Signal2Image Modules in Deep Neural Networks for EEG Classification

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201

Deep learning approach for epileptic seizure detection

Abstract. Epilepsy is the most common brain disorder that affects approximately fifty million people worldwide, according to the World Health Organization. The diagnosis of epilepsy relies on manual inspection of EEG, which is error-prone and time-consuming. Automated epileptic seizure detection of EEG signal can reduce the diagnosis time and facilitate targeting of treatment for patients. Current detection approaches mainly rely on the features that are designed manually by domain experts. The features are inflexible for the detection of a variety of complex patterns in a large amount of EEG data. Moreover, the EEG is non-stationary signal and seizure patterns vary across patients and recording sessions. EEG data always contain numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges deep learning approaches are examined in this paper. Deep learning methods were applied to a large publicly available dataset, the Children’s Hospital of Boston-Massachusetts Institute of Technology dataset (CHB-MIT). The present study includes three experimental groups that are grouped based on the pre-processing steps. The experimental groups contain 3–4 experiments that differ between their objectives. The time-series EEG data is first pre-processed by certain filters and normalization techniques, and then the pre-processed signal was segmented into a sequence of non-overlapping epochs. Second, time series data were transformed into different representations of input signals. In this study time-series EEG signal, magnitude spectrograms, 1D-FFT, 2D-FFT, 2D-FFT magnitude spectrum and 2D-FFT phase spectrum were investigated and compared with each other. Third, time-domain or frequency-domain signals were used separately as a representation of input data of VGG or DenseNet 1D. The best result was achieved with magnitude spectrograms used as representation of input data in VGG model: accuracy of 0.98, sensitivity of 0.71 and specificity of 0.998 with subject dependent data. VGG along with magnitude spectrograms produced promising results for building personalized epileptic seizure detector. There was not enough data for VGG and DenseNet 1D to build subject-dependent classifier.Epileptisten kohtausten havaitseminen syväoppimisella lähestymistavalla. Tiivistelmä. Epilepsia on yleisin aivosairaus, joka Maailman terveysjärjestön mukaan vaikuttaa noin viiteenkymmeneen miljoonaan ihmiseen maailmanlaajuisesti. Epilepsian diagnosointi perustuu EEG:n manuaaliseen tarkastamiseen, mikä on virhealtista ja aikaa vievää. Automaattinen epileptisten kohtausten havaitseminen EEG-signaalista voi potentiaalisesti vähentää diagnoosiaikaa ja helpottaa potilaan hoidon kohdentamista. Nykyiset tunnistusmenetelmät tukeutuvat pääasiassa piirteisiin, jotka asiantuntijat ovat määritelleet manuaalisesti, mutta ne ovat joustamattomia monimutkaisten ilmiöiden havaitsemiseksi suuresta määrästä EEG-dataa. Lisäksi, EEG on epästationäärinen signaali ja kohtauspiirteet vaihtelevat potilaiden ja tallennusten välillä ja EEG-data sisältää aina useita kohinatyyppejä, jotka huonontavat epilepsiakohtauksen havaitsemisen tarkkuutta. Näihin haasteisiin vastaamiseksi tässä diplomityössä tarkastellaan soveltuvatko syväoppivat menetelmät epilepsian havaitsemiseen EEG-tallenteista. Aineistona käytettiin suurta julkisesti saatavilla olevaa Bostonin Massachusetts Institute of Technology lastenklinikan tietoaineistoa (CHB-MIT). Tämän työn tutkimus sisältää kolme koeryhmää, jotka eroavat toisistaan esikäsittelyvaiheiden osalta: aikasarja-EEG-data esikäsiteltiin perinteisten suodattimien ja normalisointitekniikoiden avulla, ja näin esikäsitelty signaali segmentoitiin epookkeihin. Kukin koeryhmä sisältää 3–4 koetta, jotka eroavat menetelmiltään ja tavoitteiltaan. Kussakin niistä epookkeihin jaettu aikasarjadata muutettiin syötesignaalien erilaisiksi esitysmuodoiksi. Tässä tutkimuksessa tutkittiin ja verrattiin keskenään EEG-signaalia sellaisenaan, EEG-signaalin amplitudi-spektrogrammeja, 1D-FFT-, 2D-FFT-, 2D-FFT-amplitudi- ja 2D-FFT -vaihespektriä. Näin saatuja aika- ja taajuusalueen signaaleja käytettiin erikseen VGG- tai DenseNet 1D -mallien syötetietoina. Paras tulos saatiin VGG-mallilla kun syötetietona oli amplitudi-spektrogrammi ja tällöin tarkkuus oli 0,98, herkkyys 0,71 ja spesifisyys 0,99 henkilöstä riippuvaisella EEG-datalla. VGG yhdessä amplitudi-spektrogrammien kanssa tuottivat lupaavia tuloksia henkilökohtaisen epilepsiakohtausdetektorin rakentamiselle. VGG- ja DenseNet 1D -malleille ei ollut tarpeeksi EEG-dataa henkilöstä riippumattoman luokittelijan opettamiseksi

Epileptic seizure detection from electroencephalogram (EEG) signals using linear graph convolutional network and DenseNet based hybrid framework

A clinical condition known as epilepsy occurs when the brain's regular electrical activity is disturbed, resulting in a rapid, aberrant, and excessive discharge of brain neurons. The electroencephalogram (EEG) signal is the measurement of electrical activity received from the nerve cells of the cerebral cortex to make precise diagnoses of disorders, which is made crucial attention for treating epilepsy patients in recent years. The concentration on grid-like data has been a significant drawback of existing deep learning-based automatic epileptic seizure detection algorithms from raw EEG signals; nevertheless, physiological recordings frequently have irregular and unordered structures, making it challenging to think of them as a matrix. In order to take advantage of the implicit information that exists in seizure detection, graph neural networks have received a lot of attention. These networks feature interacting nodes connected by edges whose weights can be either dictated by temporal correlations or anatomical junctions. To address this limitation, a novel hybrid framework is proposed for epileptic seizure detection by using linear graph convolution neural network (LGCN) and DenseNet. When compared to previous deep learning networks, DenseNet achieves the model's higher computational accuracy and memory efficiency by reducing the vanishing gradient problem and enhancing feature propagation in each of its layers. The Stockwell transform (S-transform) is used to preprocess from the raw EEG signal and then group the resulting matrix into time-frequency blocks as inputs for the LGCN to use for feature selection and after the Densenet uses for classification. The proposed hybrid framework outperforms the state-of-the-art in seizure detection tasks, achieving 98% accuracy and 98.60% specificity in extensive experiments on the publicly available CHB-MIT EEG dataset

Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units through EEG, ECG and Video Recordings: A Survey

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEG-based NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis

Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG sig nals with modern deep learning models to re duce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on device applications. In this work, for the first time, we extensively compare multiple state-of the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evalu ation metrics including a new one we propose to evaluate more practical aspects of seizure de tection models.ope

The classification of wink-based eeg signals by means of transfer learning models

Stroke is one of the dominant causes of impairme nt. An estimation of half post-stroke survivors suffer from a severe motor or cognitive deterioration, that affects the functionality of the affected parts of the body, which in turn, prevents the patients from carrying out Activities of Daily Living (ADL). EEG signals which contains information on the activities carried out by a human that is widely used in many applications of BCI technologies which offers a means of controlling exoskeletons or automated orthosis to facilitate their ADL. Although motor imagery signals have been used in assisting the hand grasping motion amongst others motions, nonetheless, such signals are often difficult to be generated. It is non-trivial to note that EEG-based signals for instance, winking could mitigate the aforesaid issue. Nevertheless, extracting and attaining significant features from EEG signals are also somewhat challenging. The utilization of deep learning, particularly Transfer Learning (TL), have been demonstrated in the literature to b e able to provide seamless extraction of such signals in a myria d of various applications. Hitherto, limited studies have investigated the classification of wink-based EEG signals through TL accompanied by classical Machine Learning (ML) pipelines. This study aimed to explore the performance of different pre-processing methods, namely Fast Fourier Transform, Short-Time Fourier Transform, Discrete Wavelet Transform, and Continuous Wavelet Transform (CWT) that could allow TL models to extract features from the images generated and classify through selected classical ML algorithms . These pre-processing methods were utilized to convert the digital signals into respective images of all the right and left winking EEG signals along with no winking signals that were collected from ten (6 males and 4 females, aged between 22 and 29) subjects. The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. A new form of input which consists of scalogram and spectrogram images that represents both time and frequency domains , are then introduced in the classification of wink-based EEG signals. Different TL models were exploited to extract features from the transformed EEG signals. The features extracted were then classified through three classical ML models, namely Support Vector Machine, k -Nearest Neighbour (k-NN) and Random Forest to determine the best pipeline for wink -based EEG signals. The hyperparameters of the ML models were tuned through a 5-fold crossvalidation technique via an exhaustive grid search approach. The training, validation and testing of the models were split with a stratified ratio of 60:20:20, respectively. The results obtained from the TL-ML pipelines were evaluated in terms of classification accuracy, Precision, Recall, F1-Score and confusion matrix. It was demonstrated from the simulation investigation that the CWT model could yield a better signal transformation amongst the preprocessing algorithms. In addition, amongst the eighteen TL models evaluated based on the CWT transformation, fourteen was f ound to be able to extract the features reasonable, i.e., VGG16, VGG19, ResNet101, ResNet101 V2, ResNet152, ResNet152 V2, Inception V3, Inception ResNet V2, Xception, MobileNetV2, DenseNet 121, DenseNet 169, NasNetMobile and NasNetLarge. Whilst it was observed that the optimized k-NN model based on the aforesaid pipeline could achieve a classification accuracy of 100% for the training, validation, and tes t data. Nonetheless, upon carrying out a robustness test on new data, it was demonstrated that the CWT-NasNetMobile-kNN pipeline yielded the best performance. Therefore, it could be concluded that the proposed CWT-NasNetMobile-k-NN pipeline is suitable to be adopted to classify -winkbased EEG signals for BCI applications,for instance a grasping exoskeleton

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper