Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition

Abdulhay, Enas; Alafeef, Maha; Alzghoul, Loai; Momani, Miral Al; abdi, Rabah M. Al; Arunkumar, N.; Muñoz, Roberto; Albuquerque, Victor Hugo C. de

doi:10.1007/s00521-018-3738-0

Cited by 48 publications

(21 citation statements)

References 25 publications

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“…The power supply noise is 50 Hz/60 Hz. Empirical mode decomposition (EMD) is a signal-processing method based on the time-scale features of the data itself without pre-setting any basis functions 31 . EMD has obvious advantages in dealing with non-stationary and non-linear data.…”

Section: Resultsmentioning

confidence: 99%

Few-shot pulse wave contour classification based on multi-scale feature extraction

Liu

Mao

et al. 2021

Sci Rep

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The annotation procedure of pulse wave contour (PWC) is expensive and time-consuming, thereby hindering the formation of large-scale datasets to match the requirements of deep learning. To obtain better results under the condition of few-shot PWC, a small-parameter unit structure and a multi-scale feature-extraction model are proposed. In the small-parameter unit structure, information of adjacent cells is transmitted through state variables. Simultaneously, a forgetting gate is used to update the information and retain long-term dependence of PWC in the form of unit series. The multi-scale feature-extraction model is an integrated model containing three parts. Convolution neural networks are used to extract spatial features of single-period PWC and rhythm features of multi-period PWC. Recursive neural networks are used to retain the long-term dependence features of PWC. Finally, an inference layer is used for classification through extracted features. Classification experiments of cardiovascular diseases are performed on photoplethysmography dataset and continuous non-invasive blood pressure dataset. Results show that the classification accuracy of the multi-scale feature-extraction model on the two datasets respectively can reach 80% and 96%, respectively.

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Section: Resultsmentioning

confidence: 99%

Few-shot pulse wave contour classification based on multi-scale feature extraction

Liu

Mao

et al. 2021

Sci Rep

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“…The electrical activity called nerve current or axon potential continues along the neuron [2]. The electrical signals formed during the activities of these nerve cells in the brain were recorded for the first time with the Electroencephalograph developed by [3,4,22,26,28]. These recorded signs are called EEG and can be obtained over a very large surface of the cerebral cortex.…”

Section: Open Accessmentioning

confidence: 99%

“…Although there are many different optimization methods in the literature, particle swarm optimization (PSO) algorithm, which is one of the herd-based optimization algorithms that are among the heuristic methods, is used. Although the starting point of the standard PSO method is to detect continuous variables, since electrode selection is a binary variable, it can be used as binary PSO (Binary PSO -BPSO) with sigmoid transformation [6,7,24,25,26,27]. In the study, it is aimed to determine the most effective electrodes to be used on a person basis, to decrease the processing load by reducing the data size obtained and to increase the classification performance.…”

Section: Open Accessmentioning

confidence: 99%

Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems

Arıcan¹,

Polat²

2020

AIS

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Social participation of people with disabilities is tried to be increased with state-supported projects recently. However, even in neuromuscular diseases such as Motor Neurone Disease (MND), Full Sliding Status (TSD), even the communication skills of individuals are interrupted. Brain-Computer Interfaces (BBA), which have a few decades of history and an increasing number of studies with exponential momentum, are being developed to enable individuals with such disorders to communicate with their environment. Spelling systems are BBA systems that detect the letters that the person focuses on the matrix of letters and numbers on a screen and convert them into text through the application. In this context, with the random flashing of the letters on the screen, it aims to detect the electrical changes occurring in the brain as a result of the stimulus given to the person. Research reveals that the stimulus that the individual encounters cause an amplitude in the EEG signal called P300, between 250 and 500 ms. Brain-computer interfaces are used through EEG signals to provide environmental interactions for individuals with restricted movements due to stroke or neurodegenerative diseases. The multi-channel structure of EEG signals both increases system cost and reduces processing speed. For this reason, reducing the system cost by detecting more active electrodes during the process increases the accessibility of people. In this context, the use of optimization techniques in electrode selection is used to determine the most effective channels by a random selection method. In the study, particle herd optimization algorithm, one of the herd-based optimization techniques, was used with two classifiers, SVM and Boosted Tree, and the eight most frequently selected channels were determined to improve system performance in terms of speed and accuracy.

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“…Formulae for measuring these terms are given below: [27]. More information is available in [28][29][30][31][32][33][34].…”

Section: Classification Accuracy Of the Classifiersmentioning

confidence: 99%

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

Hemanth¹

2020

AIS

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Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition

Cited by 48 publications

References 25 publications

Few-shot pulse wave contour classification based on multi-scale feature extraction

Few-shot pulse wave contour classification based on multi-scale feature extraction

Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

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