Assessment of recurrence quantification analysis (RQA) of EEG for development of a novel drowsiness detection system

Shabani, Hamed; Mikaili, Mohammad; Noori, Seyed Mohammad Reza

doi:10.1007/s13534-016-0223-5

Cited by 32 publications

(15 citation statements)

References 22 publications

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“…The prediction of drowsiness using EEG is a well-defined research topic. Approaches that utilize conventional EEG systems have advantages for the quantitative assessment of alertness levels, which requires expensive computational signal processing (Mard et al, 2011;Correa et al, 2014;Shabani et al, 2016;Zhang et al, 2017). Observing changes in the power spectra or spatiotemporal features of EEG frequency bands have commonly been used to detect subject drowsiness, but other methods have been investigated (Ayala Meza, 2017;Min et al, 2017;Majkowski et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

115

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Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ∼27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

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Section: Introductionmentioning

confidence: 99%

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

115

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“…These symptoms can potentially be detected by computer-aided-diagnosis systems based on advanced machine learning techniques. Machine learning techniques including linear discriminant analysis (LDA) [4], AdaBoost algorithm [5], k nearest-neighbors (k-NN) algorithm and Bayes classifier [6], regression trees (RT) [7], support vector machines (SVM) [8][9][10][11][12][13][14], decision trees (DT), naive Bayes (NB), and multilayer perceptron (MLP) [12] are widely employed in the design of medical decision support systems. Indeed, machine learning techniques have been commonly used in the design of computer-aided-diagnosis (CAD) systems with applications in classification of brain magnetic resonance images [15][16][17], mammograms [18,19], electroencephalography of seizures [20,21], retinal pathologies [22,23], electrocorticogram signals [24], heartbeat signals [25], and arrhythmias [26].…”

Section: Introductionmentioning

confidence: 99%

Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures

2017

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Parkinson's disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were considered and trained with a set of twenty-two voice disorder measurements to classify healthy and PD patients. These machine learning methods included linear discriminant analysis (LDA), k nearest-neighbors (k-NN), naïve Bayes (NB), regression trees (RT), radial basis function neural networks (RBFNN), support vector machine (SVM), and Mahalanobis distance classifier. We evaluated the performance of these methods by means of a tenfold cross validation protocol. Experimental results show that the SVM classifier achieved higher average performance than all other classifiers in terms of overall accuracy, G-mean, and area under the curve of the receiver operating characteristic plot. The SVM classifier achieved higher performance measures than the majority of the other classifiers also in terms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest average precision. The RBFNN method yielded the highest F-measure.; however, it performed poorly in terms of other performance metrics. Finally, t tests were performed to evaluate statistical significance of the results, confirming that the SVM outperformed most of the other classifiers on the majority of performance measures. SVM is a promising method for identifying PD patients based on classification of dysphonia measurements.

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“…As an alternative or complement to vehicle-based or behavioral measures, physiological measures are currently actively used [2,10], and they contain various biometrical signals (such as heart rate, brain activity, respiration, etc.) acquired from different types of sensors (such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), etc.).…”

Section: Introductionmentioning

confidence: 99%

“…Such traits are simple and easy to calculate, but are not good enough to capture nonlinear dynamics of complex systems. To handle nonlinear characteristics of physiological signals like ECG that tend to be nonstationary in nature [10,12,19,20], recurrence plots (RPs) were investigated in several studies [21][22][23]. The RPs were originally introduced by Eckmann et al [21], and represent the phase-space trajectory of a dynamical system.…”

Section: Introductionmentioning

confidence: 99%

Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots

Lee

Shin

2019

Electronics

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This paper aims to investigate the robust and distinguishable pattern of heart rate variability (HRV) signals, acquired from wearable electrocardiogram (ECG) or photoplethysmogram (PPG) sensors, for driver drowsiness detection. As wearable sensors are so vulnerable to slight movement, they often produce more noise in signals. Thus, from noisy HRV signals, we need to find good traits that differentiate well between drowsy and awake states. To this end, we explored three types of recurrence plots (RPs) generated from the R–R intervals (RRIs) of heartbeats: Bin-RP, Cont-RP, and ReLU-RP. Here Bin-RP is a binary recurrence plot, Cont-RP is a continuous recurrence plot, and ReLU-RP is a thresholded recurrence plot obtained by filtering Cont-RP with a modified rectified linear unit (ReLU) function. By utilizing each of these RPs as input features to a convolutional neural network (CNN), we examined their usefulness for drowsy/awake classification. For experiments, we collected RRIs at drowsy and awake conditions with an ECG sensor of the Polar H7 strap and a PPG sensor of the Microsoft (MS) band 2 in a virtual driving environment. The results showed that ReLU-RP is the most distinct and reliable pattern for drowsiness detection, regardless of sensor types (i.e., ECG or PPG). In particular, the ReLU-RP based CNN models showed their superiority to other conventional models, providing approximately 6–17% better accuracy for ECG and 4–14% for PPG in drowsy/awake classification.

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Assessment of recurrence quantification analysis (RQA) of EEG for development of a novel drowsiness detection system

Cited by 32 publications

References 22 publications

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures

Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots

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