Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks

Abstract

Study Objectives Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming. Methods A sleep staging classifier trained using deep learning methods scored PSG data from the Sleep Heart Health Study (SHHS). The training set was composed of 42 560 hours of PSG data from 5213 patients. To capture higher-order data, spectrograms were generated from electroencephalography, electrooculography, and electromyography data and then passed to the neural network. A holdout set of 580 PSGs not included in the training set was used to assess model accuracy and discrimination via weighted F1-score, per-stage accuracy, and Cohen’s kappa (K). Results The optimal neural network model was composed of spectrograms in the input layer feeding into convolutional neural network layers and a long short-term memory layer to achieve a weighted F1-score of 0.87 and K = 0.82. Conclusions The deep learning sleep stage classifier demonstrates excellent accuracy and agreement with expert sleep stage scoring, outperforming human agreement on sleep staging. It achieves comparable or better F1-scores, accuracy, and Cohen’s kappa compared to literature for automated sleep stage scoring of PSG epochs. Accurate automated scoring of other PSG events may eventually allow for fully automated PSG scoring.

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data

“…Therefore, the model design of this study considers the possibility of abnormal signal acquisition during overnight sleep PSG. Second, since there are transitional rules associated with the sleep staging, Markov models, CNNs, and RNNs have been used in recognition of sleep EEG in recent years [13][14][15][16][17]. This research innovatively applied the method of three-epoch splicing to simulate the technician recognition of EEG, so that if there is an epoch with atypical or severe interference, technicians could refer to the previous and following epochs of the EEG.…”

SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

“…Table V provides detailed comparison information about recent studies and the proposed method on the SHHS dataset. It shows that the proposed framework can achieve higher ACC and K using raw single-channel C4/A1 EEG compared to approaches using hand-crafted features as input [12] or multi-channel PSG data [27], [30] or the single-channel EEG [32], [33]. Similarly, Table VI demonstrates that the proposed model outperforms state-of-the-art methods on the Sleep-EDF dataset.…”

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…In voice-based analysis, AI is used to evaluate pathological voice conditions associated with vocal fold disorders, to analyze and decode phonation itself [67], to improve speech perception in noisy conditions, and to improve the hearing of pa-tients with CIs. In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [72,73,122] and audiometry [123,124]. AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4.…”

“…In voice-based analysis, AI is used to evaluate pathological voice conditions associated with vocal fold disorders, to analyze and decode phonation itself [ 67 ], to improve speech perception in noisy conditions, and to improve the hearing of patients with CIs. In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”

“…Of these 14 studies, most (50%, seven studies) focused on analyses of gene expression data. Three studies (21.43%) used AI to examine polysomnography data in an effort to score sleep stages [72,73] or to identify long-term cardiovascular disease [74]. Most algorithms employed ensemble learning (random forests, Gentle Boost, XGBoost, and a general linear model+support vector machine ensemble); this approach was followed by neural networkbased algorithms (convolutional neural networks, autoencoders, and shallow artificial neural networks).…”