Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram

Temporal complexity of EEG encodes human alertness

OBJECTIVE

Automatic human alertness monitoring has recently become an important research topic with important applications in many areas such as the detection of drivers' fatigue, monitoring of monotonous tasks that require a high level of alertness such as traffic control and nuclear power plant monitoring, and sleep staging. In this study, we propose that balanced dynamics of Electroencephalography (EEG) (so called EEG temporal complexity) is a potentially useful feature for identifying human alertness states. Recently, a new signal entropy measure, called Range Entropy (RangeEn), was proposed to overcome some limitations of two of the most widely used entropy measures, namely Approximate Entropy (ApEn) and Sample Entropy (SampEn), and showed its relevance for the study of time domain EEG complexity. In this paper, we investigated whether the RangeEn holds discriminating information associated with human alertness states, namely Awake, Drowsy, and Sleep and compare its performance against those of SampEn and ApEn.

APPROACH

We used EEG data from 60 healthy subjects of both sexes and different ages acquired during whole night sleeps. Using a 30-second sliding window, we computed the three entropy measures of EEG and performed statistical analyses to evaluate the ability of these entropy measures to discriminate among the different human alertness states.

MAIN RESULTS

Although the three entropy measures contained useful information about human alertness, RangeEn showed a higher discriminative capability compared to ApEn and SampEn especially when using EEG within the Beta frequency band.

SIGNIFICANCE

Our findings highlight the EEG temporal complexity evolution through the human alertness states. This relationship can potentially be exploited for the development of automatic human alertness monitoring systems and diagnostic tools for different neurological and sleep disorders, including insomnia.

Automated Annotation of Polysomnogram Epochs for Apnoea and Non-apnoea Arousals

A system for automated annotation of selected signals from the polysomnogram (PSG) for the presence of apnoea and non-apnoea arousals is presented. Fifty nine time- and frequency-domain features were derived from the PSG for each 15 second epoch and after combining features from adjacent epochs, the feature information was processed with a bank of feed-forward neural networks that provided a probability estimate that each epoch was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using volume under the receiver operator characteristic surface (VUROS) as well as no-arousal specificity and arousal sensitivities. Using a bank of ten feed-forward neural networks with each network processing ±4 epochs of features and each used a single hidden layer of 20 units, the system achieved a VUROS of 0.73 with a specificity of 70%, a sensitivity of 75% for the apnoea arousals, and a sensitivity of 70% for the non-apnoea arousals.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.