Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

Multi-Mode Frequency Reconfigurable Conformal Antenna for Modern Electronic Systems

The article presents a miniaturized monopole antenna dedicated to modern flexible electronic systems.The antenna combines three fundamental properties in a single structure.Firstly,it is characterized by a compact size compared to the state-of-the-art literature with an overall size of 18×18×0.254 mm3,secondly,the proposed antenna integrates the reconfigurability function of frequency,produced by means of a Positive-IntrinsicNegative(PIN)diode introduced into the radiating element.Thus,the antenna is able to switch between different frequencies and different modes,making it suitable to meet the ever-changing demands of communication systems.third,the antenna is equipped by the property of flexibility.In fact,a conformability test is performed and has demonstrated the stability of the antenna performance under normal and bending conditions.Finally,in order to demonstrate the potential of the proposed antenna,a comparison between the simulated and measured results is made and turned out to be a strong agreement,making the antenna an excellent candidate for future miniaturized rigid and conformal devices.

Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform

This paper introduced an efficient compression technique that uses the compressive sensing(CS)method to obtain and recover sparse electrocardiography(ECG)signals.The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency.A novel analysis was proposed in this paper.To apply CS on ECG signal,the first step is to generate a sparse signal,which can be obtained using Modified Discrete Cosine Transform(MDCT)on the given ECGsignal.This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of this paper.A small number of wavelet components can describe the ECG signal as related work to obtain a sparse ECGsignal.Asensing technique for ECGsignal compression,which is a novel area of research,is proposed.ECG signals are introduced randomly between any successive beats of the heart.MIT-BIH database can be represented as the experimental database in this domain of research.TheMIT-BIH database consists of various ECG signals involving a patient and standard ECG signals.MATLAB can be considered as the simulation tool used in this work.The proposed method’s uniqueness was inspired by the compression ratio(CR)and achieved by MDCT.The performance measurement of the recovered signal was done by calculating the percentage root mean difference(PRD),mean square error(MSE),and peak signal to noise ratio(PSNR)besides the calculation of CR.Finally,the simulation results indicated that this work is one of the most important works in ECG signal compression.