Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images

Chriskos, Panteleimon; Frantzidιs, Christos A.; Gkivogkli, Polyxeni T.; Kourtidou‐Papadeli, Chrysoula

doi:10.1109/tnnls.2019.2899781

Cited by 40 publications

(23 citation statements)

References 51 publications

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“…Usually, the numbers of samples for each sleep stage are unbalanced. To date, several methods have been proposed to release this issue, including the classbalanced random sampling [122], data augmentation [130], class-balance training set design [28], and synthetic minority oversampling technique [131].…”

Section: E Sleep Stage Classificationmentioning

confidence: 99%

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Gong

Xing

Cichocki

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.

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Section: E Sleep Stage Classificationmentioning

confidence: 99%

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Gong

Xing

Cichocki

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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“…To compare the performance to that of conventional machine learning models, we trained random forest (RF) [38], gradient boost (GB) [39], and support vector machine (SVM) [40] with data including Time and combination of Time and IF. Given that conventional machine learning models are effective when the inputs are extracted features, we decomposed the EEG into five non-overlapping frequency ranges including delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-50 Hz), according to a conventional approach [41]. We extracted the power of each band in each of the 19 channels, namely band power (BP), and obtained a final input vector with a dimension of 90 by concatenating these values.…”

Section: Performance Comparison To Different Modelsmentioning

confidence: 99%

“…We proposed a slight modification of the fully end-to-end classifier based on the addition of frequency domain information to improve our performance. Instead of complex hand-engineered features, including time-frequency distributions [18], [27], entropy [23], and functional connectivity [20], we selected the IF which was simply obtained from time series, to maintain the nature of the end-to-end classifier as much as possible.…”

Section: A End-to-end Classifiermentioning

confidence: 99%

“…Many recent studies exploited frequency domain features in sleep staging, including PSD estimates, wavelet entropy, cross-frequency coupling, IF, and time-frequency distributions, with traditional machine learning-based [9], [11]- [15], [20] and DNN-based [18], [23] methods. For our HNN approach, we investigated the use of IF to maintain information based on the temporal variation instead of the PSD estimates, and to avoid computational complexity instead of the entropy, cross-frequency coupling, or time-frequency distributions.…”

Section: B Influences Of the Input Length Number Of Channels And Fmentioning

confidence: 99%

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Pediatric Sleep Stage Classification Using Multi-Domain Hybrid Neural Networks

et al. 2019

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Sleep staging is an important part of clinical neurology. However, it is still performed manually by technical experts and is labor-intensive and time-consuming. To overcome these obstacles in the manual sleep staging process, a large number of machine learning-based classifiers with hand-engineered features have been proposed. Additionally, combinations of a deep neural network (DNN) have been recently highlighted as the state-of-the-art classifiers in view of their effectiveness for automatic sleep staging. In spite of the existence of a large number of these types of classifiers, to-this-date, no prior DNN-based approach has attempted sleep-stage classification using pediatric electroencephalographic (EEG) signals. In this paper, we propose a novel end-to-end classifier based on a multi-domain hybrid neural network (HNNmulti) approach consisting of a convolutional neural network and bidirectional long short-term memory for automatic sleep staging with pediatric scalp EEG recordings. To find effective temporal, spatial, and domainspecific conditions, we investigated noticeable changes in the classification performance corresponding to: 1) the length of input signals; 2) the number of channels; and 3) the types of input signals in the time and frequency domains. Our HNN-based classifier yielded the best performance metrics using 30-s time series in combination with an instantaneous frequency using a 19-channel, three-stage classification, with overall accuracy, F1 score, and Cohen's Kappa, equal to 92.21%, 0.90, and 0.88, respectively. We suggest that an effective combination of temporal and spatial time-domain clues with time-varying frequency domain information plays a pivotal role in pediatric, automatic sleep staging. Sufficiently reasonable performance of our HNN-based approach coping with the highly complicated pediatric EEG signatures hopefully sheds light on the clinical feasibility of the DNN-based automatic sleep staging for pediatric neurology. INDEX TERMS Automatic sleep staging, deep learning, convolutional neural network, long short-term memory, instantaneous frequency features, pediatric electroencephalography.

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“…Recently, deep neural networks (DNNs) have been developed and have remarkably improved classification performances in various areas of study [22]. A DNN model using convolutional neural network (CNN) has been applied to the classification problem of sleep scoring based on EEG data [23,24] and electrocardiogram (ECG) data [25]. CNN has been, moreover, employed for activity recognition based on wrist worn accelerometer data [26], where CNN outperformed conventional machine learning algorithms such as SVM and LDA.…”

Section: Introductionmentioning

confidence: 99%

Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

et al. 2019

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Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.

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Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images

Cited by 40 publications

References 51 publications

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Pediatric Sleep Stage Classification Using Multi-Domain Hybrid Neural Networks

Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

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