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

STDP-based adaptive graph convolutional networks for automatic sleep staging

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models

SGM: a novel time-frequency algorithm based on unsupervised learning improves high-frequency oscillation detection in epilepsy

Objective. We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors. Approach. This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. Main results. We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. Significance. The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.Peer ReviewedPostprint (published version

Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson's Disease

Deep Brain Stimulation (DBS) of the Subthalamic Nuclei (STN) is the most used surgical treatment to improve motor skills in patients with Parkinson's Disease (PD) who do not adequately respond to pharmacological treatment, or have related side effects. During surgery for the implantation of a DBS system, signals are obtained through microelectrodes recordings (MER) at different depths of the brain. These signals are analyzed by neurophysiologists to detect the entry and exit of the STN region, as well as the optimal depth for electrode implantation. In the present work, a classification model is developed and supervised by the K-nearest neighbour algorithm (KNN), which is automatically trained from the 18 temporal features of MER registers of 14 patients with PD in order to provide a clinical support tool during DBS surgery. We investigate the effect of different standardizations of the generated database, the optimal definition of KNN configuration parameters, and the selection of features that maximize KNN performance. The results indicated that KNN trained with data that was standardized per cerebral hemisphere and per patient presented the best performance, achieving an accuracy of 94.35% (p < 0.001). By using feature selection algorithms, it was possible to achieve 93.5% in accuracy in selecting a subset of six features, improving computation time while processing in real time