Application of random forest classifier for automatic sleep spindle detection

Patti, Chanakya Reddy; Shahrbabaki, Sobhan Salari; Dissanayaka, Chamila; Cvetković, Dean

doi:10.1109/biocas.2015.7348373

Cited by 11 publications

(15 citation statements)

References 16 publications

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“…Apart from the decision fusion strategy, fundamental properties of the classifiers (like the optimization of the classifiers), also play a major role in the performance. For instance, multiple trees in RF classifier enhances the performance and decreases the chance of over-fitting of the data [41,54]. A similar observation has been made in our study.…”

Section: Discussionsupporting

confidence: 87%

Classification of brain signal (EEG) induced by shape-analogous letter perception

Bose

Goh

Wong

et al. 2019

Advanced Engineering Informatics

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Visual perception of English letters involves different underlying brain processes including brain activity alteration in multiple frequency bands. However, shape analogous letters elicit brain activities which are not obviously distinct and it is therefore difficult to differentiate those activities. In order to address discriminative feasibility and classification performance of the perception of shape-analogous letters, we performed an experiment in where EEG signals were obtained from 20 subjects while they were perceiving shape analogous letters (i.e., 'p', 'q', 'b', and 'd'). Spectral power densities from five typical frequency bands (i.e., delta, theta, alpha, beta and gamma) were extracted as features, which were then classified by either individual widely-used classifiers, namely k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA), or an ensemble of some of them. The F-score was employed to select most discriminative features so that the dimension of features was reduced. The results showed that the RF achieved the highest accuracy of 74.1% in the case of multi-class classification. In the case of binary classification, the best performance (Accuracy 86.39%) was achieved by the RF classifier in terms of average accuracy across all possible pairs of the letters. In addition, we employed decision fusion strategy to exert complementary strengths of different classifiers. The results demonstrated that the performance was elevated from 74.10% to 76.63% for the multi-class classification and from 86.39% to 88.08% for the binary class classification.

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Section: Discussionsupporting

confidence: 87%

Classification of brain signal (EEG) induced by shape-analogous letter perception

Bose

Goh

Wong

et al. 2019

Advanced Engineering Informatics

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“…We further compared our results with detectors that were applied on the same database, but using slightly different methods (e.g., TP, TN, FP and FN determined on time windows instead of time samples) or using a sub-sample of subjects (e.g., excluding, for the computation of test statistics, subjects that were used for training the detector). This second set included the eight detectors A1–A8 that were tested in Tsanas and Clifford ( 2015 ), a detector based on complex demodulation (CD; Ray et al, 2015 ) and two detectors using random forest (RF) and ANN that were assessed in Patti et al ( 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS)

Lajnef

O’Reilly

Combrisson³

et al. 2016

Front. Neuroinform.

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Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O’Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew’s coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.

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“…Features in sub-frequency bands (11): Sigma index, alpha band ratio and spindle band ratio were calculated based on prior work done by Patti et al [11]. In addition, the following features were also selected to provide information in the sub-frequency bands: mean absolute amplitudes and Hilbert mean envelope amplitude in sigma band; mean absolute amplitudes and Hilbert mean envelope amplitude in spindle band; relative and absolute band power in sigma band; and relative and absolute band power in spindle band.…”

Section: Features Extractionmentioning

confidence: 99%

“…Several classification algorithms were tested for spindle detection during this study, including neural networks and support vector machine techniques [11], [13], [14]. Although these methods have achieved success in many classification problems the random forest classifier had the best performance in this study.…”

Section: F Classification Algorithmsmentioning

confidence: 99%

Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG

Wei

Ventura

Lowery

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 exterm), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.

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Application of random forest classifier for automatic sleep spindle detection

Cited by 11 publications

References 16 publications

Classification of brain signal (EEG) induced by shape-analogous letter perception

Classification of brain signal (EEG) induced by shape-analogous letter perception

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS)

Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG

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