Abstract

• Delayed-onset seizures after deep brain stimulation (DBS) surgery were seldom reported in previous studies. We studied 341 PD patients who underwent STN-DBS surgery from 2006 to 2021 in our department retrospectively.

• Five patients experienced seizures after DBS surgery with an incidence of 1.47 %. All 5 seizures cases were characterized as delayed-onset with an average time of 12 days postoperatively, which was different from the previous studies. In the further risk factors analysis of seizures, we found that the average diameter of peri-electrode edema of patients with seizures was larger than the average diameter of patients without seizures, which indicated that peri-electrode edema was risk factor for delayed-onset seizures after DBS surgery.

• Further multivariate analysis confirmed the result. Our study is the first report focusing on clinical characteristics and risk factors of delayed-onset seizures after DBS surgery based on a case series.

• Although DBS related seizure is uncommon complication, patients who experienced seizures are still exposed in risks of trauma and damage to the implanted simulator. We believe that these findings were useful for prevention and management of seizures associated with DBS surgery.

Introduction

Deep brain stimulation (DBS) is a well-established surgical therapy for some movement and psychiatric disorders. Patients are exposed in risks of trauma and implanted simulator damage when seizures occur, though seizures are not common postoperative adverse event of deep brain stimulation surgery. Previous studies indicated that the risk factors for seizures related to DBS included hemorrhage, peri-electrode edema, and increased age [1, 2, 3]. Most of the seizures occurred during electrode implantation procedures or within 48 h after DBS surgery [1, 2, 3], and delayed-onset seizures were seldom reported. However, this seizures incidence time-point presents quite different from those reports when retrospectively screening those PD patients who underwent subthalamic nucleus (STN) DBS in our center. We found that the seizures after DBS surgery were mainly delayed-onset (1–2 weeks after surgery). Therefore, this study summarized the clinical characteristics of delayed-onset seizures after DBS surgery and analyzed related risk factors.

Conclusions

Delayed-onset seizure after STN-DBS treating PD patients was uncommon with an incidence of 1.47 %. The seizures were transient and self-limiting, not easily developing into chronic epilepsy. Peri-electrode edema was a risk factor for delayed-onset seizures after DBS surgery. Patients with an average peri-electrode edema diameter > 2.70 cm had a higher risk to develop seizures.

Highlights

Abstract

Abbreviations

Keywords

1. Introduction

2. Mechanisms of action of COVID-19 candidate drugs

3. Potential drug-drug interactions between ASMs and COVID-19 candidate drugs

4. Potential drug interactions between ASMs and antipyretic and anti-inflammatory drugs concomitantly used in COVID-19 patients

5. Conclusion

Data availability statement

References

Abstract

In regard to the global pandemic of COVID-19, it seems that persons with epilepsy (PWE) are not more vulnerable to get infected by SARS-CoV-2, nor are they more susceptible to a critical course of the disease. However, management of acute seizures in patients with COVID-19 as well as management of PWE and COVID-19 needs to consider potential drug-drug interactions between antiseizure drugs and candidate drugs currently assessed as therapeutic options for COVID-19. Repurposing of several licensed and investigational drugs is discussed for therapeutic management of COVID-19. While for none of these approaches, efficacy and tolerability has been confirmed yet in sufficiently powered and controlled clinical studies, testing is ongoing with multiple clinical trials worldwide. Here, we have summarized the possible mechanisms of action of drugs currently considered as potential therapeutic options for COVID-19 management along with possible and confirmed drug-drug interactions that should be considered for a combination of antiseizure drugs and COVID-19 candidate drugs. Our review suggests that potential drug-drug interactions should be taken into account with drugs such as chloroquine/hydroxychloroquine and lopinavir/ritonavir while remdesivir and tocilizumab may be less prone to clinically relevant interactions with ASMs.

1. Introduction

The catastrophic global pandemic of Coronavirus disease 2019 (COVID-19) has put large parts of the world on a transient standstill. COVID-19 was first discovered in December 2019 and the causative agent was a novel subtype of beta-coronaviruses, known as SARS-CoV-2 (Rothan and Byrareddy, 2020). The novel coronavirus is an enveloped non-segmented positive-sense RNA virus (Richman et al., 2016) that was transmitted to humans via zoonotic transmission, similar to its precursors, severe acute respiratory syndrome coronavirus (SARS-CoV) (Ksiazek et al., 2003) and the Middle East respiratory syndrome coronavirus (MERS-CoV) (Zaki et al., 2012). These viruses primarily affect the respiratory system, however, clinical literature also provides evidence for neuroinvasive and neurotropic properties of SARS-CoV-2 (Carod-Artal, 2020; Li et al., 2020b; Najjar et al., 2020). Neurological symptoms reported in patients with COVID-19 include febrile seizures, status epilepticus and complications including encephalopathy, cerebral haemorrhage (Asadi-Pooya, 2020; Asadi-Pooya and Simani, 2020; Carod-Artal, 2020; Yin et al., 2020). These clinical manifestations suggest that people with neurological disorders may be more vulnerable to experience severe symptoms of COVID-19 disease. However, that does not seem to be the case. The International League Against Epilepsy (ILAE) has informed that persons with epilepsy (PWE) are not likely to be more susceptible to get COVID-19 nor are they inclined to suffer through severe manifestations of SARS-CoV-2 infection (ILAE, 2020). Even if PWE are exposed to SARS-CoV-2, it is unlikely that the frequency of seizures increases (ILAE, 2020). Nevertheless, management of COVID-19 in PWE or with acute reactive seizures requires certain precautions and guidelines to avoid worsening of the condition. In particular, potential drug-drug interaction that may occur on concomitant administration of anti-seizure medication (ASM) along with the drugs for treatment of COVID-19 need to be taken into account.

1- Introduction

2- Methodology

3- Results and discussion

4- Conclusions

References

Abstract

Epilepsy is the fourth most common neurological disorder that manifests itself through unprovoked seizures, detection of which is the very first step of proper diagnosis and treatment of this severe disease. In this paper, an automated seizure detection method has been proposed based on the statistical and spectral features of max normalized intrinsic mode functions or IMFs that were extracted using complete ensemble empirical mode decomposition with adaptive noise method. First, a publicly available dataset of EEG signals was used to generate the IMFs and noise or outliers were discarded. Then IMFs were max normalized which was shown to improve the separability of features. Statistical and spectral features were extracted from the normalized IMFs which offered better separation of seizure and seizure-free data. Finally, Quadratic Discriminant classifier was used for the classification purpose and 10-fold cross validation was performed to validate the trained model. The proposed scheme is numerically efficient and shows a maximum of 100% accuracy which is the highest reported on this data set.

Motivation

The International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) have defined the terms epileptic seizure and epilepsy as follows: an epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain and epilepsy is a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures and by the neurobiologic, cognitive, psychological, and social consequences of this condition [1]. The definition of epilepsy requires the occurrence of at least one epileptic seizure. According to World Health Organization (WHO), approximately 50 million people have epilepsy worldwide. Around 80% of epilepsy patients are either from low or middleincome countries, of which around 75% failto get proper treatment. Epilepsy has severe socioeconomic consequences, including high treatment cost,low employment rate, andlow salary among epileptic patients [2]. Epilepsy remains resistant to drug therapy in about one-third of patients and people with pharmacoresistant epilepsy are about 2–10 times more likely to die compared to the general population [3].

Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features – variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients – were able to significantly capture the seizures. When compared with a baseline method of classifying all epochs as normal, an improvement of 4.77–13.51% in the Bayesian error was obtained.

Introduction

An epileptic seizure, as defined by the International League Against Epilepsy [1], is a temporary event of symptoms due to synchronization of abnormally excessive activities of neurons in the brain. It has been estimated that approximately 65 million people around the world are affected by epilepsy [2]. Nevertheless, it is still a time-consuming process for neurologists to review continuous electroencephalograms (EEGs) to monitor epileptic patients. Therefore, several researchers have developed differenttechniques that help neurologists to identify an epilepsy occurrence [3–5]. The whole process of automated epileptic seizure analysis primarily consists of data acquisition, signal pre-processing, feature extraction, feature or channel selection, and classification. This paper focuses on a selection of features commonly used in the literature, including statistical parameters (mean, variance, skewness, and kurtosis), amplitude-related parameters (energy, nonlinear energy, line length, maximum and minimum values) and entropyrelated measures. These features can be categorized based on their interpretation or the domain from which the features are calculated. While some studies have considered a particular group of features applicable to their proposed classification method [6–8], others have applied various groups of features extracted from the time, frequency, and time-frequency domains.

1- Introduction

2- P-glycoprotein inhibitors

3- Gene therapy

4- Stem cell therapy

5- Traditional and novel antiepileptic devices

6- Precision medicine

7- Advances in the therapy of epileptic encephalopathies in children

References

Abstract

Despite the availability of many antiepileptic drugs (AEDs) (old and newly developed) and, as recently suggested, their optimization in the treatment of patients with uncontrolled seizures, more than 30% of patients with epilepsy continue to experience seizures and have drug-resistant epilepsy; the management of these patients represents a real challenge for epileptologists and researchers. Resective surgery with the best rates of seizure control is not an option for all of them; therefore, research and discovery of new methods of treating resistant epilepsy are of extreme importance. In this article, we will discuss some innovative approaches, such as P-glycoprotein (P-gp) inhibitors, gene therapy, stem cell therapy, traditional and novel antiepileptic devices, precision medicine, as well as therapeutic advances in epileptic encephalopathy in children; these treatment modalities open up new horizons for the treatment of patients with drug-resistant epilepsy.

Introduction

For decades, the epilepsy community has addressed the burden of drug-resistant epilepsy by focusing on the development of new antiepileptic drugs (AEDs) and advancing epilepsy surgery techniques. These approaches have resulted in some success, but the percentage of patients with drug-resistant epilepsy has not changed substantially. More than 30% of individuals with epilepsy have persistent seizures despite the use of appropriate therapies, i.e., they have drug-resistant epilepsy [1–3]. Therefore, investigating and discovering new ways and strategies to treat epilepsy is of paramount significance. There are multiple approaches in the pipeline to treat drug-resistant epilepsy. In this article, we are going to discuss some of the interesting and innovative approaches, which are potentially opening new horizons in treating patients with drug-resistant epilepsy. We have to mention that many of these plausible strategies need to be validated and should be tested in large double-blind randomized clinical trials.

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.

INTRODUCTION

According to the World Health Organization (WHO), nearly 50 million people suffer from epilepsy worldwide [1]. It is estimated that 2.4 million people are diagnosed with epilepsy annually [1]. Seizures are due to the uncontrolled electrical discharges in a group of neurons. [2, 3]. The excessive electrical discharges result in the disruption of brain function. Epilepsy is diagnosed when there is recurrence of at least two unprovoked seizures. It can affect anyone at any age [4]. A timely and accurate diagnosis of epilepsy is essential for patients in order to initiate anti-epileptic drug therapy and subsequently reduce the risk of future seizures and seizurerelated complications [5]. Currently, the diagnosis of epilepsy is made by obtaining a detailed history, performing a neurological exam, and ancillary testing such as neuro-imaging and EEG. The EEG signals can identify inter-ictal (between seizures) and ictal (during seizure) epileptiform abnormalities. Figure 1 shows a graphical representation of the electrical activity in the brain of healthy subjects and seizure patients. Typically, neurons communicate through electrical signals. Therefore, in a regular brain activity, these electrical signals are normally regulated [3] (see the normal activity in Figure 1). However, during seizure, there is an abnormally increased hypersynchronous electrical activity of epileptogenic neural network. This activity may remain localized to one part of the brain, or spread to the entire brain. In either scenario, an individual may experience a clinical seizure (see the seizure activity in Figure 1) [3]. Neurologists scrutinize the EEG via direct visual inspection to investigate for epileptiform abnormalities that may provide valuable information on the type and etiology of a patient’s epilepsy. However, interpretation of the EEG signals by visual assessment is time-consuming particularly with the increased use of out-patient ambulatory EEG’s and in-patient continuous video EEG recordings, where there are hours or days worth of EEG data that needs to be reviewed manually [6]. The majority of EEG software includes some form of automated seizuredetection, however, due to the poor sensitivity and specificity of the pre-determined seizure detection algorithms, the current forms of automated seizure detection are rarely used in clinical practice. In addition, the inherent nature of visual inspection results in varying clinical interpretations based on the EEG reader’s level of expertise in electroencephalography. Complicating matters, the quality of the study may be confounded by interfering artifactual signal limiting the reader’s ability to accurately identify abnormalities. Moreover, the low yield of routine out-patient studies poses another problem. A patient with epilepsy can go for an outpatient EEG and the study may be completely normal. This is because the brains of patients with epilepsy are generally not continually firing off epileptic discharges.

People with epilepsy (PWE) frequently suffer from comorbid mood and anxiety disorders. Depression is one of the major psychiatric comorbidities having a negative impact on the quality of life in people with epilepsy. A review of the literature indicates that the majority of antidepressant-related seizures have been associated with either ultra-high doses or overdosing and, generally, the risk of antidepressant-associated seizures is low. Correspondingly, there is some evidence indicating that antidepressants of most widely used groups may additionally lower the risk of triggering seizures. Four antidepressants are not recommended for patients with epilepsy, i.e.: amoxapine, bupropion, clomipramine and maprotiline. Clinicians applying first line of depression treatment in patients with epilepsy should consider use of SSRIs or SNRIs, particularly sertraline, citalopram, mirtazapine, reboxetine, paroxetine, fluoxetine, escitalopram, fluvoxamine, venlafaxine, duloxetine. Implementation of anticonvulsive drugs in depressed patients should include valproate, carbamazepine, lamotrigine, gabapentin, pregabalin. The paper reviews the evidence for the clinical use of antidepressants in PWE.

Introduction

People with epilepsy (PWE) frequently suffer from comorbid mood disorders. Lifetime prevalence ranges between 11% and 62% [1]. One of the major psychiatric comorbidities having a negative impact on the quality of life in PWE is depression [2]. The relationship between depression and epilepsy may be bidirectional; having depression would increase the risk of epilepsy, and having epilepsy appears to increase the risk of depression [3]. Suicide attempts among depressed PWE are four- to fivefold more frequent than in the general population [4–6]. Depression has also been associated with higher rates of drug resistance in PWE [7]. Considering these issues the successful and safe treatment of depression associated with epilepsy is of utmost clinical importance. Also psychotherapy must be mentioned as effective and safe therapeutic intervention in PWE suffering from depression [8]. Guidelines indicating the choice of psychotropic drugs in PWE are mainly based on studies in patients without epilepsy, as well as risk evaluation of seizures [9,10]. Antidepressants may impact the incidence of seizures in a dose-related manner in line with the animal model of epilepsy [11]. The anticonvulsive effect is noticed at low doses and with the increase of the dose the effect is being reversed [12]. A review of the literature indicates that most antidepressant-related seizures have been associated with either ultrahigh doses or particularly in overdosing [13]. The paper reviews best practice indications for antidepressant treatment in patients with epilepsy.

چکیده

بیماران مبتلا به صرع (به اصطلاح: PWE) غالباً از اختلالات رفتاری و اضطراب به طور همزمان رنج می‌برند. افسردگی یکی از مهم ترین اختلالات روانپزشکی است که به طور مستقیم بر روی کیفیت زندگی بیماران مبتلا به صرع تاثیر منفی می‌گذارد. بررسی‌ها نشانگر آن است که اکثر تشنج‌های ناشی از داروهای ضدافسردگی به دلیل دوزهای بسیار بالا صورت می‌گیرد و بدین ترتیب، خطر تشنج ناشی از مصرف داروهای ضدافسردگی کم می‌باشد.این در حالی است که شواهد نشان می‌دهد داروهای ضدافسردگی مورداستفاده ریسک ابتلا به تشنج را نیز کاهش می‌دهند. چهار داروی ضدافسردگی برای بیماران مبتلا به صرع توصیه نمی‌گردد که از آنها می‌توان به آموکساپین، بوپروپیون، کلومیپرامین و ماپروتیلین اشاره نمود. متخصصان بالینی در وهلۀ نخست باید در خصوص بیماران مبتلا به صرع اس‌اس‌آرآی‌ها و اس‌ان‌آرآی‌ها به ویژه سرتالین، سیتالوپرام، میرتازاپین، ربوکستین، پاروکستین، فلوکستین، اسیتالوپرام، فلووکسامین، ونلافاکسین و دولوکستین را تجویز نمایند.

I. INTRODUCTION

EEG (Electroencephalography) is an important and effective tool to measure the activities of brain and understand the complex behavior of the brain. Even a small variations in EEG signal define a specific type of brain disorder and hence it is necessary to develop an algorithm which can be used for the detection of brain abnormality since it may affect the life of a person. EEG is a dynamic non-invasive and relatively inexpensive method used to monitor the state of brain. According to the survey about 1% of the world’s population is having epilepsy and it is the 2nd most common neurological disorder [3]. This neurological disorder is associated with recurrent, unprovoked epileptic seizures which results due to the excessive – discharge of central neuron groups which are harmful to human. However, person who does not have epilepsy can suffer from seizures. The Behavior of the person with seizure ranges from simple finger twitching to convulsion where muscles will contract and relax in a manner which can not be controlled, results in an involuntary movement of the body. Automatic seizure detection can be divided into five groups: frequency domain based, time domain based, timefrequency domain based, nonlinear methods and artificial neural network based [4]. Since EEG is a non stationary signal, it is more appropriate to use time-frequency based method such as discrete wavelet transforms (DWT). DWT analysis is important to address the different behavior of EEG signal on both time and frequency domain [5]. Once the feature has been extracted, then the resultant data will be given to the classifier to classify signal as normal or abnormal. Many methodologies have been used for the classification purpose. In the proposed work neural network classifier has been used.

II. REVIEW

Many researchers are trying to propose different methodologies for the automatic detection of brain disorder using EEG signals. In a paper by M. Mursalin [1] an algorithm has been proposed for automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. The algorithm has tested on 5 set of EEG data. Each consists of 100 channels. Two set was recorded from a normal person and 3 were recorded from epileptic seizure patient. Feature extraction was done using time domain, frequency domain and entropy based. Best feature were selected using ICFS (Improved Correlation-based Feature Selection method) and classifier used was Random Forest classifier (RF). In another paper by J. G. Bogaarts [2] has worked on a method for the seizure detection in which the EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm. For the classification purpose SVM classifier has been used. Zandi [2] has proposed a wavelet based technique for the real time detection of epileptic seizure. Decomposition of EEG signal was done using wavelet packet transform and frrequency band which represnets maximum separation was obtained. The algorithm was experimented on 14 pateints with 75.8 h with 63 seizures. The method resulted in 90.5% of sensitivity and false detection rate of 0.51/h. Yinxia Liu [6] has described an algorithm using wavelet transform and SVM for sizure detection. The data set they have used consist of 2 to 5 h of seizure data and 24 to 26 h of non-seizure data and. The decomposition of multi-channel iEEG (intracranial EEG) was done and frequency bands were extracted from that only three bands are selected. Features were extracted and then sent to SVM for classification purpose. The specificity achieved was 95.26% and seneitivity of 94.46% with false detection rate of 0.58/h.

Introduction

Neurocysticercosis (NCC), one of the most common helminthic infestations of the brain, is highly prevalent in low and middle income countries (LMICs). It is one of the most common causes of acquired epilepsy and neurological morbidity in children [1]. A community based survey conducted in 300 patients from Morang district of Nepal reported that NCC was the main risk factors in about 7.3% prevalent seizure cases as was in another study from Western Nepal that also reported NCC as the common neuroimaging fndings in abnormal brain scans [2, 3]. Central nervous system (CNS) involvement is seen in 60–90% of infested patients with cerebrum and cerebellum as common sites but cysticerci may involve brainstem, basal ganglion, thalamus, and lateral sinus as well [4]. NCC is common in the communities with poor hygiene and sanitation where untreated human faeces are used in the feld/garden as fertilizer. Vegetarians might get infected by eating raw vegetables contaminated with ova or proglottids of Taenia solium whereas nonvegetarians may get infected afer eating improperly cooked pork meat [5]. Tis study was thus conducted to study the prevalence, the clinical profle, and the radiological fndings of NCC in children presenting with seizures.

Material and Methods

Tis study recruited children aged 0–16 years from 1 August 2014 to 31 July 2016 at a tertiary hospital of South-Western Nepal. All cases with seizures admitted to pediatric ward and pediatric intensive care unit were enrolled following written and verbal consent. Seizure was defned as a transient occurrence of signs and/or symptoms resulting from abnormal excessive or synchronous neuronal activity in the brain (as per International league against epilepsy (ILAE) classifcation). Neurocysticercosis was diagnosed by the revised criteria as described by Del Brutto [6]. Radiological classifcation of NCC was done as per Escobar’s pathological staging system [7]. Children not presenting with seizures and/or encephalopathy or children with seizures and/or encephalopathy where neuroimaging (computed tomography or magnetic resonance imaging) could not be carried out were excluded from the study. To rule out tubercular infection of brain, a complete blood count, Mantoux test, erythrocyte sedimentation rate (ESR), X-ray chest, and gastric aspirate for acid fast bacilli (AFB) were done in all patients having NCC in CT or MRI scan. Te study was approved by the Universal Medical Colleges institute’s ethical review board (IRB). Sample size (� = 162) was calculated “anticipating 11.9% prevalence of neurocysticercosis in epilepsy patients [8] to fall within 10% points of true proportion with 95% confdence interval.” Data was recorded in predesigned pro forma. Patients were followed up in 3 months’ time following administration of antiepileptic drugs (AEDs).

1- Introduction

2- Proposed method

3- Results

4- Discussion

5- Conclusion

References

Abstract

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children’s Hospital–MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets.

Introduction

Advances in data mining and machine learning in the past few decades have attracted significantly more attention to the application of these techniques in detective and predictive data analytics, especially in health care, medical 5 practices, and biomedical engineering (Kuhlmann et al., 2015; Freestone et al., 2015; Xiao et al., 2017; Bou Assi et al., 2017; Kuhlmann et al., 2017; Freestone et al., 2017; Sinha et al., 2017). While the body of available proven knowledge lacks a convincing and comprehensive understanding of the sources of epileptic seizures, some early studies showed the possibility of predicting seemingly unpredictable 10 seizures (Rogowski et al., 1981; Salant et al., 1998). Along with continuous improvements in recording electroencephalogram (EEG) signals, there have been an increasing number of EEG-based techniques for seizure prediction (Szostak 2 et al., 2017). There have been some articles on seizure prediction using the Freiburg Hospital dataset (University of Freiburg, 2003). For example, the 15 dynamical similarity index, effective correlation dimension, and increments of accumulated energy were used as features (Maiwald et al., 2004). The dynamical similarity index yielded the highest performance, with sensitivity of 42% and false prediction rate (FPR) less than 0.15/h. The mean phase coherence and lag synchronization index of 32-s sliding EEG windows were used as features 20 for seizure prediction (Winterhalder et al., 2006).

1- Introduction

2- Related work

3- Proposed cognitive IoT-cloud framework and seizure detection technique

4- Results

5- Conclusion

References

Abstract

We propose a cognitive Internet of Things (IoT)–cloud-based smart healthcare framework, which communicates with smart devices, sensors, and other stakeholders in the healthcare environment; makes an intelligent decision based on a patient’s state; and provides timely, low-cost, and accessible healthcare services. As a case study, an EEG seizure detection method using deep learning is also proposed to access the feasibility of the cognitive IoT–cloud smart healthcare framework. In the proposed method, we use smart EEG sensors (apart from general healthcare smart sensors) to record and transmit EEG signals from epileptic patients. Thereafter, the cognitive framework makes a real-time decision on future activities and whether to send the data to the deep learning module. The proposed system uses the patient’s movements, gestures, and facial expressions to determine the patient’s state. Signal processing and seizure detection take place in the cloud, while signals are classified as seizure or nonseizure with a probability score. The results are transmitted to medical practitioners or other stakeholders who can monitor the patients and, in critical cases, make the appropriate decisions to help the patient. Experimental results show that the proposed model achieves an accuracy and sensitivity of 99.2 and 93.5%, respectively.

Introduction

The Internet of Things (IoT), which can be considered an interconnected network of intelligent sensor devices, often has limited storage and low processing power capability. IoT, together with cloud computing, which has a large storage and sufficient processing power capability, has made essential services, such as smart healthcare [1, 2], possible in a smart city environment. However, monitoring and communicating remotely with patients are necessary in such environments. In addition, the need to provide low-cost, high-quality, and patient-centric smart healthcare to patients has emerged. The advancements in the field of IoT [3] and cloud technologies [4] has resulted in a tremendous demand for realtime, intelligent, and remote healthcare services under the paradigm of smart cities. Furthermore, the integration of IoT and cloud technologies has provided a seamless and ubiquitous framework for smart healthcare monitoring. At present, residents in smart cities have access to smart sensor devices and advanced mobile technologies. In an environment such as that of a smart city, finding specialized doctors, healthcare centers, and hospitals nearby is difficult. The movement of patients in critical conditions is also quite difficult. Hence, we need to create a smart healthcare monitoring framework by integrating the resources available at our disposal to improve the quality and accessibility of healthcare services. In such a smart healthcare monitoring framework, we can transmit and process medical-related multimedia signals from smart sensors and mobile devices to provide timely assistance and quality healthcare services to patients. However, such healthcare data and signals are often naturally large and challenging to handle because of their complexity. The healthcare industry has emerged as one of the major industries with tremendous demands. Apart from providing patients with critical and crucial services, this industry is also generating large revenues for the government and the private sector.

Febrile seizures are the most common paroxysmal episode during childhood, affecting up to one to de 10 children. Febrile seizures (FS) are among the most common reasons that patients present with to pediatric emergencies. FS has been recognized as a separate disease entity from other type of seizures since the early mid-nineteenth century. These seizures are classically associated with high fever in children during their lives (Hirtz et al., 2003). Their etiology and pathophysiological pathways are being understood better over time; however, there is still more to learn. Genetic predisposition is thought to be a major contributor leading to an increased susceptibility to seizure (Lennox, 1949). Febrile seizures have been historically classified as benign; however, many emerging febrile seizure syndrome behave differently. Lennox was the first clinician to study the background and risk factors for FS and the risk of progression to epilepsy (Lennox, 1949). The American Academy of Pediatricś (AAP) committee of quality improvement published the first evidence-based practice parameters for FS (Baumann & Duffer, 2000). The International League Against Epilepsy (ILAE) then developed a clearer consensus regarding the recognition and treatment of children with FS (Capovilla, Mastrangelo, Romero, & Vigevano, 2009). More recently, the American Academy Pediatrics (AAP) has announced a standard definition of febrile seizures as any seizure associated with fever of > 38 °C (rectal or tympanic), but without central nervous infection (CNS), metabolic disturbance or history of afebrile seizures, in child aged 6 months to 5 years (American Academy of Pediatrics, 2008).They are the result of a particular sensitive to fever in the developing brain, have a mayor genetic predisposition, and nearly always have a being outcome.

Complex febrile seizures are a common cause for an Emergency Department (ED) visit. The prevalence of febrile seizures is ~2-5% in the United States, and approximately 25-35% of febrile seizures are classified as complex.1-4 Complex febrile seizures (CFSs) are defined as those with focality, prolonged duration (greater than 15 minutes), and/or multiple seizures within 24 hours.2 Approximately 20- 30% of children with a first febrile seizure will have subsequent febrile seizures.3-6 There are currently no clinical practice guidelines regarding management of CFS in the US. There are international guidelines by the Italian league against epilepsy (the Ad Hoc Task Force of LICE Guidelines)[5], the Joint Working Group of the Research Unit of the Royal College of Physicians and the British Paediatric Association Commission[6] and the world health organization guidelines[7], all calling for routine admission for observation for all patients presenting with CFS. These recommendations echo algorithms published in several review articles in the medical literature .[8, 9] Recent literature suggest that routine lumbar puncture and urgent neuroimaging are low yield and are not indications for admission after CFS.9-13 Thus indication for admission would typically be for observation or supportive care. Recommendations on if and in what time frame to perform EEG after CFS vary.2,3,14 . There is limited data on the utility of EEG in the acute period (<72 hours) after first time CFS.11, 15-17 Early EEG could be performed as inpatient or outpatient in most centers, but this study focuses on the inpatient setting The goals of this study were to assess the risk of recurrent seizures during admission and to determine whether EEG in the in the first 72 hours affects acute inpatient management.