A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates

Dimitriadis, Stavros I.; Salis, Christos; Linden, David

doi:10.1016/j.clinph.2017.12.039

Cited by 55 publications

(43 citation statements)

References 92 publications

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“…[22], or even entire recordings, such as in Dimitriadis et al. [55] and in Alickovic & Subasi [13], into their experiments. These add-on data, which are mainly Wake epochs, often boost the performance as Wake , in general, is easier to be recognized than other sleep stages.…”

Section: Methodsmentioning

confidence: 99%

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

Phan

Andreotti

Cooray

et al. 2019

IEEE Trans. Biomed. Eng.

342

232

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Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs’ labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

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Section: Methodsmentioning

confidence: 99%

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

Phan

Andreotti

Cooray

et al. 2019

IEEE Trans. Biomed. Eng.

342

232

View full text Add to dashboard Cite

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“…Sleep Research has been studied in this way by performing Polysomnographic recordings (PSG) [ 22 , 23 ]. The different sleep stages are evaluated by visually marking waveforms or graphoelements in long-running electroencephalographic recordings, looking for patterns based on standardized guidelines [ 24 ]. Visual characterization includes the identification or classification of certain waveform components based on a subjective characterization (e.g., positive or negative peak polarity) or the location within the strip.…”

Section: Electroencephalographymentioning

confidence: 99%

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018

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The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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“…Dimitriadis SI et al proposed a single-EEG-sensor automatic sleep stage classification technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling [30]. A high classification performance was achieved (accuracy was 94.4% across 20 folds), the recordings for evaluating classification performance were taken from 20 healthy young adults.…”

Section: Introductionmentioning

confidence: 99%

“…Aboalayon K. et al proposed a novel and efficient technique to identify sleep stages using new statistical features applied to the single-channel EEG signal [31]. They used the same dataset in Dimitriadis et al [30], but less data were included. Zhang et al proposed an automatic sleep stage method that combined a sparse deep belief net with multiple classifiers [32].…”

Section: Introductionmentioning

confidence: 99%

Development of a human-computer collaborative sleep scoring system for polysomnography recordings

et al. 2019

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The overnight polysomnographic (PSG) recordings of patients were scored by an expert to diagnose sleep disorders. Visual sleep scoring is a time-consuming and subjective process. Automatic sleep staging methods can help; however, the mechanism and reliability of these methods are not fully understood. Therefore, experts often need to rescore the recordings to obtain reliable results. Here, we propose a human-computer collaborative sleep scoring system. It is a rule-based automatic sleep scoring method that follows the American Academy of Sleep Medicine (AASM) guidelines to perform an initial scoring. Then, the reliability level of each epoch is analyzed based on physiological patterns during sleep and the characteristics of various stage changes. Finally, experts would only need to rescore epochs with a low-reliability level. The experimental results show that the average agreement rate between our system and fully manual scorings can reach 90.42% with a kappa coefficient of 0.85. Over 50% of the manual scoring time can be reduced. Due to the demonstrated robustness and applicability, the proposed approach can be integrated with various PSG systems or automatic sleep scoring methods for sleep monitoring in clinical or homecare applications in the future.

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A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates

Abstract: Single-sensor ASSC makes the entire methodology appropriate for longitudinal monitoring using wearable EEG in real-world and laboratory-oriented environments.

Cited by 55 publications

References 92 publications

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

Development of a human-computer collaborative sleep scoring system for polysomnography recordings

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