scholarly journals Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

2020 ◽  
Vol 37 (6) ◽  
pp. 1045-1054
Author(s):  
Suat Toraman
Keyword(s):  
Epileptic Seizure ◽  
Classification Accuracy ◽  
Neurological Disease ◽  
Significant Contribution ◽  
World Population ◽  
Seizure Prediction ◽  
Minute Interval ◽  
Three Stages ◽  
Preictal State

Epilepsy is a neurological disease affecting almost 1% of world population. Predicting a possible seizure will make a significant contribution to improving the quality of life of patients suffering from this disease. One of the most important steps in seizure prediction studies is the preictal activity recognition stage. In many previous studies, the preictal state was determined to end at the onset of the seizure, which makes it difficult for the physician to intervene in the patient in a possible seizure. In the proposed method, unlike previous studies, the preictal state was determined as the 30-minute interval ending 30 minutes before the onset of an epileptic seizure. The method consisted of three stages; (I) preictal and interictal activities were divided into five-second segments, (ii) the separated signals were converted into spectrograms, and (iii) the spectrogram images were classified using three different pre-trained CNN models (VGG19, ResNet, DenseNet) and the results were compared among these models. Classification was performed separately using the predetermined four EEG channels for 20 cases in the CHB-MIT dataset. The best classification accuracy value in preictal/interictal discrimination (91.05%) was obtained on channel 8 (P3-O1). An important contribution of this study was that the proposed approach provided important information about the preictal and interictal discrimination of the section 30 minutes before the onset of seizures. In addition, by examining the four channels separately, channel-based information on preictal/interictal discrimination was also obtained. Based on these results, we consider that the proposed method will bring a different perspective to seizure prediction studies.

scholarly journals Seizure Susceptibility Prediction in Uncontrolled Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Nhan Duy Truong ◽  
Yikai Yang ◽  
Christina Maher ◽  
Levin Kuhlmann ◽  
Alistair McEwan ◽  
...  
Keyword(s):  
Epileptic Seizure ◽  
Brain Dynamics ◽  
False Alarms ◽  
Main Body ◽  
Seizure Susceptibility ◽  
Seizure Prediction ◽  
Clinical Benefits ◽  
Computational Research ◽  
Potential Biomarkers

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of “What are the true biomarkers for seizure prediction” or even “Is there a valid biomarker for seizure prediction?” are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.

scholarly journals A Highly Effective Route for Real-Time Traffic Using an IoT Smart Algorithm for Tele-Surgery Using 5G Networks

2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Radwan S. Abujassar ◽  
Husam Yaseen ◽  
Ahmad Samed Al-Adwan
Keyword(s):  
Live Streaming ◽  
Data Traffic ◽  
Data Packets ◽  
Real Time Traffic ◽  
Medical Quality ◽  
Optimal Smoothing ◽  
Patient Will ◽  
Three Stages ◽  
Exchange Data

Nowadays, networks use many different paths to exchange data. However, our research will construct a reliable path in the networks among a huge number of nodes for use in tele-surgery using medical applications such as healthcare tracking applications, including tele-surgery which lead to optimizing medical quality of service (m-QoS) during the COVID-19 situation. Many people could not travel due to the current issues, for fear of spreading the covid-19 virus. Therefore, our paper will provide a very trusted and reliable method of communication between a doctor and his patient so that the latter can do his operation even from a far distance. The communication between the doctor and his/her patient will be monitored by our proposed algorithm to make sure that the data will be received without delay. We test how we can invest buffer space that can be used efficiently to reduce delays between source and destination, avoiding loss of high-priority data packets. The results are presented in three stages. First, we show how to obtain the greatest possible reduction in rate variability when the surgeon begins an operation using live streaming. Second, the proposed algorithm reduces congestion on the determined path used for the online surgery. Third, we have evaluated the affection of optimal smoothing algorithm on the network parameters such as peak-to-mean ratio and delay to optimize m-QoS. We propose a new Smart-Rout Control algorithm (s-RCA) for creating a virtual smart path between source and destination to transfer the required data traffic between them, considering the number of hops and link delay. This provides a reliable connection that can be used in healthcare surgery to guarantee that all instructions are received without any delay, to be executed instantly. This idea can improve m-QoS in distance surgery, with trusted paths. The new s-RCA can be adapted with an existing routing protocol to track the primary path and monitor emergency packets received in node buffers, for direct forwarding via the demand path, with extended features.

scholarly journals Clinical differentiation of psychogenic non-epileptic seizure: a practical diagnostic approach

2021 ◽  
Vol 57 (1) ◽  
Author(s):  
I. Putu Eka Widyadharma ◽  
Andreas Soejitno ◽  
D. P. G. Purwa Samatra ◽  
Anna M. G. Sinardja
Keyword(s):  
Epileptic Seizure ◽  
Diagnostic Yield ◽  
Relevant Information ◽  
Seizure Semiology ◽  
Unstructured Search ◽  
Baseline Characteristics ◽  
Antiepileptic Medications ◽  
Eeg Pattern

Abstract Background Psychogenic non-epileptic seizure (PNES) has long been the counterpart of epileptic seizure (ES). Despite ample of evidence differentiating the two, PNES mistakenly diagnosed as ES was still common, resulting in unnecessary exposure to long-term antiepileptic medications and reduced patient’s and caregiver’s quality of life, not to mention the burgeoning financial costs. Objectives In this review, we aimed to elucidate various differences between PNES and epileptic seizure with respect to baseline characteristics, seizure semiology, EEG pattern, and other key hallmark features. Methods An unstructured search was carried out in PubMed, MEDLINE, and EMBASE using keywords pertinent to PNES and ES differentiation. Relevant information was subsequently summarized herein. Results PNES differs significantly with ES in terms of baseline characteristics, prodromal symptoms, seizure semiology, presence of pseudosleep, and other hallmark features (for instance provoking seizure with suggestion). The combined approach, if applied appropriately, can yield high diagnostic yield. Conclusions PNES can be clearly differentiated from ES via careful adherence to a set of valid clinical cues. The summarized clinical hallmarks is highly useful to prevent unnecessary ES diagnosis and treatment with AEDs.

scholarly journals A Review on Wearable Epileptic Seizure Prediction System

2021 ◽  
Vol 1916 (1) ◽  
pp. 012075
Author(s):  
V Seethalakshmi ◽  
P Naveenkumar ◽  
G Kavin Prabu ◽  
S Praveen Kumaar
Keyword(s):  
Epileptic Seizure ◽  
Prediction System ◽  
Seizure Prediction ◽  
Epileptic Seizure Prediction

scholarly journals Measurements of Local Sources of Particulates with a Portable Monitor along the Coast of an Insular City

2020 ◽  
Vol 13 (1) ◽  
pp. 261
Author(s):  
Christos Petsas ◽  
Marinos Stylianou ◽  
Antonis Zorpas ◽  
Agapios Agapiou
Keyword(s):  
Air Quality ◽  
Significant Contribution ◽  
Anthropogenic Activities ◽  
Portable Monitor ◽  
Traffic Jam ◽  
Modern Cities ◽  
Anthropogenic Processes ◽  
The City ◽  
Coastal Front

The air quality of modern cities is considered an important factor for the quality of life of humans and therefore is being safeguarded by various international organizations, concentrating on the mass concentration of particulate matter (PM) with an aerodynamic diameter less than 10, 2.5 and 1 μm. However, the different physical and anthropogenic processes and activities within the city contribute to the rise of fine (<1 μm) and coarse (>1 μm) particles, directly impacting human health and the environment. In order to monitor certain natural and anthropogenic events, suspecting their significant contribution to PM concentrations, seven different events taking place on the coastal front of the city of Limassol (Cyprus) were on-site monitored using a portable PM instrument; these included both natural (e.g., dust event) and anthropogenic (e.g., cement factory, meat festival, tall building construction, tire factory, traffic jam, dust road) emissions taking place in spring and summer periods. The violations of the limits that were noticed were attributed mainly to the various anthropogenic activities taking place on-site, revealing once more the need for further research and continuous monitoring of air quality.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals The Potential Preventive Effect of Pregnancy and Breastfeeding on Multiple Sclerosis

2021 ◽  
Vol 84 (2) ◽  
pp. 71-84
Author(s):  
Mohsen Ali Alhomoud ◽  
Abdul Sattar Khan ◽  
Iftetah Alhomoud
Keyword(s):  
Multiple Sclerosis ◽  
Neurological Disease ◽  
Literature Search ◽  
Current Evidence ◽  
Obstetric Outcomes ◽  
Preventive Effect ◽  
Third Trimester ◽  
The Third ◽  
The Central Nervous System

<b><i>Background:</i></b> Multiple sclerosis (MS) is an inflammatory demyelinating chronic neurological disease that affects the central nervous system of young adults and their quality of life. Several studies have investigated the effects of pregnancy and breastfeeding on MS. However, the evidence regarding the influence of pregnancy and breastfeeding on MS is still accumulating. This review aimed to summarize the current evidence regarding the effects of pregnancy and breastfeeding on MS. <b><i>Summary:</i></b> A systematic electronic literature search of the PubMed and Embase databases was conducted to determine relevant published articles. The eligible studies were summarized and evaluated in tables. <b><i>Key Messages:</i></b> The majority of the studies indicated that pregnancy appears to lower the rate of MS relapses, particularly in the third trimester. The evidence regarding the effect of breastfeeding on MS remains inconsistent. Despite reports of negative obstetric outcomes in some pregnant women with MS, pregnancies in women with MS should not be categorized as high-risk pregnancies.

scholarly journals Volatile, Sensory and Functional Properties of HydroSOS Pistachios

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 158 ◽  
Author(s):  
Luis Noguera-Artiaga ◽  
Paola Sánchez-Bravo ◽  
David Pérez-López ◽  
Antoni Szumny ◽  
Ángel Calin-Sánchez ◽  
...  
Keyword(s):  
Functional Properties ◽  
Sensory Properties ◽  
Morphological Properties ◽  
World Population ◽  
Stem Water Potential ◽  
Regulated Deficit Irrigation ◽  
Total Polyphenol ◽  
Highest Weight ◽  
Stem Water

Climate change, the increase in world population, and the intensification of urban and industrial activities, will cause a shortage of water for agriculture. This situation requires conscientious studies to manage water deficits without affecting the quality of the crops. In this study, regulated deficit irrigation (RDI) strategies and three rootstocks (P. atlantica, P. integerrima, and P. terebinthus) were applied to pistachio cultivation to study the quality of fruits obtained based on the morphological, functional, aroma, and their sensory properties. The results obtained demonstrated that RDI T1 (during phenological phase II of cultivation the stem water potential was maintained around −1.5 MPa) led to pistachios with same morphological properties, total polyphenol content, antioxidant activity, volatile composition, sensory properties, better profile of fatty acids, and being the favorite ones for international consumers, as compared to pistachios obtained under full irrigation treatments. On the other hand, when P. integerrima was used, pistachios obtained had the highest weight, the lowest content of sucrose and the best functional properties.

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