Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating

A novel universal deep learning approach for accurate detection of epilepsy

Epilepsy claims the lives of many people, so researchers strive to build highly accurate diagnostic models. One of the limitations of obtaining high accuracy is the scarcity of Electroencephalography (EEG) data and the fact that they are from different devices in terms of the channels number and sampling frequency. The paper proposes universal epilepsy diagnoses with high accuracy from electroencephalography signals taken from any device. The novelty of the proposal is to convert VEEG video into images, separating some parts and unifying images taken from different devices. The images were tested by dividing the video into labeled frames of different periods. By adding the spatial attention layer to the deep learning in the new model, classification accuracy increased to 99.95 %, taking five seconds/frame. The proposed has high accuracy in detecting epilepsy from any EEG without being restricted to a specific number of channels or sampling frequencies.

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

An epilepsy classification based on FFT and fully convolutional neural network nested LSTM

Background and objective

Epilepsy, which is associated with neuronal damage and functional decline, typically presents patients with numerous challenges in their daily lives. An early diagnosis plays a crucial role in managing the condition and alleviating the patients' suffering. Electroencephalogram (EEG)-based approaches are commonly employed for diagnosing epilepsy due to their effectiveness and non-invasiveness. In this study, a classification method is proposed that use fast Fourier Transform (FFT) extraction in conjunction with convolutional neural networks (CNN) and long short-term memory (LSTM) models.

Methods

Most methods use traditional frameworks to classify epilepsy, we propose a new approach to this problem by extracting features from the source data and then feeding them into a network for training and recognition. It preprocesses the source data into training and validation data and then uses CNN and LSTM to classify the style of the data.

Results

Upon analyzing a public test dataset, the top-performing features in the fully CNN nested LSTM model for epilepsy classification are FFT features among three types of features. Notably, all conducted experiments yielded high accuracy rates, with values exceeding 96% for accuracy, 93% for sensitivity, and 96% for specificity. These results are further benchmarked against current methodologies, showcasing consistent and robust performance across all trials. Our approach consistently achieves an accuracy rate surpassing 97.00%, with values ranging from 97.95 to 99.83% in individual experiments. Particularly noteworthy is the superior accuracy of our method in the AB versus (vs.) CDE comparison, registering at 99.06%.

Conclusion

Our method exhibits precise classification abilities distinguishing between epileptic and non-epileptic individuals, irrespective of whether the participant's eyes are closed or open. Furthermore, our technique shows remarkable performance in effectively categorizing epilepsy type, distinguishing between epileptic ictal and interictal states versus non-epileptic conditions. An inherent advantage of our automated classification approach is its capability to disregard EEG data acquired during states of eye closure or eye-opening. Such innovation holds promise for real-world applications, potentially aiding medical professionals in diagnosing epilepsy more efficiently.

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.

Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals

A comprehensive analysis of an automated system for epileptic seizure detection is explained in this work. When a seizure occurs, it is quite difficult to differentiate the non-stationary patterns from the discharges occurring in a rhythmic manner. The proposed approach deals with it efficiently by clustering it initially for the sake of feature extraction by using six different techniques categorized under two different methods, e.g., bio-inspired clustering and learning-based clustering. Learning-based clustering includes K-means clusters and Fuzzy C-means (FCM) clusters, while bio-inspired clusters include Cuckoo search clusters, Dragonfly clusters, Firefly clusters, and Modified Firefly clusters. Clustered values were then classified with 10 suitable classifiers, and after the performance comparison analysis of the EEG time series, the results proved that this methodology flow achieved a good performance index and a high classification accuracy. A comparatively higher classification accuracy of 99.48% was achieved when Cuckoo search clusters were utilized with linear support vector machines (SVM) for epilepsy detection. A high classification accuracy of 98.96% was obtained when K-means clusters were classified with a naive Bayesian classifier (NBC) and Linear SVM, and similar results were obtained when FCM clusters were classified with Decision Trees yielding the same values. The comparatively lowest classification accuracy, at 75.5%, was obtained when Dragonfly clusters were classified with the K-nearest neighbor (KNN) classifier, and the second lowest classification accuracy of 75.75% was obtained when Firefly clusters were classified with NBC.

Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.

Seizure detection with reduced electroencephalogram channels: research trends and outlook

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

EEG signal classification of tinnitus based on SVM and sample entropy

The prevalence of tinnitus is high and seriously affects the daily life of patients. As the pathogenesis of tinnitus is not yet clear, there is a lack of rapid and objective diagnostic modalities. In order to provide clinicians with an objective diagnostic approach, this paper combines time-frequency domain and non-linear power analysis to investigate the differences in the specificity of the EEG signal in tinnitus patients compared to healthy subjects. In this paper, resting-state electroencephalograms (EEG) were collected from 10 cases each of tinnitus patients and healthy subjects, and the data from the two groups were compared in the δ (0.5 - 3 .5 Hz), θ (4 - 7.5 Hz), α1 (8 - 10 Hz), α2 (10 - 12 Hz), β1 (13 - 18 Hz), β2 (18.5 - 21 Hz), β3 (21.5 - 30 Hz), and γ (30.5 - 44 Hz) bands for the differences in sample entropy values. The results of the resting state experiment revealed that the δ, α2 and β1 band samples of tinnitus patients all had greater entropy values than healthy subjects, with extremely significant differences compared to healthy subjects (p < 0.01). It is mainly concentrated in the δ band in the right parietal region of the cerebral cortex, the α2 band in the central region, and the γ band in the left prefrontal region. Finally, support vector machines combined with optimal feature combinations were used to achieve objective recognition of tinnitus disorders, with an 8.58% increase in accuracy compared to other features. Through the above study, entropy reflects the degree of chaos in the brain and the chaotic characteristics of the resting state EEG signal can characterise the onset of tinnitus, the results of which can help clinicians in the early diagnosis of tinnitus.

A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction

Background

K-complex detection traditionally relied on expert clinicians, which is time-consuming and onerous. Various automatic k-complex detection-based machine learning methods are presented. However, these methods always suffered from imbalanced datasets, which impede the subsequent processing steps.

New method

In this study, an efficient method for k-complex detection using electroencephalogram (EEG)-based multi-domain features extraction and selection method coupled with a RUSBoosted tree model is presented. EEG signals are first decomposed using a tunable Q-factor wavelet transform (TQWT). Then, multi-domain features based on TQWT are pulled out from TQWT sub-bands, and a self-adaptive feature set is obtained from a feature selection based on the consistency-based filter for the detection of k-complexes. Finally, the RUSBoosted tree model is used to perform k-complex detection.

Results

Experimental outcomes manifest the efficacy of our proposed scheme in terms of the average performance of recall measure, AUC, and F10-score. The proposed method yields 92.41 ± 7.47%, 95.4 ± 4.32%, and 83.13 ± 8.59% for k-complex detection in Scenario 1 and also achieves similar results in Scenario 2.

Comparison to state-of-the-art methods

The RUSBoosted tree model was compared with three other machine learning classifiers [i.e., linear discriminant analysis (LDA), logistic regression, and linear support vector machine (SVM)]. The performance based on the kappa coefficient, recall measure, and F10-score provided evidence that the proposed model surpassed other algorithms in the detection of the k-complexes, especially for the recall measure.

Conclusion

In summary, the RUSBoosted tree model presents a promising performance in dealing with highly imbalanced data. It can be an effective tool for doctors and neurologists to diagnose and treat sleep disorders.

Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges

Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.

Evaluating the Window Size's Role in Automatic EEG Epilepsy Detection

Electroencephalography is one of the most commonly used methods for extracting information about the brain's condition and can be used for diagnosing epilepsy. The EEG signal's wave shape contains vital information about the brain's state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals' classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden-Fletcher-Goldfarb-Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.

Textural feature based intelligent approach for neurological abnormality detection from brain signal data

The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject's EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data.

Automatic Epileptic Seizure Detection Using Graph-Regularized Non-Negative Matrix Factorization and Kernel-Based Robust Probabilistic Collaborative Representation

Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development. An automatic detection of epileptic seizure method based on kernel-based robust probabilistic collaborative representation (ProCRC) combined with graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The raw EEG signals are pre-processed through the wavelet transform to obtain time-frequency distribution of EEG signals as preliminary feature information and GNMF is further employed for dimension reduction, retaining and enhancing the productive feature information of EEG signals. Then, the test sample is represented using robust ProCRC that can decide whether the testing sample belongs to each class (seizure or non-seizure) by jointly maximizing the likelihood. In addition, the kernel trick is applied to improve the separability of non-linear high dimensional EEG signals in robust ProCRC. Finally, post-processing techniques are introduced to generate more accurate and reliable results. The average epoch-based sensitivity of 96.48%, event-based sensitivity of 93.65% and specificity of 98.55% are acquired in this method, which is evaluated on the public Freiburg EEG database.

An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.

Wavelet-Based Multi-Class Seizure Type Classification System

Epilepsy is one of the most common brain diseases that affects more than 1% of the world’s population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and require professional skills. This paper presents a novel automatic technique that involves the extraction of specific features from epileptic seizures’ EEG signals using dual-tree complex wavelet transform (DTCWT) and classifying them into one of the seven types of seizures, including absence, complex-partial, focal non-specific, generalized non-specific, simple-partial, tonic-clonic, and tonic seizures. We evaluated the proposed technique on the TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with the existing state-of-the-art techniques using the overall F1-score due to class imbalance of seizure types. Our proposed technique achieved the best results of a weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification, respectively, thereby setting new benchmark results for this dataset.

Deep-learning-based seizure detection and prediction from electroencephalography signals

Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection and prediction system can improve the quality of patient care in terms of shortening the diagnosis period, reducing manual errors, and automatically detecting debilitating events. Moreover, for patient treatment, it is important to alert the patients of epilepsy seizures prior to seizure occurrence. Various distinguished studies presented good solutions for two-class seizure detection problems with binary classification scenarios. To deal with these challenges, this paper puts forward effective approaches for EEG signal classification for normal, pre-ictal, and ictal activities. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific, which makes it better for the general classification tasks. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized three-class classification framework is considered to identify all EEG signal activities. The first model depends on a Convolutional Neural Network (CNN) with residual blocks. It contains thirteen layers with four residual learning blocks. It works on spectrograms of EEG signal segments. The second model depends on a CNN with three layers. It also works on spectrograms. On the other hand, the third model depends on Phase Space Reconstruction (PSR) to eliminate the limitations of the spectrograms used in the first models. A five-layer CNN is used with this strategy. The advantage of the PSR is the direct projection from the time domain, which keeps the main trend of different signal activities. The third model deals with all signal activities, and it was tested for all patients of the CHB-MIT dataset. It has a superior performance compared to the first models and the state-of-the-art models.

A 65nm/0.448 mW EEG processor with parallel architecture SVM and lifting wavelet transform for high-performance and low-power epilepsy detection

In recent years, low-power and wearable biomedical testing devices have emerged as a key answer to the challenges associated with epilepsy disorders, which are prone to crises and require prolonged monitoring. The feature vector of the electroencephalographic (EEG) signal was extracted using the lifting wavelet transform algorithm, and the hardware of the lifting wavelet transform module was optimized using the canonic signed digit (CSD) coding method. A low-power EEG feature extraction circuit with a power consumption of 0.42 mW was constructed. This article employs the support vector machine (SVM) technique after feature extraction to categorize and identify epilepsy. A parallel SVM processing unit was constructed to accelerate classification and identification, and then a high-speed, low-power EEG epilepsy detection processor was implemented. The processor design was completed using TSMC 65 nm technology. The chip size is 0.98 mm2, operating voltage is 1 V, operating frequency is 1 MHz, epilepsy detection latency is 0.91 s, power consumption is 0.448 mW, and energy efficiency of a single classification is 2.23 μJ/class. The CHB-MIT database test results show that this processor has a sensitivity of 91.86% and a false detection rate of 0.17/h. Compared to other processors, this processor is more suitable for portable/wearable devices.

Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals

Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate an epileptic seizure by differentiating between the pre-ictal and interictal stages of the subject's brain. Despite having the appearance of a typical anomaly detection task, this problem is complicated by subject-specific characteristics in EEG data. Therefore, studies that investigate seizure prediction widely employ subject-specific models. However, this approach is not suitable in situations where a target subject has limited (or no) data for training. Subject-independent models can address this issue by learning to predict seizures from multiple subjects, and therefore are of greater value in practice. In this study, we propose a subject-independent seizure predictor using Geometric Deep Learning (GDL). In the first stage of our GDL-based method we use graphs derived from physical connections in the EEG grid. We subsequently seek to synthesize subject-specific graphs using deep learning. The models proposed in both stages achieve state-of-the-art performance using a one-hour early seizure prediction window on two benchmark datasets (CHB-MIT-EEG: 95.38% with 23 subjects and Siena-EEG: 96.05% with 15 subjects). To the best of our knowledge, this is the first study that proposes synthesizing subject-specific graphs for seizure prediction. Furthermore, through model interpretation we outline how this method can potentially contribute towards Scalp EEG-based seizure localization.

EMD-WOG-2DCNN based EEG signal processing for Rolandic seizure classification

Objective Approximately 65 million people have epilepsy around the world. Recognition of epilepsy types is the basis to determine the treatment method and predict the prognosis in epilepsy patients. Childhood benign epilepsy with centrotemporal spikes (BECTS) or benign Rolandic epilepsy is the most common focal epilepsy in children, accounting for 15-20% of childhood epilepsies. These EEG patterns of individuals usually predict good treatment responses and prognosis. Until now, the interpretation of EEG still depends entirely on experienced neurologists, which may be a lengthy and tedious task. Method In this article, we proposed a novel machine learning model that efficiently distinguished Rolandic seizures from normal EEG signals. The proposed machine learning model processes the identification procedure in the following order (1) creating preliminary EEG features using signal empirical mode decomposition, (2) applying weighted overlook graph (WOG) to represent the decomposed EMD of IMF, and (3) classifying the results through a two Dimensional Convolutional Neural Network (2DCNN). The performance of our classification model is compared with other representative machine learning models. Results The model offered in this article gains an accuracy performance exceeding 97.6% in the Rolandic dataset, which is higher than other classification models. The effect of the model on the Bonn public dataset is also comparable to existing methods and even performs better in some subsets. Conclusion The purpose of this study is to introduce the most common childhood benign epilepsy type and propose a model that meets the real clinical needs to distinguish this Rolandic EEG pattern from normal signals accurately. Significance Future research will optimize the model to categorize other types of epilepsies beyond BECTS and finally implement them in the hospital system.

Extracting Epileptic Features in EEGs Using a Dual-Tree Complex Wavelet Transform Coupled with a Classification Algorithm

The detection of epileptic seizures from electroencephalogram (EEG) signals is traditionally performed by clinical experts through visual inspection. It is a long process, is error prone, and requires a highly trained expert. In this research, a new method is presented for seizure classification for EEG signals using a dual-tree complex wavelet transform (DT-CWT) and fast Fourier transform (FFT) coupled with a least square support vector machine (LS-SVM) classifier. In this method, each EEG signal is divided into four segments. Each segment is further split into smaller sub-segments. The DT-CWT is applied to decompose each sub-segment into detailed and approximation coefficients (real and imaginary parts). The obtained coefficients by the DT-CWT at each decomposition level are passed through an FFT to identify the relevant frequency bands. Finally, a set of effective features are extracted from the sub-segments, and are then forwarded to the LS-SVM classifier to classify epileptic EEGs. In this paper, two epileptic EEG databases from Bonn and Bern Universities are used to evaluate the extracted features using the proposed method. The experimental results demonstrate that the method obtained an average accuracy of 97.7% and 96.8% for the Bonn and Bern databases, respectively. The results prove that the proposed DT-CWT and FFT based features extraction is an effective way to extract discriminative information from brain signals. The obtained results are also compared to those by k-means and Naïve Bayes classifiers as well as with the results from the previous methods reported for classifying epileptic seizures and identifying the focal and non-focal EEG signals. The obtained results show that the proposed method outperforms the others and it is effective in detecting epileptic seziures in EEG signals. The technique can be adopted to aid neurologists to better diagnose neurological disorders and for an early seizure warning system.

Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy

Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate brain dynamics. These signals are dynamic, complex, chaotic and nonlinear, the nature of which is represented with features - fractal dimensions, entropies and chaotic features. This study aims at examining the discriminative power of individual features and their combination in the diagnosis of a neuro-pathological condition called encephalopathy. Feature combination is accomplished with the help of feature selection using Gini impurity score that improves discriminative power and keeps redundancy minimal. Further, three widely used non-parametric classifiers which are known to be effective with wavelet features on EEG signals - Support Vector Machine, Random Forest, Multilayer Perceptron - are employed for disease classification. The models created by the combination of aforementioned stages are analysed and evaluated with performance scores, leading to an optimal model for automated diagnostic applications.

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

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.

Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization

Emotional brain-computer interface based on electroencephalogram (EEG) is a hot issue in the field of human-computer interaction, and is also an important part of the field of emotional computing. Among them, the recognition of EEG induced by emotion is a key problem. Firstly, the preprocessed EEG is decomposed by tunable-Q wavelet transform. Secondly, the sample entropy, second-order differential mean, normalized second-order differential mean, and Hjorth parameter (mobility and complexity) of each sub-band are extracted. Then, the binary gray wolf optimization algorithm is used to optimize the feature matrix. Finally, support vector machine is used to train the classifier. The five types of emotion signal samples of 32 subjects in the database for emotion analysis using physiological signal dataset is identified by the proposed algorithm. After 6-fold cross-validation, the maximum recognition accuracy is 90.48%, the sensitivity is 70.25%, the specificity is 82.01%, and the Kappa coefficient is 0.603. The results show that the proposed method has good performance indicators in the recognition of multiple types of EEG emotion signals, and has a better performance improvement compared with the traditional methods.

Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques

Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A tunable Q-factor wavelet transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control subjects was performed using the Kruskal Wallis test. Feature values ​​obtained from each sub band were classified using well-known ensemble learning techniques and their classification performances were tested. Among the evaluated classifiers, the highest classification performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.

A progressive deep wavelet cascade classification model for epilepsy detection

Automatic epileptic seizure detection according to EEG recordings is helpful for neurologists to identify an epilepsy occurrence in the initial anti-epileptic treatment. To quickly and accurately detect epilepsy, we proposed a progressive deep wavelet cascade classification model (PDWC) based on the discrete wavelet transform (DWT) and Random Forest (RF). Different from current deep networks, the PDWC mimics the progressive object identification process of human beings with recognition cycles. In every cycle, enhanced wavelet energy features at a specific scale were extracted by DWT and input into a set of cascade RF classifiers to realize one recognition. The recognition accuracy of PDWC is gradually improved by the fusion of classification results produced by multiple recognition cycles. Moreover, the cascade structure of PDWC can be automatically determined by the classification accuracy increment between layers. To verify the performance of the PDWC, we respectively applied five traditional schemes and four deep learning schemes to four public datasets. The results show that the PDWC is not only superior than five traditional schemes, including KNN, Bayes, DT, SVM, and RF, but also better than deep learning methods, i.e. convolutional neural network (CNN), Long Short-Term Memory (LSTM), multi-Grained Cascade Forest (gcForest) and wavelet cascade model (WCM). The mean accuracy of PDWC for all subjects of all datasets reaches to 0.9914. With a flexible structure and less parameters, the PDWC is more suitable for the epilepsy detection of diverse EEG signals.

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automatic detection of epileptic seizures can substantially improve the patient's quality of life. To achieve a high-quality model, we have to collect data from various patients in a central server. However, sending the patient's raw data to this central server puts patient privacy at risk and consumes a significant amount of energy. To address these challenges, in this work, we have designed and evaluated a standard federated learning framework in the context of epileptic seizure detection using a deep learning-based approach, which operates across a cluster of machines. We evaluated the accuracy and performance of our proposed approach on the NVIDIA Jetson Nano Developer Kit based on the EPILEPSIAE database, which is one of the largest public epilepsy datasets for seizure detection. Our proposed framework achieved a sensitivity of 81.25%, a specificity of 82.00%, and a geometric mean of 81.62%. It can be implemented on embedded platforms that complete the entire training process in 1.86 hours using 344.34 mAh energy on a single battery charge. We also studied a personalized variant of the federated learning, where each machine is responsible for training a deep neural network (DNN) to learn the discriminative electrocardiography (ECG) features of the epileptic seizures of the specific person monitored based on its local data. In this context, the DNN benefitted from a well-trained model without sharing the patient's raw data with a server or a central cloud repository. We observe in our results that personalized federated learning provides an increase in all the performance metric, with a sensitivity of 90.24%, a specificity of 91.58%, and a geometric mean of 90.90%.

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.

Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient's sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz's algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform (TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method.The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of 97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.

A Deep Fourier Neural Network for Seizure Prediction Using Convolutional Neural Network and Ratios of Spectral Power

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods usually adopt handcrafted features and manual parameter setting. The over-reliance on the expertise of specialists may lead to weak exploitation of features and low popularization of clinical application. This paper proposes a novel parameterless patient-specific method based on Fourier Neural Network (FNN), where the Fourier transform and backpropagation learning are synthesized to make the predictor more efficient and practical. The employment of FNN is the first attempt in the field of seizure prediction due to its automatic extraction of immanent spectra in epileptic signals. Despite the self-adaptive superiority of FNN, we introduce Convolutional Neural Network (CNN) to further improve its search capability in high-dimensional feature spaces. The study also develops a multi-layer module to estimate spectral power ratios of raw recordings, which optimizes the prediction by enhancing feature diversity. Based on these modules, this paper proposes a two-channel deep neural network: Fourier Ratio Convolutional Neural Network (FRCNN). To demonstrate the reliability of the model, we explain the mathematical meaning of hidden-layer neurons in FRCNN theoretically. This approach is evaluated on both intracranial and scalp EEG datasets. It shows that the predictor achieved a sensitivity of 91.2% and a false prediction rate (FPR) of 0.06[Formula: see text]h[Formula: see text] across intracranial subjects and a sensitivity of 85.4% and an FPR of 0.14[Formula: see text]h[Formula: see text] over scalp subjects. The results indicate that FRCNN enables the convenience of epilepsy treatments while preserving a high degree of precision. In the end, a detailed comparison with the previous methods demonstrates that FRCNN has achieved higher performance and generalization ability.

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

INTRODUCTION

The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations.

METHODS

In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data.

RESULTS

The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification.

DISCUSSION/CONCLUSION

The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.