EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm

Alyasseri, Zaid Abdi Alkareem; Alomari, Osama Ahmad; Papa, João Paulo; Al-Betar, Mohammed Azmi; Abdulkareem, Karrar Hameed; Mohammed, Mazin Abed; Kadry, Seifedine; Thinnukool, Orawit; Khuwuthyakorn, Pattaraporn

doi:10.3390/s22062092

Cited by 12 publications

(8 citation statements)

References 27 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the multichannel characteristic of EEG signals, it is necessary to perform channel selection [44][45][46][47][48][49]. Most of the cases in CHB-MIT collect 23 channel information.…”

Section: Channel Selectionmentioning

confidence: 99%

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

Sun

Liu

et al. 2022

Journal of Sensors

View full text Add to dashboard Cite

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

show abstract

“…Due to the multichannel characteristic of EEG signals, it is necessary to perform channel selection [44][45][46][47][48][49]. Most of the cases in CHB-MIT collect 23 channel information.…”

Section: Channel Selectionmentioning

confidence: 99%

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

Sun

Liu

et al. 2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…This hybrid approach achieved highest accuracy 90.1%. Such algorithms are commonly used in other biomedical signals, where the synthetic signal generation could help in fine tuning set parameters [67][68][69][70][71].…”

Section: Recent Workmentioning

confidence: 99%

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Augustýnek¹,

Kubicek²,

Thomas³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary. This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker's functionality. In this work, we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment. The electrical model of the heart allows signals generation (right atrium, right ventricle) and the monitoring of the stimulation pulses. The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time. The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses. The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode. In addition, the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.

show abstract

“…An electrocardiogram (ECG) signal is the most widely utilized biosignal for CVDs detection on an early basis. The ECG is a non-invasive measurement of the heart that is utilized to diagnose different cardiac illnesses and anomalies [ 2 , 3 ]. Cardio specialists have been utilizing ECG waveforms for over seven decades to distinguish heart illnesses, for example, arrhythmia and myocardial areas of dead tissue for more than 70 years [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Novel DERMA Fusion Technique for ECG Heartbeat Classification

Mastoi

Wah

Mohammed

et al. 2022

Life

Self Cite

View full text Add to dashboard Cite

An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People’s Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity.

show abstract

EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm

Cited by 12 publications

References 27 publications

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Novel DERMA Fusion Technique for ECG Heartbeat Classification

Contact Info

Product

Resources

About