An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme

Chui, Kwok Tai; Tsang, Kim Fung; Ran, Hao; Ling, Bo; Wu, Chung Kit

doi:10.1109/tii.2016.2573259

Cited by 117 publications

(46 citation statements)

References 26 publications

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“…Through 10-fold cross-validation (CV) method partitioned the ℶ ′ into 10 disjoint sets (ℶ 1 , ℶ 2 ,…, ℶ 10 ) of equal size. NB and SVM classifiers learned from the training dataset as (ℶ ′ -ℶ ′ v) and then learned model is ready to classify the instances in testing dataset as ℶ ′ v by applying the rule in Equations (6) to (7). This process is repeated 10 times so that each set ℶ ′ v is used once as the test set.…”

Section: Nb and Svm As Fitness Functionmentioning

confidence: 99%

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.

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Section: Nb and Svm As Fitness Functionmentioning

confidence: 99%

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Shukla¹

2019

Computational Intelligence

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“…ECG signals are widely utilized to predict arrhythmia, coronary artery disease, and paroxysmal atrial fibrillation [15][16][17]. Chui et al [18] and Sahayadhas et al [19] considered ECG signals for estimating the drowsiness condition by extracting heart rate variability (HRV) information, which includes timeand frequency-domain analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

et al. 2020

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A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-network (DQN) algorithm was designed, using conventional feature engineering and deep convolutional neural network (CNN) methods, to extract the optimal features. The DQN yielded the optimal features: two CNN features from ECG and two conventional features from EEG. The ECG features were more significant for tracking the transitions within the alertness continuum with the DQN. The classification was performed with a small number of features, and the results were similar to those from using all of the features. This suggests that the DQN could be applied to investigating biomarkers for physiological responses and optimizing the classification system to reduce the input resources.

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“…Heartwave as biometric mode has great potential to complement existing 2FA infrastructure for secured access to services and products through the means of wearable devices and smart connected systems. For example a reported work on using biometric to enhance transportation safety such as authenticated access to vehicle and detection of drowsiness via heartwave signal [12]. Apart from real-time IoT applications, with the rapid increase in elderly population, there are intense developments in tele-C.L.P.…”

Section: Introductionmentioning

confidence: 99%

Heartrate-Dependent Heartwave Biometric Identification With Thresholding-Based GMM–HMM Methodology

Lim

Woo

Dlay

et al. 2019

IEEE Trans. Ind. Inf.

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An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme

Cited by 117 publications

References 26 publications

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

Heartrate-Dependent Heartwave Biometric Identification With Thresholding-Based GMM–HMM Methodology

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