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

Brain-computer interface for robot control with eye artifacts for assistive applications

Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot

Brain Tumour Classification Using Deep Features from Structural MRI

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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

Eye-tracking assistive technologies for individuals with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research

Validation of fNIRS System as a Technique to Monitor Cognitive Workload

CognitiveWorkload (CW) is a key factor in the human learning context. Knowing the optimal amount of CW is essential to maximise cognitive performance, emerging as an important variable in e-learning systems and Brain-Computer Interfaces (BCI) applications. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising avenue of brain discovery because of its easy setup and robust results. It is, in fact, along with Electroencephalography (EEG), an encouraging technique in the context of BCI. Brain- Computer Interfaces, by tracking the user’s cognitive state, are suitable for educational systems. Thus, this work sought to validate the fNIRS technique for monitoring different CW stages. For this purpose, we acquired the fNIRS and EEG signals when performing cognitive tasks, which included a progressive increase of difficulty and simulation of the learning process. We also used the breathing sensor and the participants’ facial expressions to assess their cognitive status. We found that both visual inspections of fNIRS signals and power spectral analysis of EEG bands are not sufficient for discriminating cognitive states, nor quantify CW. However, by applying machine learning (ML) algorithms, we were able to distinguish these states with mean accuracies of 79.8%, reaching a value of 100% in one specific case. Our findings provide evidence that fNIRS technique has the potential to monitor different levels of CW. Furthermore, our results suggest that this technique allied with the EEG and combined via ML algorithms is a promising tool to be used in the e-learning and BCI fields for its skill to discriminate and characterize cognitive states.O esforço cognitivo (CW) é um factor relevante no contexto da aprendizagem humana. Conhecer a quantidade óptima de CW é essencial para maximizar o desempenho cognitivo, surgindo como uma variável importante em sistemas de e-learning e aplicações de Interfaces Cérebro-Computador (BCI). A Espectroscopia Funcional de Infravermelho Próximo (fNIRS) emergiu como uma via de descoberta do cérebro devido à sua fácil configuração e resultados robustos. É, de facto, juntamente com a Electroencefalografia (EEG), uma técnica encorajadora no contexto de BCI. As interfaces cérebro-computador, ao monitorizar o estado cognitivo do utilizador, são adequadas para sistemas educativos. Assim, este trabalho procurou validar o sistema de fNIRS como uma técnica de monitorização de CW. Para este efeito, adquirimos os sinais fNIRS e EEG aquando da execução de tarefas cognitivas, que incluiram um aumento progressivo de dificuldade e simulação do processo de aprendizagem. Utilizámos, ainda, o sensor de respiração e as expressões faciais dos participantes para avaliar o seu estado cognitivo. Verificámos que tanto a inspeção visual dos sinais de fNIRS como a análise espectral dos sinais de EEG não são suficientes para discriminar estados cognitivos, nem para quantificar o CW. No entanto, aplicando algoritmos de machine learning (ML), fomos capazes de distinguir estes estados com exatidões médias de 79.8%, chegando a atingir o valor de 100% num caso específico. Os nossos resultados fornecem provas da prospecção da técnica fNIRS para supervisionar diferentes níveis de CW. Além disso, os nossos resultados sugerem que esta técnica aliada à de EEG e combinada via algoritmos ML é uma ferramenta promissora a ser utilizada nos campos do e-learning e de BCI, pela sua capacidade de discriminar e caracterizar estados cognitivos

Neuroimaging of endogenous lapses of responsiveness,

Attention lapses (ALs) and microsleeps (MSs) are complete lapses of responsiveness in which performance is completely disrupted for a short period of time, but consciousness is retained in the case of ALs. ALs are behaviourally different from MSs, as in an AL the eyes remain open whereas in a MS eyes are partially or completely closed. Both ALs and MSs can result in catastrophic consequences, especially in the transportation sector. Research over the past two decades has investigated the AL and MS phenomena using behavioural and physiological means. However, both ALs and MSs need further investigation to separate the different types of ALs physiologically, and to explore the neural signature of MSs in relation to normal sleep and drowsiness. Hence, the objective of this project was to understand the underlying physiological substrates of endogenous (internal) ALs and MSs which could potentially result in differentiating types of ALs and provide more understanding of MSs. Data from two previous Christchurch Neurotechnology Research Programme (NeuroTech™) studies (C and D) were combined resulting in a total of 40 subjects. During each session, subjects performed a 2-D continuous visuomotor tracking (CVT) task for 50 min (Study C) and 20 min (Study D). For each participant, tracking performance, eye-video, EEG, and fMRI were simultaneously collected. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorize lapses of responsiveness for each participant. Participants performed the 2-D CVT task without interruptions. The repetitive nature of the task and the lack of a motivational factor made the task monotonous and fatiguing. As a result, it was more likely to introduce boredom leading to task-unrelated thoughts (TUTs), which divides attention between the task and the internal thoughts unrelated to the task, also fatigue which will introduce a trend of vigilance decrement over time. The project had hypotheses focusing on the changes in the brain’s activity compared to the baseline of good responsiveness tracking. We expected a decrease in dorsal attention network (DAN) activity during ALs due to a decoupling of attention from the external environment. Furthermore, we hypothesized that the ALs were due to involuntary mind-blanks. As such, we expected no change in default mode network (DMN) activity, as would have otherwise been expected if the ALs were due to mind-wandering. Functional connectivity (FC) of the brain was also investigated between the networks of interest which were the DMN, DAN, frontoparietal network (FPN), sensorimotor network (SMN), visual network (VSN), salience network (SN), eye-movement network (EMN), and working memory network (WMN), by analysing data from fMRI. EEG data were also used to perform analysis on ALs and MSs, by analysing changes in power in the delta, theta, alpha, beta, and gamma bands. Voxel-wise fMRI throughout the whole brain, group-ICA, haemodynamic response (HR) over the regions of interest (ROIs), and FC analyses were performed to reveal the neural signature during ALs. In voxel-wise analysis, a significant increase in activity was found in two regions: the dorsal anterior cingulate cortex (dACC) and the supplementary motor area (SMA). The group-ICA analysis did not show any significant results but did show a trend of increased activity in an independent component (IC) that was spatially correlated with SMN. Dynamic HR analysis was performed to further investigate findings from the voxel-wise analysis. Our results were not significant but there were strong trends of change. There was a trend of increased HR 7.5 s after the onset of the AL in the left intraparietal sulcus (IPS) of the DAN. There was also a decrease of 2.5 s before the onset of the AL in the right posterior parietal cortex (PPC) of the FPN. There was also an increase in the HR 5 s after the onset of the AL in the dACC of the SN. Finally, an increase in the HR 15 s before the onset of ALs in the left inferior parietal lobule (IPL) of the DMN is a major finding, as it is an indication that a lapse is about to happen. The HR analysis provided consistent findings with the voxel-wise analysis. FC analysis showed increases in FC within all networks of interest during the ALs. On looking at FC between networks, there was an increase in FC between the DMN and the FPN, no change between the DAN and the FPN, a decrease in FC between the SMN and the FPN, and an increase in FC between the FPN and the VSN. The EMN had an increased FC with the DMN, while it had both increases and decreases in FC with the DAN. There was also an increase in FC between the SN and the DAN, and no change between the SN and the DMN. Finally, a decrease in FC was found between the WMN and the DMN. These findings indicate an overlap between decoupling due to ALs and the process of recovery from ALs. The EEG analysis showed no significant change in the relative difference between average spectral power during ALs and their average baselines for any band of interest for ALs. During MSs, there was a significant increase in power relative to responsive baselines in the delta, theta, alpha, beta, and gamma bands. However, we could not be completely sure that all motion-related artefacts had been removed. Hence, we investigated this further by removing the effect of the global signal, which left only an increase in gamma activity, in addition to a trend of decreased activity in the alpha band. The significant increase in BOLD seen in the voxel-wise analysis is considered to represent the recovery of responsiveness following ALs. This was also seen in trends in group ICA and HR analyses. Overall, findings from the FC analysis show that, in addition to decoupling during ALs, and recovery from ALs, it is highly likely that the ALs during the 2-D CVT task were due to involuntary mind-blanks. This is supported by three major findings: (1) no significant increase in DMN activity in both voxel-wise and HR analyses, (2) the decrease in the HR in the FPN prior to the onset of the AL, and (3) the decrease in FC between the DMN and the WMN. This is further supported behaviourally by the short average duration of ALs (~ 1.7 s), in contrast to what would be likely during mind-wandering. Finally, the significant results from the EEG analysis of MSs, agreed with the literature in delta, theta, and alpha bands. However, increased power in beta and gamma bands was an important finding. We consider this increased high-frequency activity reflects unconscious ‘cognitive’ activity during a MS aimed at restoring consciousness after having fallen asleep during an active task. This highlights a key behavioural and physiological difference between MSs and sleep. Even after removing the effect of the global signal, we still believe that MSs and sleep are physiologically different in the recovery process. To summarize our key findings: (1) this is the first study to demonstrate that ALs during a continuous task are likely to be due to involuntary mind-blanks, (2) the increase in the HR in the DMN 15 s before the onset of AL could be a predictive signature of these lapses, and finally (3) MSs are physiologically different from sleep in terms of the recovery process. This project has improved our understanding of endogenous ALs and MSs and taken us a step closer to accurate detection/prediction systems which can increase prevention of fatal accidents

Eye-Tracking Assistive Technologies for Individuals with Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research