Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis

Shahin, Mostafa; Ahmed, Beena; Hamida, Sana Tmar-Ben; Mulaffer, Fathima Lamana; Glos, Martin; Penzel, Thomas

doi:10.1109/jbhi.2017.2650199

Cited by 71 publications

(33 citation statements)

References 29 publications

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“…With regards to sleep science, the impact of AI is multifaceted. First, it can aid clinicians in making sleep disorder diagnoses 94 . This is achieved by translating collected sensor data into predefined knowledge (e.g., class label), providing an inexpensive and objective alternative to manual sleep stage scoring 95 .…”

Section: Artificial Intelligence-based Sleep Modellingmentioning

confidence: 99%

The future of sleep health: a data-driven revolution in sleep science and medicine

et al. 2020

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In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-ofthe-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

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Section: Artificial Intelligence-based Sleep Modellingmentioning

confidence: 99%

The future of sleep health: a data-driven revolution in sleep science and medicine

et al. 2020

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“…The first principal component was then classified according to the set threshold. Shahin et al [14] extracted statistical, temporal and spectral features of EEG signals, and leveraged deep neural network (DNN) for automatic insomnia identification. Zhang et al [15] proposed an insomnia identification method based on temporal, spectral, nonlinear features and random forest (RF) classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have investigated the insomnia identification performance based on different sleep stages of EEG [13], [14]. HAMIDA et al [13] evaluated the performance of wake, S1, S2, SWS and REM stages, finding SWS epochs have the best insomnia identification performance.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Insomnia Based on Single-Channel EEG Labelled With Sleep Stage Annotations

Yang

Liu

2020

IEEE Access

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Monitoring single-channel EEG is a promising home-based approach for insomnia identification. Currently, many automatic sleep stage scoring approaches based on single-channel EEG have been developed, whereas few studies research on automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations. In this paper, we propose a one-dimensional convolutional neural network (1D-CNN) model for automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, and further investigate the identification performance based on different sleep stages EEG epochs. Single-channel EEG on 9 insomnia patients and 9 healthy subjects was used in this study. We constructed 4 subdatasets from EEG epochs based on the sleep stage annotations: All sleep stage dataset (ALL-DS), REM sleep stage dataset (REM-DS), light sleep stage dataset (LSS-DS), and SWS sleep stage dataset (SWS-DS). Subsequently, 4 subdatasets were fed into our 1D-CNN. We conducted experiments under intra-patient and inter-patient paradigms, respectively. Our experiments demonstrated that our 1D-CNN leveraging 3 subdatasets composed of REM, LSS and SWS epochs, respectively, achieved higher average accuracies in comparison with baseline methods under both intra-patient and inter-patient paradigms. The experimental results also indicated that amongst all the sleep stages, 1D-CNN leveraging REM and SWS epochs exhibited the best insomnia identification average accuracies in intra-patient paradigm, which are 98.98% and 99.16% respectively, whereas no statistically significant difference was found in inter-patient paradigm. For automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, 1D-CNN model introduced in this paper could achieve superior performance than traditional methods. INDEX TERMS Convolutional neural networks, insomnia, inter-patient paradigm, intra-patient paradigm, single-channel electroencephalogram (EEG), sleep stage, sleep data analysis.

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“…Moreover, the EEG is affected by the style of thinking, mood, and even the atmosphere so that it is a unique wave [6]. Benefiting from the deep learning (DL) techniques in various applications such as insomnia diagnosis, seizure detection, sleep studies, emotional recognition, and brain-computer interface (BCI) [7][8][9][10], the accuracy of the EEG-based ID recognition has been remarkably improved. In order to ensure that EEG can be used for the identification, most studies focus on the design of experimental paradigms.…”

Section: Introductionmentioning

confidence: 99%

DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography

Wang

et al. 2020

Computational and Mathematical Methods in Medicine

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In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security problems of conventional biometric systems. In the present study, a novel EEG-based identification system with different entropy and a continuous convolution neural network (CNN) classifier is proposed. The performance of the proposed method is experimentally evaluated through the emotional EEG data. The conducted experiment shows that the proposed method approaches the stunning accuracy (ACC) of 99.7% on average and can rapidly train and update the DE-CNN model. Then, the effects of different emotions and the impact of different time intervals on the identification performance are investigated. Obtained results show that different emotions affect the identification accuracy, where the negative and neutral mood EEG has a better robustness than positive emotions. For a video signal as the EEG stimulant, it is found that the proposed method with 0–75 Hz is more robust than a single band, while the 15–32 Hz band presents overfitting and reduces the accuracy of the cross-emotion test. It is concluded that time interval reduces the accuracy and the 15–32 Hz band has the best compatibility in terms of the attenuation.

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Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis

Cited by 71 publications

References 29 publications

The future of sleep health: a data-driven revolution in sleep science and medicine

The future of sleep health: a data-driven revolution in sleep science and medicine

Automatic Identification of Insomnia Based on Single-Channel EEG Labelled With Sleep Stage Annotations

DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography

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