Improved A-phase Detection of Cyclic Alternating Pattern Using Deep Learning

Hartmann, Simon; Baumert, Mathias

doi:10.1109/embc.2019.8857006

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…A total of 61% of the subjects are men (66 people), and 38% are women (42 people). Most of the studies conducted using the CAP sleep database are on cyclic phase detection [ 30 , 31 , 32 , 33 , 34 , 35 ]. There are many studies on sleep stage detection using other datasets but there is no study available in literature on sleep stage classification using the CAP sleep database.…”

Section: Materials Usedmentioning

confidence: 99%

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Sharma

Tiwari

Acharya

2021

IJERPH

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Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

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Section: Materials Usedmentioning

confidence: 99%

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Sharma

Tiwari

Acharya

2021

IJERPH

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“…A similar approach was employed by Mendonça et al [16], feeding the EEG signal to an LSTM. Hartmann and Baumert [19] have also used an LSTM to perform the classification, fed with entropy-based features, TEO, differential variance, and frequency-based features.…”

Section: State-of-the-artmentioning

confidence: 99%

“…It was reported by Mendonça et al [18] that the deep learning models have difficulties recognizing the relevant patterns for two of the three subtypes, which compose the A phases, suggesting the need for examining feature-based methods in this work. Specifically, the LSTM was examined since it was identified as a suitable classifier for feature-based analysis with temporal dependencies [19]. The Feed-Forward Neural Network (FFNN) was also tested as it was identified in the state-of-the-art as possibly the best conventional classifier for A phase estimation, working as a benchmark for the other examined classifiers [20].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

Mendonça

Mostafa

Freitas

et al. 2022

Entropy

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Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.

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“…The CAP Sleep Database on PhysioNet [ 24 ] is widely used in scientific work on sleep staging, and most of the published studies use the CAP database to establish the sleep phase [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Table 1 summarizes selected studies on sleep stage detection using different datasets.…”

Section: Introductionmentioning

confidence: 99%

L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets

et al. 2022

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Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.

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Improved A-phase Detection of Cyclic Alternating Pattern Using Deep Learning

Cited by 10 publications

References 18 publications

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets

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