Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines

Baykara, Muhammet; Abdulrahman, Awf

doi:10.18280/ts.380210

Cited by 16 publications

(5 citation statements)

References 34 publications

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“…Shannon Entropy [45] It is the expected amount of information in an instance of the distribution. Other features that include the maximum and the minimum point difference and their ratio in the frequency domain, median, mode, and min and max points of time and frequency domain [51][52][53][54][55][56][57][58][59][60][61][62][63] require a complete mathematical procedure to be followed for their computation, and therefore, we did not mention them in Table 1. The features that are mentioned in Table 1 can be graphically observed in Figure 5.…”

Section: 𝒏 (𝒏 − 𝟏)(𝒏 − 𝟐) 𝜮( 𝑿𝒊 − 𝑿 𝑺 )mentioning

confidence: 99%

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

et al. 2022

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Applied sensing technology has made it possible for human beings to experience a revolutionary aspect of the science and technology world. Along with many other fields in which this technology is working wonders, human locomotion activity recognition, which finds applications in healthcare, smart homes, life-logging, and many other fields, is also proving to be a landmark. The purpose of this study is to develop a novel model that can robustly handle divergent data that are acquired remotely from various sensors and make an accurate classification of human locomotion activities. The biggest support for remotely sensed human locomotion activity recognition (RS-HLAR) is provided by modern smartphones. In this paper, we propose a robust model for an RS-HLAR that is trained and tested on remotely extracted data from smartphone-embedded sensors. Initially, the system denoises the input data and then performs windowing and segmentation. Then, this preprocessed data goes to the feature extraction module where Parseval’s energy, skewness, kurtosis, Shannon entropy, and statistical features from the time domain and the frequency domain are extracted from it. Advancing further, by using Luca-measure fuzzy entropy (LFE) and Lukasiewicz similarity measure (LS)–based feature selection, the system drops the least-informative features and shrinks the feature set by 25%. In the next step, the Yeo–Johnson power transform is applied, which is a maximum-likelihood-based feature optimization algorithm. The optimized feature set is then forwarded to the multilayer perceptron (MLP) classifier that performs the classification. MLP uses the cross-validation technique for training and testing to generate reliable results. We designed our system while experimenting on three benchmark datasets namely, MobiAct_v2.0, Real-World HAR, and Real-Life HAR. The proposed model outperforms the existing state-of-the-art models by scoring a mean accuracy of 84.49% on MobiAct_v2.0, 94.16% on Real-World HAR, and 95.89% on Real-Life HAR. Although our system can accurately differentiate among similar activities, excessive noise in data and complex activities have shown an inverse effect on its performance.

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Section: 𝒏 (𝒏 − 𝟏)(𝒏 − 𝟐) 𝜮( 𝑿𝒊 − 𝑿 𝑺 )mentioning

confidence: 99%

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

et al. 2022

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“…It is an unpredictable non-curable chronic mental illness (1). This disorder mars its patients with unbearable physical burdens but also psychological impacts including depression and anxiety (3). A seizure is a sudden shift in human behavior caused by momentarily disturbance of brain's electrical activity (1).…”

Section: Introductionmentioning

confidence: 99%

“…Electroencephalography (EEG) is a brain signal scanning technique which gives an insight into internal electrical activity of normal as well as abnormal brains. It is a painless, noninvasive, yet cost-efficient tool that can be used in conjunction with wearable and portable devices as an early warning mechanism for the incidence of seizures (3).…”

Section: Introductionmentioning

confidence: 99%

Detection of epileptic seizures in EEG by using machine learning techniques

Al-Huseiny¹,

Sajit²

2023

Diagnostyka

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In this research a public dataset of recordings of EEG signals of healthy subjects and epileptic patients was used to build three simple classifiers with low time complexity, these are decision tree, random forest and AdaBoost algorithm. The data was initially preprocessed to extract short waves of electrical signals representing brain activity. The signals are then used for the selected models. Experimental results showed that random forest achieved the best accuracy of detection of the presence/absence of epileptic seizure in the EEG signals at 97.23% followed by decision tree with accuracy of 96.93%. The least performing algorithm was the AdaBoost scoring accuracy of 87.23%. Further, the AUC scores were 99% for decision tree, 99.9% for random forest and 95.6% for AdaBoost. These results are comparable to state-of-the-art classifiers which have higher time complexity.

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“…These methods are applicable for multi-class identification, but a high level of error rate were obtained. The other useful feature is LLE ( 5 ), which is widely used in different fields of studies such as schizophrenia ( 6 , 7 ), sleep EEG processing and memory investigations, BCI applications for control of remote vehicles ( 4 ), as well as in bionic hands, and for the prediction of epilepsy seizure attacks ( 8 , 9 ).…”

Section: Introductionmentioning

confidence: 99%

Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study

Hekmatmanesh

Handroos

2022

Front. Rehabilit. Sci.

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This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.

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Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines

Cited by 16 publications

References 34 publications

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Detection of epileptic seizures in EEG by using machine learning techniques

Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study

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