Nonconvulsive Epileptic Seizure Detection in Scalp EEG Using Multiway Data Analysis

Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally prolonged epileptic seizures without evident physical symptoms. Since these continuous seizures may cause permanent brain damage, it constitutes a medical emergency. This paper proposes a method to detect nonconvulsive seizures for a further nonconvulsive status epilepticus diagnosis. To differentiate between the normal and seizure electroencephalogram (EEG), a K-Nearest Neighbor, a Radial Basis Support Vector Machine, and a Linear Discriminant Analysis classifier are used. The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition (BTD) of the EEG data represented as third order tensor. To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. The experimental results suggest that a Hilbert-Huang tensor representation and the CPD analysis provide the most suitable framework for nonconvulsive seizure detection. The Radial Basis Support Vector Machine classifier shows the best performance with sensitivity, specificity, and accuracy values over 98%. A rough comparison with other methods proposed in the literature shows the superior performance of the proposed method for nonconvulsive epileptic seizure detection

Methods for automatic seizure detection in intensive care: Feature selection and evaluation

Epileptinen kohtaus aiheutuu aivoissa niiden epänormaalista sähköisestä toiminnasta. Kun kohtaus on ei-konvulsiivinen, ulkoisia merkkejä, kuten lihaskouristuksia, ei havaita. Tästä syystä ei-konvulsiiviset kohtaukset voidaan havaita vain mittaamalla aivojen sähköisiä signaaleja aivosähkökäyrällä (elektroenkefalografia, EEG). Ei-konvulsiiviset kohtaukset ovat yleisiä tehohoidossa. Niiden havaitseminen on tärkeää, sillä viivästyneellä diagnoosilla ja kohtauksen kestolla on yhteys kuolleisuuteen ja sairastavuuteen. Diagnosointia varten EEG tarvitsee kokeneen neurofysiologin tulkinnan. Signaalin analysointi on raskasta ja aikaa vievää, ja tästä syystä automaattisesta kohtausten havaitsemisesta olisi tehohoidossa apua. Tässä tutkimuksessa verrattiin kahden neurofysiologin merkintöjä kohtausten ajankohdista 50 tehohoitopotilaalla. Yksimielisyys neurofysiologien välillä oli kohtalainen. Kohtausajanjaksoja, joista asiantuntijat olivat yksimielisiä, sekä dataa 55 tehohoitopotilaalta, joilla ei ollut kohtauksia, käytettiin sellaisten EEG-piirteiden etsimiseen, joilla voitaisiin erottaa kohtaukset jaksoista ilman kohtauksia. 18 piirrettä laskettiin useilla aikaikkunoilla kahdesta kaksiulotteisesta EEG-piirreavaruudesta. Lisäksi EEG:stä laskettiin spektrimuuttujia sekä piikkien määrä minuutissa. Piirteiden valinta suoritettiin optimisointimenetelmällä. Piirreyhdistelmät muodostettiin 5, 7 ja 10 piirteellä, ja niiden suorituskykyä vertailtiin itsenäisellä aineistolla, joka muodostui EEG-mittauksista 40 tehohoitopotilaalla, kohtauksilla ja ilman kohtauksia. 5 piirteen mallilla oli paras suorituskyky. 5 piirteen malli havaitsi itsenäisestä aineistosta kaikki 11 potilasta, joilla oli yksiselitteisiä kohtauksia. Mediaanisensitiivisyys potilaiden yli oli 0.90 ja mediaani väärien havaintojen asteesta 0.56 havaintoa tunnissa. Tulokset ovat lupaavia, mutta lisäkehitystä tarvitaan väärien havaintojen vähentämiseksi.Epileptic seizure is caused by abnormal electrical activity in the brain. When a seizure is nonconvulsive, external indications of seizure, such as muscle contractions, are not visible. Nonconvulsive seizures can be detected only by measuring the electrical signals of the brain with electroencephalogram (EEG). Nonconvulsive seizures are common in intensive care unit (ICU). Detection of seizures is important, because the delay of diagnosis and duration of seizures have association with mortality and morbidity. For the diagnosis, EEG needs to be reviewed by an experienced reader. The analysis of EEG signals is burdensome and time-taking, and therefore, an automatic detection method for seizures in intensive care would provide a great help. In this study, seizure markings of two certified EEG readers in EEG records of 50 ICU patients were compared. The agreement between the readers was moderate. Seizure periods agreed by the experts and data from 55 ICU patients without seizures were used to search features from EEG that could distinguish seizure activity from non-seizure activity. 18 features were computed in several time windows from two two-dimensional EEG feature spaces. In addition, spectral features and spike rate were computed from EEG signal. Feature selection was performed with an optimizing method. Feature combinations of 5, 7, and 10 features were formed. Their performance was compared in an independent data set of EEG records of 40 ICU patients, including patients with and without seizures. 5-feature-model had the best performance among the models. 5-feature-model detected in the independent data set all 11 patients with unequivocal seizures. Median sensitivity over patients was 0.90 and median false positive rate was 0.56 false positives per hour. Results are promising, but further development is needed for reducing the false positive rate

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy

Towards developing a reliable medical device for automated epileptic seizure detection in the ICU

Abstract. Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced. The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients

Management strategies for refractory status epilepticus

Refractory status epilepticus (RSE) is defined as the persistence of either clinical or electrographic seizures despite the administration of appropriate doses of an initial benzodiazepine and suitable second-line antiepileptic drugs (AEDs). The Neurocritical Care Society and the American Epilepsy Society have proposed a treatment paradigm for the management of convulsive status epilepticus (CSE). The third-line therapy in refractory CSE may involve general anesthesia using intravenous midazolam, propofol, or other agents, while recent evidence supports the use of ketamine to manage RSE in both adults and children. However, although these treatment strategies are frequently employed in nonconvulsive status epilepticus (NCSE), the efficacy of AEDs and anesthetics in NCSE has not been thoroughly investigated. Recent evidence has demonstrated the safety and efficacy of newer AEDs, including levetiracetam and lacosamide, in the treatment of status epilepticus (SE) and RSE, which also encompasses NCSE. Use of multiple combinations of various intravenous AEDs can also be considered in NCSE before the administration of general anesthesia. In addition, AEDs alone exhibit limited effectiveness in managing SE for new-onset RSE (NORSE) and its subset, febrile infection-related epilepsy syndrome. Therefore, in cases of refractory status, it is imperative to explore treatment options beyond AEDs, including immunotherapy and the incorporation of a ketogenic diet. The present review suggests treatment approaches for RSE based on subgroups, including CSE, NCSE, and NORSE. A discussion of recent clinical studies on AEDs and anesthetics in the management of RSE, as well as proposed treatment methods for NORSE, is also provided

Seizure Detection Using Teager-Kaiser Energy and a Channel Signal Quality Assessment Algorithm

This study investigated the possibility of reducing the time required for accurate epileptic seizure detection through a retroactive analysis. Epilepsy is a neurological disorder affecting over 50 million individuals globally and is defined as a disorder which results in two seizures unprovoked by fever or medication. Diagnosis of epilepsy typically involves a monitored stay at an Epilepsy Monitoring Unit (EMU). The monitoring and diagnosis process ranges on average from $35,000 to$40,000 for a single stay, and the patient results from EMU are not instantly available to the patient. The collected electroencephalogram (EEG) must be analyzed by a trained EMU technician before the physician analyzes the data. The retroactive seizure detection algorithm utilizes Teager-Kaiser energy (TE). TE increases as either a signal’s frequency or amplitude increases and is only dependent on three consecutive samples from the time-domain. The detection algorithm was trained and tested on 37,718 hours of data from 70 male Sprague Dawley rats with a total of 843 recorded seizures. The algorithm resulted in an average sensitivity of 98.1% and an average false positive rate (FPR) of 0.2660 per hour. Current algorithms involve a training stage and perform with a sensitivity between 80% and 98.8% and a FPR between 0.054 and 1 per hour. The study supports TE as a useful measure for seizure detection, and although this algorithm focuses on retroactive seizure detection, the quick response time of TE makes it well suited for real-time seizure detection

fNIRS improves seizure detection in multimodal EEG-fNIRS recordings

In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database-a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction