132 research outputs found
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 Childrenâs 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 3â4 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 syvÀoppimisella lÀhestymistavalla. TiivistelmÀ. Epilepsia on yleisin aivosairaus, joka Maailman terveysjÀrjestön mukaan vaikuttaa noin viiteenkymmeneen miljoonaan ihmiseen maailmanlaajuisesti. Epilepsian diagnosointi perustuu EEG:n manuaaliseen tarkastamiseen, mikÀ on virhealtista ja aikaa vievÀÀ. Automaattinen epileptisten kohtausten havaitseminen EEG-signaalista voi potentiaalisesti vÀhentÀÀ diagnoosiaikaa ja helpottaa potilaan hoidon kohdentamista. Nykyiset tunnistusmenetelmÀt tukeutuvat pÀÀasiassa piirteisiin, jotka asiantuntijat ovat mÀÀritelleet manuaalisesti, mutta ne ovat joustamattomia monimutkaisten ilmiöiden havaitsemiseksi suuresta mÀÀrÀstÀ EEG-dataa. LisÀksi, EEG on epÀstationÀÀrinen signaali ja kohtauspiirteet vaihtelevat potilaiden ja tallennusten vÀlillÀ ja EEG-data sisÀltÀÀ aina useita kohinatyyppejÀ, jotka huonontavat epilepsiakohtauksen havaitsemisen tarkkuutta. NÀihin haasteisiin vastaamiseksi tÀssÀ diplomityössÀ tarkastellaan soveltuvatko syvÀoppivat menetelmÀt epilepsian havaitsemiseen EEG-tallenteista.
Aineistona kĂ€ytettiin suurta julkisesti saatavilla olevaa Bostonin Massachusetts Institute of Technology lastenklinikan tietoaineistoa (CHB-MIT). TĂ€mĂ€n työn tutkimus sisĂ€ltÀÀ kolme koeryhmÀÀ, jotka eroavat toisistaan esikĂ€sittelyvaiheiden osalta: aikasarja-EEG-data esikĂ€siteltiin perinteisten suodattimien ja normalisointitekniikoiden avulla, ja nĂ€in esikĂ€sitelty signaali segmentoitiin epookkeihin. Kukin koeryhmĂ€ sisĂ€ltÀÀ 3â4 koetta, jotka eroavat menetelmiltÀÀn ja tavoitteiltaan. Kussakin niistĂ€ epookkeihin jaettu aikasarjadata muutettiin syötesignaalien erilaisiksi esitysmuodoiksi. TĂ€ssĂ€ tutkimuksessa tutkittiin ja verrattiin keskenÀÀn EEG-signaalia sellaisenaan, EEG-signaalin amplitudi-spektrogrammeja, 1D-FFT-, 2D-FFT-, 2D-FFT-amplitudi- ja 2D-FFT -vaihespektriĂ€. NĂ€in saatuja aika- ja taajuusalueen signaaleja kĂ€ytettiin erikseen VGG- tai DenseNet 1D -mallien syötetietoina.
Paras tulos saatiin VGG-mallilla kun syötetietona oli amplitudi-spektrogrammi ja tÀllöin tarkkuus oli 0,98, herkkyys 0,71 ja spesifisyys 0,99 henkilöstÀ riippuvaisella EEG-datalla.
VGG yhdessÀ amplitudi-spektrogrammien kanssa tuottivat lupaavia tuloksia henkilökohtaisen epilepsiakohtausdetektorin rakentamiselle. VGG- ja DenseNet 1D -malleille ei ollut tarpeeksi EEG-dataa henkilöstÀ riippumattoman luokittelijan opettamiseksi
Detection of interictal discharges with convolutional neural networks using discrete ordered multichannel intracranial EEG
Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features which utilised specific characteristics of neural responses. Although these algorithms achieve high accuracy, mere detection of an IED holds little clinical significance. In this work, we consider deep learning for epileptic subjects to accommodate automatic feature generation from intracranial EEG data, while also providing clinical insight. Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. We illustrate how the convolved filters in the deepest layers provide insight towards the different types of IEDs within the group, as confirmed by our expert clinicians. The morphology of the IEDs found in filters can help evaluate the treatment of a patient. To improve the learning of the deep model, moderately different score classes are utilised as opposed to binary IED and non-IED labels. The resulting model achieves state of the art classification performance and is also invariant to time differences between the IEDs. This study suggests that deep learning is suitable for automatic feature generation from intracranial EEG data, while also providing insight into the dat
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Advances in epilepsy monitoring by detection and analysis of brain epileptiform discharges
Brain interictal and pre-ictal epileptiform discharges (EDs) are transient events occurred between two or before seizure onsets visible in intracranial electroencephalographs. In the diagnosis of epilepsy and localization of seizure sources, both interictal and ictal recordings are extremely informative. For this propose, computerized intelligent spike and seizure detection techniques have been researched and are constantly improving. This is not only to detect more EDs from over the scalp but also to classify epileptic and non-epileptic discharges. Tensor factorization and deep learning are two advanced and powerful techniques which have been recently suggested for ED detection. Here, our main contribution is to review recent ED detection methods with emphasis on multi-way analysis and deep learning approaches. These techniques have opened a new window to the epilepsy diagnosis and management spheres
EEG-based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features.
Prognostication for comatose patients after cardiac arrest is a difficult but essential task. Currently, visual interpretation of electroencephalogram (EEG) is one of the main modality used in outcome prediction. There is a growing interest in computer-assisted EEG interpretation, either to overcome the possible subjectivity of visual interpretation, or to identify complex features of the EEG signal. We used a one-dimensional convolutional neural network (CNN) to predict functional outcome based on 19-channel-EEG recorded from 267 adult comatose patients during targeted temperature management after CA. The area under the receiver operating characteristic curve (AUC) on the test set was 0.885. Interestingly, model architecture and fine-tuning only played a marginal role in classification performance. We then used gradient-weighted class activation mapping (Grad-CAM) as visualization technique to identify which EEG features were used by the network to classify an EEG epoch as favorable or unfavorable outcome, and also to understand failures of the network. Grad-CAM showed that the network relied on similar features than classical visual analysis for predicting unfavorable outcome (suppressed background, epileptiform transients). This study confirms that CNNs are promising models for EEG-based prognostication in comatose patients, and that Grad-CAM can provide explanation for the models' decision-making, which is of utmost importance for future use of deep learning models in a clinical setting
Predicting sex from brain rhythms with deep learning
We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10-5), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20-25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology
EEG Interictal Spike Detection Using Artificial Neural Networks
Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patientâs brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce the amount of data for a neurologist to manually analyze. The effectiveness of multiple neural network implementations is compared, and a data reduction of 3-4 orders of magnitude, or upwards of 99%, is achieved
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