Approaches of Phase Lag Index to EEG Signals in Alzheimer’s Disease from Complex Network Analysis

Kasakawa, Shinya; Yamanishi, Tomonori; Takahashi, Tetsuya; Ueno, Kanji; Kikuchi, Mitsuru; Nishimura, Haruhiko

doi:10.1007/978-3-319-23024-5_42

Cited by 17 publications

(16 citation statements)

References 19 publications

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“…The effects of these noises can be substantially removed by different digital filtering methods. Recently, the Blind Source Separation (BSS) techniques such as Independent Component Analysis (ICA) are wildly used for the artifact removal of the Sensors 2020, 20, 1340 5 of 13 physiological signals [32][33][34], however, our signals are single channel signals while the BSS techniques requires multi-channels signals, thus, we choose the traditional methods to remove the noise. First, we applied a notch filter to remove the effect of the power frequency interference.…”

Section: Signal Preprocessingmentioning

confidence: 99%

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

Huang¹,

Luo²,

Peng³

2020

Sensors

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In this study, a novel classification method for a driver's cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver's cognitive stress level. First, we defined three levels of tasks and collected the ECG signal of the driver at different cognitive stress levels by designing and performing a driving simulation experiment. We extracted the interbeat intervals and converted them to pictures according to the number of consecutive interbeat intervals in each picture. Second, the CNN model was used to train the data set to recognize the cognitive stress levels. Classification accuracies of 100%, 91.6% and 92.8% were obtained for the training set, validation set and test set, respectively, and were compared with those the BP neural network. Last, we discussed the influence of the number of interbeat intervals in each picture on the performance of the proposed classification method. The results showed that the performance initially improved with an increase in the number of interbeat intervals. A downward trend was observed when the number exceeded 40, and when the number was 40, the model performed best with the highest accuracy (98.79%) and a relatively low relative standard deviation (0.019).

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Section: Signal Preprocessingmentioning

confidence: 99%

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

Huang¹,

Luo²,

Peng³

2020

Sensors

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“…The Machines (LIBSVM) [35] 3 interest of applying this approach lies in its ability to define structural and functional properties of a network with dynamical units through simple measures that characterise the topology of the network. This type of analysis has been used in a wide range of applications such as the study of social networks and interactions [39,40], brain connectivity analysis [41,42], genetics [43], and transportation systems [44,45], among others.…”

Section: Complex Network Analysismentioning

confidence: 99%

Activity Recognition Using Complex Network Analysis

Jalloul¹,

Porée²,

Viardot³

et al. 2018

IEEE J. Biomed. Health Inform.

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In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body.

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“…Another method of classification is provided by the steady state visual evoked potential (SSVEP), proposed as a trade-off solution among accuracy, responsiveness and complexity [ 28 ]. It is also possible to find a relation between some of the mental disorders and the brain neural network activity, which can be detected, for example, by the Phase Lag Index [ 29 ] or DWT, sample entropy and O_CCA [ 30 ].…”

Section: State Of the Artmentioning

confidence: 99%

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Opałka

Stasiak

Szajerman

et al. 2018

Sensors

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Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

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Approaches of Phase Lag Index to EEG Signals in Alzheimer’s Disease from Complex Network Analysis

Cited by 17 publications

References 19 publications

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

Activity Recognition Using Complex Network Analysis

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

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