Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography

Fu, Mingyu; Wang, Yitian; Chen, Zixin; Li, Jin; Xu, Fengguo; Liu, Xinyu; Hou, Fengzhen

doi:10.3389/fphys.2021.628502

Cited by 40 publications

(26 citation statements)

References 60 publications

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“…To confirm the validity of the control sample, other studies used the same database [ 10 , 77 , 78 , 79 , 80 , 81 , 82 ], including with FM population [ 83 ]. Alterations in functional connectivity are frequent in patients with FM (Hargrove et al, 2010), in particular alterations in frontotemporal connectivity [ 62 , 65 , 84 ], and are often associated with a lower white matter volume than in controls in frontal regions [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…Pain leaves a footprint: experiencing chronic pain can cause anatomical and functional reorganization of the brain [ 8 ], as shown in several disorders such as phantom pain, chronic back pain, irritable bowel syndrome, and FM (May, 2008). The morphological changes include the loss of gray matter volume [ 9 ], whereas the functional alterations include aberrant functional activity [ 10 ]. Recent reviews of the long-term effects of chronic pain have demonstrated the appearance of a “brain signature” [ 7 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Fibromyalgia Detection Based on EEG Connectivity Patterns

Martín-Brufau

Gómez

Sánchez-Sánchez-Rojas

et al. 2021

JCM

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Objective: The identification of a complementary test to confirm the diagnosis of FM. The diagnosis of fibromyalgia (FM) is based on clinical features, but there is still no consensus, so patients and clinicians might benefit from such a test. Recent findings showed that pain lies in neuronal bases (pain matrices) and, in the long term, chronic pain modifies the activity and dynamics of brain structures. Our hypothesis is that patients with FM present lower levels of brain activity and therefore less connectivity than controls. Methods: We registered the resting state EEG of 23 patients with FM and compared them with 23 control subjects’ resting state recordings from the PhysioBank database. We measured frequency, amplitude, and functional connectivity, and conducted source localization (sLORETA). ROC analysis was performed on the resulting data. Results: We found significant differences in brain bioelectrical activity at rest in all analyzed bands between patients and controls, except for Delta. Subsequent source analysis provided connectivity values that depicted a distinct profile, with high discriminative capacity (between 91.3–100%) between the two groups. Conclusions: Patients with FM show a distinct neurophysiological pattern that fits with the clinical features of the disease.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fibromyalgia Detection Based on EEG Connectivity Patterns

Martín-Brufau

Gómez

Sánchez-Sánchez-Rojas

et al. 2021

JCM

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“…The achieved 79.7 % (κ = 0.73) scoring accuracy compares favorably to the recent advances in the deep learning-based automatic sleep scoring presented in the literature. Based on standard EEG recordings, several deep learning models with different architectures have achieved an accuracy of 82.9-86.2 % (κ = 0.77-0.80) [10], [15], [17], [21], [22] with single-channel inputs. Furthermore, a random forest classifier has been used to achieve 72.98 % accuracy based on a single frontal EEG channel [26].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

et al. 2022

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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 % (κ=0.729) for five sleep stages (W, N1, N2, N3, and R), 84.1 % (κ=0.773) for four sleep stages (W, light sleep, deep sleep, R), and 89.1 % (κ=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 AESbased sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in-home PSGs.

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“…It is also worth noting that electrode selection in single-channel-based automated staging is also an essential factor affecting the correct rate. Some studies have used F4-M1 channels [ 23 ], and others have used Pz-Oz channels, or Fpz-Cz channels, and staging based on prefrontal FP1 and FP2 channels [ 24 , 25 , 26 , 27 ]. Ghimatgar et al revealed that the results of sleep stage staging using Fpz-Cz EEG signals were more accurate than other channels [ 21 ].…”

Section: Inductionmentioning

confidence: 99%

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Zhao

Long

Wei

et al. 2022

IJERPH

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Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.

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Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography

Cited by 40 publications

References 60 publications

Fibromyalgia Detection Based on EEG Connectivity Patterns

Fibromyalgia Detection Based on EEG Connectivity Patterns

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

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