, Reykjavik, Iceland) supplemented with AES. Only one AES recording failed. The use of AES signals in data analysis significantly affected the median apnea-hypopnea index (AHI), increasing it from 9.4 to 12.7 events/h (p < 0.001) compared to the conventional HSAT. Also, in eight patients, the OSA severity class changed to one class worse. Perceived ease of use was well in line with that previously found among healthy volunteers. These results suggest that the AES provides an easy, clinically feasible solution to record EEG as a part of conventional HSAT.Peer reviewe

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

We have previously developed an ambulatory electrode set (AES) for the measurement of electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). The AES has been proven to be suitable for manual sleep staging and self-application in in-home polysomnography (PSG). To further facilitate the diagnostics of various sleep disorders, this study aimed to utilize a deep learning-based automated sleep staging approach for EEG signals acquired with the AES. The present neural network architecture comprises a combination of convolutional and recurrent neural networks previously shown to achieve excellent sleep scoring accuracy with a single standard EEG channel (F4-M1). In this study, the model was re-trained and tested with 135 EEG signals recorded with AES. The recordings were conducted for subjects suspected of sleep apnea or sleep bruxism. The performance of the deep learning model was evaluated with 10-fold cross-validation using manual scoring of the AES signals as a reference. The accuracy of the neural network sleep staging was 79.7% (kappa = 0.729) for five sleep stages (W, N1, N2, N3, and R), 84.1% (kappa = 0.773) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% (kappa = 0.801) for three sleep stages (W, NREM, R). The utilized neural network was able to accurately determine sleep stages based on EEG channels measured with the AES. The accuracy is comparable to the inter-scorer agreement of standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in-home PSGs.Peer reviewe

Signal acquisition and analysis of ambulatory electromyographic recordings for the assessment of sleep bruxism : A scoping review

Background Ambulatory electromyographic (EMG) devices are increasingly being used in sleep bruxism studies. EMG signal acquisition, analysis and scoring methods vary between studies. This may impact comparability of studies and the assessment of sleep bruxism in patients. Objectives (a) To provide an overview of EMG signal acquisition and analysis methods of recordings from limited-channel ambulatory EMG devices for the assessment of sleep bruxism; and (b) to provide an overview of outcome measures used in sleep bruxism literature utilising such devices. Method A scoping review of the literature was performed. Online databases PubMed and Semantics Scholar were searched for studies published in English until 7 October 2020. Data on five categories were extracted: recording hardware, recording logistics, signal acquisition, signal analysis and sleep bruxism outcomes. Results Seventy-eight studies were included, published between 1977 and 2020. Recording hardware was generally well described. Reports of participant instructions in device handling and of dealing with failed recordings were often lacking. Basic elements of signal acquisition, for example amplifications factors, impedance and bandpass settings, and signal analysis, for example rectification, signal processing and additional filtering, were underreported. Extensive variability was found for thresholds used to characterise sleep bruxism events. Sleep bruxism outcomes varied, but typically represented frequency, duration and/or intensity of masticatory muscle activity (MMA). Conclusion Adequate and standardised reporting of recording procedures is highly recommended. In future studies utilising ambulatory EMG devices, the focus may need to shift from the concept of scoring sleep bruxism events to that of scoring the whole spectrum of MMA.Peer reviewe

Signal acquisition and analysis of ambulatory electromyographic recordings for the assessment of sleep bruxism: A scoping review

Background: Ambulatory electromyographic (EMG) devices are increasingly being used in sleep bruxism studies. EMG signal acquisition, analysis and scoring methods vary between studies. This may impact comparability of studies and the assessment of sleep bruxism in patients. Objectives: (a) To provide an overview of EMG signal acquisition and analysis methods of recordings from limited-channel ambulatory EMG devices for the assessment of sleep bruxism; and (b) to provide an overview of outcome measures used in sleep bruxism literature utilising such devices. Method: A scoping review of the literature was performed. Online databases PubMed and Semantics Scholar were searched for studies published in English until 7 October 2020. Data on five categories were extracted: recording hardware, recording logistics, signal acquisition, signal analysis and sleep bruxism outcomes. Results: Seventy-eight studies were included, published between 1977 and 2020. Recording hardware was generally well described. Reports of participant instructions in device handling and of dealing with failed recordings were often lacking. Basic elements of signal acquisition, for example amplifications factors, impedance and bandpass settings, and signal analysis, for example rectification, signal processing and additional filtering, were underreported. Extensive variability was found for thresholds used to characterise sleep bruxism events. Sleep bruxism outcomes varied, but typically represented frequency, duration and/or intensity of masticatory muscle activity (MMA). Conclusion: Adequate and standardised reporting of recording procedures is highly recommended. In future studies utilising ambulatory EMG devices, the focus may need to shift from the concept of scoring sleep bruxism events to that of scoring the whole spectrum of MMA

Generalizable Deep Learning-Based Sleep Staging Approach for Ambulatory Textile Electrode Headband Recordings

Publisher Copyright: © 2013 IEEE.Reliable, automated, and user-friendly solutions for the identification of sleep stages in home environment are needed in various clinical and scientific research settings. Previously we have shown that signals recorded with an easily applicable textile electrode headband (FocusBand, T 2 Green Pty Ltd) contain characteristics similar to the standard electrooculography (EOG, E1-M2). We hypothesize that the electroencephalographic (EEG) signals recorded using the textile electrode headband are similar enough with standard EOG in order to develop an automatic neural network-based sleep staging method that generalizes from diagnostic polysomnographic (PSG) data to ambulatory sleep recordings of textile electrode-based forehead EEG. Standard EOG signals together with manually annotated sleep stages from clinical PSG dataset (n = 876) were used to train, validate, and test a fully convolutional neural network (CNN). Furthermore, ambulatory sleep recordings including a standard set of gel-based electrodes and the textile electrode headband were conducted for 10 healthy volunteers at their homes to test the generalizability of the model. In the test set (n = 88) of the clinical dataset, the model's accuracy for 5-stage sleep stage classification was 80% (κ = 0.73) using only the single-channel EOG. The model generalized well for the headband-data, reaching 82% (κ = 0.75) overall sleep staging accuracy. In comparison, accuracy of the model was 87% (κ = 0.82) in home recordings using the standard EOG. In conclusion, the CNN model shows potential on automatic sleep staging of healthy individuals using a reusable electrode headband in a home environment.Peer reviewe

Automatic sleep staging of EEG signals: recent development, challenges, and future directions.

Modern deep learning holds a great potential to transform clinical studies of human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep-staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to provide the shared view of the authors on the most recent state-of-the-art developments in automatic sleep staging, the challenges that still need to be addressed, and the future directions needed for automatic sleep scoring to achieve clinical value