Towards a Deeper Understanding of Sleep Stages through their Representation in the Latent Space of Variational Autoencoders

Artificial neural networks show great success in sleep stage classification, with an accuracy comparable to human scoring. While their ability to learn from labelled electroencephalography (EEG) signals is widely researched, the underlying learning processes remain unexplored. Variational autoencoders can capture the underlying meaning of data by encoding it into a low-dimensional space. Regularizing this space furthermore enables the generation of realistic representations of data from latent space samples. We aimed to show that this model is able to generate realistic sleep EEG. In addition, the generated sequences from different areas of the latent space are shown to have inherent meaning. The current results show the potential of variational autoencoders in understanding sleep EEG data from the perspective of unsupervised machine learning

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

Multi-centre arousal scoring agreement in the Sleep Revolution

We investigated arousal scoring agreement within full-night polysomnography in a multi-centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs). Moreover, kappa statistics were used to evaluate the second-by-second agreement in whole recordings and in different sleep stages. Finally, arousal clusters, that is, periods with overlapping arousals by multiple scorers, were extracted. The overall similarity of the ArIs was fair (ICC = 0.41), varying from poor to excellent between the scorer pairs (ICC = 0.04-0.88). The ArI similarity was better in respiratory (ICC = 0.65) compared with spontaneous (ICC = 0.23) arousals. The overall second-by-second agreement was fair (Fleiss' kappa = 0.40), varying from poor to substantial depending on the scorer pair (Cohen's kappa = 0.07-0.68). Fleiss' kappa increased from light to deep sleep (0.45, 0.45, and 0.53 for stages N1, N2, and N3, respectively), was moderate in the rapid eye movement stage (0.48), and the lowest in the wake stage (0.25). Over a half of the arousal clusters were scored by one or two scorers, and less than a third by at least five scorers. In conclusion, the scoring agreement varied depending on the arousal type, sleep stage, and scorer pair, but was overall relatively low. The most uncertain areas were related to spontaneous arousals and arousals scored in the wake stage. These results indicate that manual arousal scoring is generally not reliable, and that changes are needed in the assessment of sleep fragmentation for clinical and research purposes.Peer reviewe

Morbid obesity influences the nocturnal electrocardiogram wave and interval durations among suspected sleep apnea patients

Background: Obesity is a global issue with a major impact on cardiovascular health. This study explores how obesity influences nocturnal cardiac electrophysiology in suspected obstructive sleep apnea (OSA) patients. Methods: We randomly selected 12 patients from each of the five World Health Organization body mass index (BMI) classifications groups (ntotal = 60) while keeping the group's age and sex matched. We evaluated 1965 nocturnal electrocardiography (ECG) samples (10 s) using modified lead II recorded during normal saturation conditions. R-wave peaks were detected and confirmed using dedicated software, with the exclusion of ventricular extrasystoles and artifacts. The duration of waves and intervals was manually marked. The average electric potential graphs were computed for each segment. Thresholds for abnormal ECG waveforms were P-wave > 120 ms, PQ interval > 200 ms, QRS complex > 120 ms for, and QTc > 440 ms. Results: Obesity was significantly (p <.05) associated with prolonged conduction times. Compared to the normal weight (18.5 ≤ BMI < 25) group, the morbidly obese patients (BMI ≥ 40) had a significantly longer P-wave duration (101.7 vs. 117.2 ms), PQ interval (175.8 vs. 198.0 ms), QRS interval (89.9 vs. 97.7 ms), and QTc interval (402.8 vs. 421.2 ms). We further examined ECG waveform prolongations related to BMI. Compared to other patient groups, the morbidly obese patients had the highest number of ECG segments with PQ interval (44% of the ECG samples), QRS duration (14%), and QTc duration (20%) above the normal limits. Conclusions: Morbid obesity predisposes patients to prolongation of cardiac conduction times. This might increase the risk of arrhythmias, stroke, and even sudden cardiac death.Peer reviewe

Review and perspective on sleep-disordered breathing research and translation to clinics

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.Peer reviewe

Mortality-risk-based apnea–hypopnea index thresholds for diagnostics of obstructive sleep apnea

The severity of obstructive sleep apnea is clinically assessed mainly using the apnea–hypopnea index. Based on the apnea–hypopnea index, patients are classified into four severity groups: non-obstructive sleep apnea (apnea–hypopnea index\ua0< 5); mild (5 ≤ apnea–hypopnea index < 15); moderate (15 ≤ apnea–hypopnea index < 30); and severe obstructive sleep apnea (apnea–hypopnea index ≥ 30). However, these thresholds lack solid clinical and scientific evidence. We hypothesize that the current apnea–hypopnea index thresholds are not optimal despite their global use, and aim to assess this clinical shortcoming by optimizing the thresholds with respect to the risk of all-cause mortality. We analysed ambulatory polygraphic recordings of 1,783 patients with suspected obstructive sleep apnea (mean follow-up 18.3 years). We simulated 79,079 different threshold combinations in 100 randomized subgroups of the population and studied the relative risk of all-cause mortality corresponding to each combination and randomization. The optimal thresholds were chosen according to three criteria: (a) the hazard ratios increase linearly between severity groups towards more severe obstructive sleep apnea; (b) each group includes at least 15% of the study population; (c) group sizes decrease with increasing obstructive sleep apnea severity. The risk of all-cause mortality varied greatly across simulations; the threshold defining non-obstructive sleep apnea group having the largest effect on the hazard ratios. The apnea–hypopnea index threshold combination of 3-9-24 was optimal in most of the subgroups. In conclusion, the assessment of obstructive sleep apnea severity based on the current apnea–hypopnea index thresholds is not optimal. Our novel approach provides methods for optimizing apnea–hypopnea index-based severity classification, and the revised thresholds better differentiate patients into severity groups, ensuring that an increase in the severity corresponds to an increase in the risk of all-cause mortality

Automatic oxygen saturation signal analysis software

Automatic blood oxygen saturation signal analysis software. This software automatically detects desaturation and following recovery events from the blood oxygen saturation signal. The software calculates numerous event-specific metrics for each scored event. From these metrics, several parameters are calculated. These parameters have been shown to be very useful, for example, in the severity assessment and phenotyping of sleep apnea. The user can decide how the desaturation events are scored by selecting the minimum event duration and minimum transient drop criteria for the desaturations. In addition, (optionally) the user can input analysis start/end times and/or hypnograms for each recording. These can be used to define the total analysis time used in parameter calculations. Software takes .EDF files as an input from which the oxygen saturation signals are retrieved. The software enables batch-like analysis, i.e. all .EDF files in the input folder are analysed in one go. Software is programmed with MATLAB (version 2021b). However, the user does not need to have MATLAB to use the software. Before the first use, MATLAB runtime is installed, if the computer does not have it installed. Publication describing the structure and validation of the ABOSA software (version 1.1) has been published in the Computer Methods and Programs in Biomedicine -journal with title "ABOSA - Freely available automatic blood oxygen saturation signal analysis software: Structure and validation", DOI: 10.1016/j.cmpb.2022.107120. In case ABOSA is used in the scientific publication, the above mentioned article should be cited, in addition to this Zenodo page. Software is intended for research use only.This work was funded by the European Union's Horizon 2020 research and innovation programme (grant agreement no. 965417), Nordforsk (NordSleep project 90458) via Business Finland (5133/31/2018), the Academy of Finland (323536), the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (projects 5041767, 5041770, 5041787, 5041794, and 5041797), Instrumentarium Science Foundation, Tampere Tuberculosis Foundation, and The Finnish Cultural Foundation – North Savo Regional Fund

Author Correction: Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea (Scientific Reports, (2019), 9, 1, (13200), 10.1038/s41598-019-49330-7)

This Article incorrectly states that informed consent was obtained. Consent for research was not sought, because under Finnish law (Law on medical research 2 § (23.4.2004/295)) the need for consent is not required for retrospective chart reviews

Automatic Respiratory Event Scoring in Obstructive Sleep Apnea Using a Long Short-Term Memory Neural Network

The diagnosis of obstructive sleep apnea is based on daytime symptoms and the frequency of respiratory events during the night. The respiratory events are scored manually from polysomnographic recordings, which is time-consuming and expensive. Therefore, automatic scoring methods could considerably improve the efficiency of sleep apnea diagnostics and release the resources currently needed for manual scoring to other areas of sleep medicine. In this study, we trained a long short-term memory neural network for automatic scoring of respiratory events using input signals from peripheral blood oxygen saturation, thermistor-airflow, nasal pressure -airflow, and thorax respiratory effort. The signals were extracted from 887 in-lab polysomnography recordings. 787 patients with suspected sleep apnea were used to train the neural network and 100 patients were used as an independent test set. The epoch-wise agreement between manual and automatic neural network scoring was high (88.9%, κ = 0.728). In addition, the apnea-hypopnea index (AHI) calculated from the automated scoring was close to the manually determined AHI with a mean absolute error of 3.0 events/hour and an intraclass correlation coefficient of 0.985. The neural network approach for automatic scoring of respiratory events achieved high accuracy and good agreement with manual scoring. The presented neural network could be used for analysis of large research datasets that are unfeasible to score manually, and has potential for clinical use in the future In addition, since the neural network scores individual respiratory events, the automatic scoring can be easily reviewed manually if desired