scholarly journals Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7710
Author(s):  
Anis Malekzadeh ◽  
Assef Zare ◽  
Mahdi Yaghoobi ◽  
Hamid-Reza Kobravi ◽  
Roohallah Alizadehsani
Keyword(s):  
Deep Learning ◽  
Fractal Dimensions ◽  
Epileptic Seizures ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Brain Disorder ◽  
Pass Filter ◽  
Nonlinear Features ◽  
Statistical Frequency ◽  
Conventional Machine

Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals

2018 ◽  
Vol 99 ◽  
pp. 24-37 ◽  
Author(s):  
Κostas Μ. Tsiouris ◽  
Vasileios C. Pezoulas ◽  
Michalis Zervakis ◽  
Spiros Konitsiotis ◽  
Dimitrios D. Koutsouris ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Short Term ◽  
Term Memory ◽  
Learning Network ◽  
Long Short Term Memory ◽  
Deep Learning Network

scholarly journals Continuous Gesture Recognition Based on Time Sequence Fusion Using MIMO Radar Sensor and Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 869 ◽  
Author(s):  
Wentai Lei ◽  
Xinyue Jiang ◽  
Long Xu ◽  
Jiabin Luo ◽  
Mengdi Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Continuous Wave ◽  
Short Term Memory ◽  
Estimation Algorithm ◽  
Intermediate Frequency ◽  
Mimo Radar ◽  
Time Sequence ◽  
Band Pass Filter ◽  
Pass Filter

Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%.

A Portable Electroencephalogram Recording System for Rats

2014 ◽  
Vol 577 ◽  
pp. 1236-1240
Author(s):  
Dian Zhang ◽  
Bo Wang ◽  
Qing Liang Qin
Keyword(s):  
Recording System ◽  
Band Pass Filter ◽  
Test Results ◽  
Electronic Components ◽  
Eeg Signals ◽  
Pass Filter ◽  
Wireless Transmitter ◽  
Eeg Data ◽  
Band Pass ◽  
Electroencephalogram Eeg

A wireless portable electroencephalogram (EEG) recording system for animals was designed, manufactured and then tested in rats. The system basically consisted of four modules: 1) EEG collecting module with the wireless transmitter and receiver (designed by NRF24LE1), 2) filter bank consisting of pre-amplifier, band pass filter and 50Hz trapper, 3) power management module and 4) display interface for showing EEG signals. The EEG data were modulated firstly and emitted by the wireless transmitter after being amplified and filtered. The receiver demodulated and displayed the signals in voltage through serial port. The system was designed as surface mount devices (SMD) with small size (20mm×25mm×3mm) and light weight (4g), and was fabricated of electronic components that were commercially available. The test results indicated that in given environment the system could stably record more than 8 hours and transmit EEG signals over a distance of 20m. Our system showed the features of small size, low power consumption and high accuracy which were suitable for EEG telemetry in rats.

scholarly journals Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

2021 ◽  
Vol 5 (4) ◽  
pp. 78
Author(s):  
Anis Malekzadeh ◽  
Assef Zare ◽  
Mahdi Yaghoobi ◽  
Roohallah Alizadehsani
Keyword(s):  
Fractal Dimension ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Band Pass Filter ◽  
Classification Problems ◽  
Complex Wavelet Transform ◽  
Eeg Signals ◽  
Pass Filter ◽  
Box Counting ◽  
Convolutional Autoencoder

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.

AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350009 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
RATNA YANTI ◽  
JIA WEI ZHENG ◽  
M MUTHU RAMA KRISHNAN ◽  
JEN HONG TAN ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Texture Features ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Brain Disorder

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

scholarly journals Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ali Torabi ◽  
Mohammad Reza Daliri
Keyword(s):  
Hurst Exponent ◽  
Neurological Disorder ◽  
Fractal Dimensions ◽  
Classification Problems ◽  
Eeg Signals ◽  
Epilepsy Diagnosis ◽  
Nonlinear Features ◽  
Linear Svm ◽  
Electroencephalogram Eeg ◽  
The Brain

Abstract Background Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. Methods In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). Results According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. Conclusion The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

Influence of Brain Cooling on Frequency Characteristics of the Epileptic Focus and its Surrounding Area

2018 ◽  
Author(s):  
Saya Kumano ◽  
Takashi Saito ◽  
Kenyu Uehara
Keyword(s):  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Epileptic Focus ◽  
Band Pass Filter ◽  
Brain Cooling ◽  
Surrounding Area ◽  
Brain Wave ◽  
Pass Filter ◽  
Suppression Effect ◽  
Band Pass

In this study, we examined the suppression effect of brain cooling on the epileptic focus and surrounding area. An epileptic seizure was induced in rats to obtain electrocorticography (ECoG) data when brain cooling was performed on the epileptic focus and its surroundings. Then, the frequency response characteristics were calculated by applying fast Fourier transform (FFT) and band pass filter to the obtained multichannel brain wave data. At this time, the frequency band calculated by the band pass filter was α waves (8.0–13.0Hz) and β waves (13.0–30.0 Hz) which were remarkably observed in epileptic seizure in the previous study, the analysis window of FFT was 4.095 seconds, and the overlap was 75%. As a result of comparing the calculated frequency responses for each rat, it was found that at the site where epileptic seizures were observed, power was reduced by cooling and suppressing effect was observed, whereas at the same time, the power increased at the site a few millimeters adjacent to the seizure site. This result suggests that epileptic waves suppressed by brain cooling might propagate to the surrounding area by a few millimeters.

scholarly journals Classification of sleep apnea based on EEG sub-band signal characteristics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoyun Zhao ◽  
Xiaohong Wang ◽  
Tianshun Yang ◽  
Siyu Ji ◽  
Huiquan Wang ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Automatic Classification ◽  
Support Vector ◽  
Composition Analysis ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Normal Breathing ◽  
Obstructive Sleep

AbstractSleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

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