Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)

Abstract

Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT) based on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages.

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

“…The LSTM model is an improvement from its predecessor methods known as RNN [16]. Just like its name suggests, the LSTM model attempts to mimic how the brain stores memories and makes predictions based on immediate past events stored in the memories [24].…”

“…Just like its name suggests, the LSTM model attempts to mimic how the brain stores memories and makes predictions based on immediate past events stored in the memories [24]. Both the RNN and the LSTM models are known for their ability to recognize patterns in sequential data [16]. However, the vanishing gradient has often been a very common problem in RNN models, where a large information gap exists between the new and old data, causing erroneous signals to vanish during the model's training phase.…”

“…These CAD tools can perform automated detection using the biomarkers of PD, such as Electroencephalogram (EEG) signals, posture analysis in the gait cycle, voice aberration, or brain imaging such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) [14]. In a conventional machine learning model, it is mandatory to extract the features from the biomarkers and then select the most salient features in order to train the model [15][16][17][18][19]. This is a required step because machine learning models by itself are not capable of learning the high dimensional data in their raw forms, otherwise, the model is likely to overfit the dataset [20].…”

“…This is a required step because machine learning models by itself are not capable of learning the high dimensional data in their raw forms, otherwise, the model is likely to overfit the dataset [20]. Also, the selection of the most relevant features must be carried out by an experienced expert system that is knowledgeable in terms of various feature selection tools [15,16]. This has led to the somewhat poor adoption of machine learning models as the future CAD tools as feature extraction and selection can be complicated procedures comprehensible by machine learning experts, but not so by the end-user of the CAD tool [21,22].…”

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

“…In literature, many studies are available on the macrostructure of sleep and researchers have developed models for automated classification of sleep stages using machine learning techniques and PSG [2][3][4][5][6][7][8][9]. Recently, deep learning-based methods have also been employed for sleep scoring [10][11][12].…”