Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea

Abstract

The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n = 153) and from a clinical dataset (n = 891) of patients with

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea

“…In a clinical dataset of patients with suspected OSA, the developed method reached at least similar inter-rater reliability (83.8% accuracy, κ = 0.78) [16] as between two manual scorers [20][21][22]. The deep learning-based sleep staging also succeeded in accurately identifying sleep stages from a single EEG channel [16] or even from a photoplethysmography signal [17]. However, the main advantage of the automatic sleep staging over manual scoring is the ability to always score the sleep stages consistently.…”

“…We have previously presented a deep learning-based automatic sleep staging model utilizing a clinical dataset of Type 1 polysomnographies (PSGs) [16]. The PSGs were conducted at the Princess Alexandra Hospital (Brisbane, Australia) for the clinical suspicion of OSA and recorded with the Compumedics Grael acquisition system (Compumedics, Abbotsford, Australia) between 2015 and 2017.…”

“…Therefore, we hypothesize that the current sleep staging with non-overlapping 30-second epochs heavily underestimates the extent of sleep fragmentation in patients suffering from OSA. As deep learning-based methods have demonstrated remarkable accuracy in automatic sleep staging [12][13][14][15][16][17], we hypothesize that deep learning offers a unique possibility for providing a more feasible and accurate representation of sleep architecture beyond the non-overlapping 30-second epochs.…”

“…We have recently introduced a deep learning-based automatic sleep staging method [16] that surpassed previous state-of-the-art methods on a publicly available research dataset (Physionet Sleep-EDF [18,19]). In a clinical dataset of patients with suspected OSA, the developed method reached at least similar inter-rater reliability (83.8% accuracy, κ = 0.78) [16] as between two manual scorers [20][21][22]. The deep learning-based sleep staging also succeeded in accurately identifying sleep stages from a single EEG channel [16] or even from a photoplethysmography signal [17].…”

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

“…It can not only process single data points (such as images), but also entire sequences of data (such as speech or EEG signal). Korkalainen et al used a combined convolutional and LSTM neural network on the public database and achieved sleep staging accuracy of 83.7% with a single frontal EEG channel [ 16 ]. Michielli et al proposed a novel cascaded RNN architecture based on LSTM for automated scoring of sleep stages on single-channel EEG signals [ 17 ].…”