Sleep scoring using artificial neural networks

Ronzhina, Marina; Janoušek, Oto; Kolářová, Jana; Novàkovâ, Marie; Honzík, Petr; Provazník, Ivo

doi:10.1016/j.smrv.2011.06.003

Cited by 199 publications

(138 citation statements)

References 62 publications

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“…These stages, based on AASM classification [1] are, Wake, N1, N2, N3 and REM. Analysis of these overnight recordings and their classification is a tedious task [2] making their automation highly desirable. This would not only save time and costs associated with sleep testing but also make the tests more accessible to a larger population.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the algorithms proposed recently include the use of support vector machines [4]- [6], hidden Markov models [7] and frequency coupling with linear discriminant analysis classification [8]. Other methods have also used artificial neural networks [2], [9], [10] and decision trees [11], [12] for classification with a variety of time, frequency, entropy [13], [14] and wavelet [10] sleep data either recorded by the researchers or using signals available from public sleep databases. It is generally difficult to compare the performance of algorithms that have been evaluated using different databases.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic sleep staging using state machine-controlled decision trees

Imtiaz¹,

Rodriguez‐Villegas²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic sleep staging using state machine-controlled decision trees

Imtiaz¹,

Rodriguez‐Villegas²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…Namely, they require user intervention or manual data annotation. Among these techniques, neural networks (NN) [14] yield very good results, with up to 80% classification accuracy [14,15]. Other approaches have achieved 95% accuracy in more specific applications, such as differentiating alert states from drowsy and sleep states.…”

Section: Introductionmentioning

confidence: 99%

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

106

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Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

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“…Accordingly, [1,[6][7][8]. Different parametric and nonparametric methods have been applied in the classification process such as random forest classifiers, artificial neural networks (ANN), fuzzy logic, the nearest neighbour, linear discriminant analysis (LDA,) support vector machine (SVM) and kernel logistic regression (KLR) [6][7][8][9][10][11][12]. Classification accuracies vary widely among the ASSC methods reported in scientific literature.…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

Khalighi

Sousa

Pires

et al. 2013

Expert Systems with Applications

118

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To improve applicability of automatic sleep staging an efficient subjectindependent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess the method, polysomnographic (PSG) records of 40 patients from our ISRUC-Sleep dataset, which was scored by an expert clinician in the central hospital of Coimbra, are used. To find the best combination of PSG signals for automatic sleep staging, six electroencephalographic (EEG), two electrooculographic (EOG), and one electromyographic (EMG) channels are analyzed. An extensive set of feature extraction techniques are applied, covering temporal, frequency and timefrequency domains. The maximum overlap wavelet transform (MODWT), a shift invariant transform, was used to extract the features in time-frequency domain. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. The most discriminative features are selected through a two-step method composed by a manual selection step based on features' histogram analysis followed by an automatic feature selector. The selected feature set is classified using support vector machines (SVMs). The system achieved the best performance by combining 6 channels (C3, C4, O1, left EOG (LOC), right EOG (ROC) and chin EMG (X1)) for sleep-wake detection, and 9 channels (C3, C4, O1, O2, F3, F4, LOC, ROC, X1) for multiclass sleep staging.

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Sleep scoring using artificial neural networks

Cited by 199 publications

References 62 publications

Automatic sleep staging using state machine-controlled decision trees

Automatic sleep staging using state machine-controlled decision trees

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

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