A simple and robust method for the automatic scoring of EEG arousals in polysomnographic recordings

Fernández-Varela, Isaac; Álvarez-Estévez, Diego; Hernández-Pereira, Elena; Moret‐Bonillo, Vicente

doi:10.1016/j.compbiomed.2017.05.011

Cited by 21 publications

(33 citation statements)

References 23 publications

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“…HMC-22 was used for the validation of an earlier version of our algorithm and it is composed of 22 in-hospital PSG recordings gathered from the HMC database. More detailed description of the dataset and the related validation process can be found in [16]. The SHHS1-26 dataset, on the other hand, is composed of 26 ambulatory PSG recordings gathered from the SHHS-1 study.…”

Section: Methodsmentioning

confidence: 99%

“…The current version of the EEG arousals detection algorithm is largely based on a previous version which was described and validated elsewhere [16]. The current updated version is an evolution, although it preserves the same original philosophy of simplicity and robustness.…”

Section: Algorithm Overviewmentioning

confidence: 99%

“…The use of an additional ECG channel for the removal of ECG artifacts is optional. The method for ECG artifact removal is based on adaptive filtering and it has been described in detail elsewhere [28] [16].…”

Section: Algorithm Overviewmentioning

confidence: 99%

“…Manual visual examination of the entire PSG for the scoring of these events is costly, due to the complexity and the amount of data involved. Given this context several works have explored the possibility of developing automatic analysis software to help the clinician during the scoring process [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16].…”

Section: Introductionmentioning

confidence: 99%

“…In a recent work [16], we have presented a preliminary version of a method for automatic EEG arousal detection, obtaining good validation results on a controlled dataset of 22 PSG in-hospital recordings. Building upon this initial version, an updated algorithm has been developed and set it up to work in the clinical environment, where clinicians can choose whether to use it, or not, as a supportive scoring tool while reviewing their night recordings.…”

Section: Introductionmentioning

confidence: 99%

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Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases

Álvarez-Estévez

Fernández-Varela

2019

Sleep Medicine

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Objective: To assess the validity of an automatic EEG arousal detection algorithm using large patient samples and different heterogeneous databases Methods: Automatic scorings were confronted with results from human expert scorers on a total of 2768 full-night PSG recordings obtained from two different databases. Of them, 472 recordings were obtained during clinical routine at our sleep center, and were subdivided into two subgroups of 220 (HMC-S) and 252 (HMC-M) recordings each, attending to the procedure followed by the clinical expert during the visual review (semi-automatic or purely manual, respectively). In addition, 2296 recordings from the public SHHS-2 database were evaluated against the respective manual expert scorings. Results: Event-by-event epoch-based validation resulted in an overall Cohen's kappa agreement κ = 0.600 (HMC-S), 0.559 (HMC-M), and 0.573 (SHHS2). Estimated inter-scorer variability on the datasets was, respectively, κ = 0.594, 0.561 and 0.543. Analyses of the corresponding Arousal Index scores showed associated automatic-human repeatability indices ranging in 0.693-0.771 (HMC-S), 0.646-0.791 (HMC-M), and 0.759-0.791 (SHHS2).Conclusions: Large-scale validation of our automatic EEG arousal detector on different databases has shown robust performance and good generalization results comparable to the expected levels of human agreement. Special emphasis has been put on allowing reproducibility of the results and implementation of our method has been made accessible online as open source code.

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Section: Methodsmentioning

confidence: 99%

Section: Algorithm Overviewmentioning

confidence: 99%

Section: Algorithm Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases

Álvarez-Estévez

Fernández-Varela

2019

Sleep Medicine

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Deep convolutional architecture‐based hybrid learning for sleep arousal events detection through single‐lead EEG signals

et al. 2023

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Introduction: Detecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression.Methods: An efficient hybrid deep learning method to identify and evaluate arousal events is presented in this paper using only single-lead electroencephalography (EEG) signals for the first time. Using the proposed architecture, which incorporates Inception-ResNet-v2 learning transfer models and optimized support vector machine (SVM) with the radial basis function (RBF) kernel, it is possible to classify with a minimum error level of less than 8%. In addition to maintaining accuracy, the Inception module and ResNet have led to significant reductions in computational complexity for the detection of arousal events in EEG signals. Moreover, in order to improve the classification performance of the SVM, the grey wolf algorithm (GWO) has optimized its kernel parameters.Results: This method has been validated using pre-processed samples from the 2018 Challenge Physiobank sleep dataset. In addition to reducing computational complexity, the results of this method show that different parts of feature extraction and classification are effective at identifying sleep disorders. The proposed model detects sleep arousal events with an average accuracy of 93.82%. With the lead present in the identification, the method becomes less aggressive in recording people's EEG signals. Conclusion:According to this study, the suggested strategy is effective in detecting arousals in sleep disorder clinical trials and may be used in sleep disorder detection clinics.

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On The Automation of Medical Knowledge and Medical Decision Support Systems

Moret‐Bonillo

Fernández-Varela

Hernández-Pereira

et al. 2017

Advances in Biomedical Informatics

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A simple and robust method for the automatic scoring of EEG arousals in polysomnographic recordings

Cited by 21 publications

References 23 publications

Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases

Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases

Deep convolutional architecture‐based hybrid learning for sleep arousal events detection through single‐lead EEG signals

On The Automation of Medical Knowledge and Medical Decision Support Systems

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