Artificial Intelligence based prediction of seizures for Epileptic Patients: IoT based Cost effective Solution

A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

Epilepsy is one of the most common and serious neurological disorders which is characterized by recurrent unprovoked seizures. Its prevalence ranges from 0.5% to 1% of the population in developed countries, and even higher in developing countries. Around 70 million people worldwide have epilepsy.This research aims to perform a descriptive study on epileptic children 7-18 years old in Herat city of Afghanistan.This descriptive study was performed on 7-18 years old children with epilepsy, which was conducted from in 2021 in Herat city of Afghanistan. The study included 100 patients with epileptic disorders, in this study Encephalography, Magnetic resonance imaging, Computed tomography, and Sociodemographic information were used to describe the patients.The main group of this study included 100 epileptic children. The age of study participants ranged from 7- 18 years and the mean age was 9.21 ± 2.64 years. 46 (51.1%) were males and 44 (48.9%) were females. More often Epilepsy cases were observed in patients with focal seizures. Hereditary predisposition was found as a risk factor and it was statistically significant, the p-value was less than 0.0007. Having status epilepsy was also found a risk factor for epilepsy and the p-value was 0.001. Also, prognostic factors for refractory epilepsy were determined in this study.The risk factors that were found in epileptic patients, were high and comparable with other studies in the world literature. More often epilepsy cases were determined in patients with focal seizures, hereditary predisposition, and having status epilepsy also found as prevalent risk factors for refractory epilepsy. Also, prognostic factors for epilepsy were identified. Negative factors were a high number of seizures, abnormal electroencephalography findings, and early onset of seizures in epileptic patients.

The aim of this study was to establish a correlation between a thermal measurement and a magnetic resonance imaging (MRI) signal during laser-induced interstitial thermotherapy (LITT) in liver. Pig liver was irradiated for 15 minutes with a diode laser at two different powers, 0.5 W (450 J) and 1.5 W (1,350 J). Tissue temperature was monitored every 20 seconds using thermocouples. Thermosensitive MRI sequences (T(1)-weighted Turbo-Flash) were acquired with the same irradiation parameters. Correlation between MRI signals (SI) and temperature measures was defined at two different distances from the fiber (5 and 10 mm). At 0.5 W, temperatures rose progressively up to a maximum increase of 9.5 degrees C at 5 mm and 4 degrees C at 10 mm after 15 minutes. The corresponding MRI signal decreased progressively to -27.6 SI at 5 mm and -18.5 SI at 10 mm. At 1.5 W, temperatures rose dramatically at 5 mm, reaching a plateau. The temperature elevation measured at the end of the irradiation was of 30 degrees C whereas at 10 mm it was only 14.5 degrees C. The MRI signal varied accordingly, remaining inversely proportional to temperature (-76 SI at 5 mm and -35.5 SI at 10 mm). An inversely proportional relationship was observed between MRI signal in sequential Turbo-Flash and temperature. MRI should allow to analyze heat diffusion in the liver, and thus to monitor real-time LITT treatments.

ForeSeiz: An IoMT based headband for Real-time epileptic seizure forecasting

Epilepsy is a group of chronic neurological disorder of the brain that affects around 1-2% of the population across the world. According to the World Health Organization (WHO), epilepsy is characterized by periodically spontaneous seizures, which are usually due to excessive electrical discharges in a group of brain cells. The earlier day electroencephalogram (EEG) signals are useful tool for detection of epileptic seizures.Epileptogenesis is a slow process. After several months of initial insult, spontaneous recurrent seizures begin to appear. Epilepsy is considered to be resolved for individuals who are seizure-free for the last 10 years, with no seizure medicines for the last 5 years. Currently, used drugs available for treating epilepsy have draw backs like Epileptogenesis and other dose-related side effects. In spite of daily treatment, nearly 30% of patients continue to have convulsions and fail to provide a complete cure. Hence, there is a need for another alternative option to control the epileptic seizure and minimize the duration of seizure without taking a medicine and improving the quality of patient’s life. In current scenario the Vagus nerve stimulation (VNS) has become an important tool for controlling the epileptic seizure. Vagus nerve stimulation is used for patient with refractory and drug resistant epilepsy. Various non-drug therapies form preclinical to clinical for controlling seizures in epileptic patients with drug resistance current available have been highlighted in this review.

Epilepsy is a neurological disorder that happens because of the propagation of abnormal signals produced by clusters of neurons in the brain. The majority of those with epileptic seizures can be treated by drug therapies and surgery. However, 25% of the patients with diagnosed epilepsy continue to have seizures. Seizures can cause serious injuries and limit the independence and mobility of an individual. Seizure detection and prediction could lead to a better understanding of seizures and with that help preventing patient injury.This paper discusses extraction and evaluation of nonlinear multivariate features using the cross-bispectral method to help predict epileptic seizure occurrences. These ten statistic features were employed to discriminate pre-ictal from interictal states. Therefore, the features were given to the support vector machine classifier as the input. Outputs were then processed in order to evaluate the sensitivity, false positive rate (FPR) and the prediction time. The proposed method obtained sensitivity of 100% and average FPR of 0.044 per hour by using the “Freiburg epileptic seizure prediction” dataset. This high sensitivity index and low FPR index compared with other studies show the ability of cross-higher-order spectral method to analyze epileptic EEG signals. The proposed method is also fast and easy and may be helpful in other applications of EEG analysis such as sleep stage identification and brain–computer interface.

64-Year-Old Woman With Aphasia and Troponin Elevation

Incomplete hippocampal inversion: diagnostic criteria and effect on epilepsy, seizure localization and therapeutic outcome in children

Seizure prediction based on electroencephalogram (EEG) has great potential to improve patients' life quality. Due to the high heterogeneity in distributions of EEG signals among different patients, conventional studies usually show poor generalization ability when transferring the model to new patients, which also leads to difficulties in clinical applications. To alleviate the challenging issue concerning cross-subject domain shift, we propose a transformer-based domain adversarial model. Our model first adopts a pretrained general neural network to extract common features from the EEG signals of available patients. Then, we design a distiller module and a domain discriminator module to perform domain adaptation training based on a small amount of labeled data from the new-coming patient. During the adaptation process, conditional domain adversarial training with the addition of label information is employed to remove patient-related information from the extracted features to learn a common seizure feature space among different patients. Our proposed seizure prediction method is evaluated on the CHB-MIT EEG database. The proposed model achieves a sensitivity of 79.5%, a false alarm rate (FPR) of 0.258/h, and an AUC of 0.814. Experimental results demonstrate that the proposed method can effectively reduce interpatient domain disparity compared to state-of-the-art methods.

In this work, a T2-T1 switchable superparamagnetic iron oxide nanoprobe with a pH/H2O2 dual response was obtained using a microemulsion method. This novel method for the controllable assembly of small iron clusters followed by their independent modification was reported, which could not be prepared by common synthetic methods. The size of the assembled nanoprobe was uniform and controllable, with a stable T2 magnetic resonance imaging (MRI) signal under a single condition. When the nanoprobe was exposed to the tumor environment, the higher H+ and H2O2 concentrations at the tumor site could dissociate the nanoprobe and redisperse into small iron clusters. When this occurred, the T2 MRI signal was converted into a T1 MRI signal, achieving specific detection of tumors by a pH/H2O2 dual-response T2-T1 MRI.

Background:epilepsy is one of the common neurological disorders and approximately0.5-1% of populations suffers from it. Unfortunately, despite of treatment by antiepileptic drugs, between 20-30% of patients cannot be controlled completely. There are a lot of supplementary therapies for this group of patients and in this study, L-arginine was used as an additional therapy in refractory epileptic patients.Methods: Two groups of epileptic patients (n=21) were selected randomly. Group 1 received lactose as a placebo, and group 2 received L-arginine (twofold of daily need) as a drug for three months. We used a standard questionnaire for evaluating the effect of L-arginine on the quality of life before and after the study.Results: we did not find significant change in the quality of life of patients after using L-arginine, but the epileptic attacks decreased in L-arginine group significantly.Conclusion: L-arginine therapy is safe and can decrease the rate of seizure attacks in refractory epileptic patients, and can be an alternative of ketogenic diets.

Epilepsy is the most common neurological disorder characterized by recurrent seizures due to the abnormality of brain neuronal discharge. One of the tools to help neurologists diagnose epilepsy relies on EEG signals extensively because it can represent the brain's neuron activity. Due to the EEG signal being considered a complex biological signal, the signal complexity-based feature extraction method is suitable for characterizing epileptic EEG signals. One method for extracting EEG signals is entropy which is able to quantify signal complexity. Several previously proposed methods were wavelet entropy and IMF entropy. This study proposed multi-distance signal level difference (MSLD) entropy as a feature extraction method to extract the EEG signal complexity. The decomposed signal from MSLD was then extracted using entropy measurement. In the classification process, a Support Vector Machine (SVM) was applied to classify the normal, pre-ictal, and ictal signals of EEG signals. The highest accuracy of 96% was obtained using cubic SVM as the classifier. The proposed method provides an alternative method for analyzing EEG or other biomedical signals based on signal complexity.

Electroencephalogram (EEG) is commonly used for analyzing numerous psychological states of the brain. However, epileptic seizure prediction from EEG signals is quite challenging since it has more fluctuating information about the behaviour of the brain. Analyzing such long-term EEG signals to discriminate between interictal versus ictal regions is a difficult task. Also, EEG signals can be affected by noises from different sources. The proposed work presents an efficient approach based on Weighted Visibility Graph (WVG) for seizure prediction. In this work, the EEG signals are filtered to remove the artifacts due to power supply noise and then the filtered EEG time series data is segmented. The segmented time series data is converted into a complex network called WVG. This WVG inherits the dynamic characteristics of the EEG signal from which it is created. Features like mean degree, mean weighted degree and mean entropy are extracted from the WVG. These features are used to derive the essential characteristics of EEG from the WVG. Finally, classification is done using Support Vector Machine (SVM). The experiments show that the proposed system provides better performance than the existing methods in prediction of seizure in ictal as well as interictal states of EEG over the benchmark dataset.

The epilepsy is a common neurological disease caused by a neuronal electric activity imbalance in any side of the brain, named epileptic focus. The epilepsy is characterized by recurrent and sudden seizures. Recently, researchers found that approximately 50% of epileptic patients feel auras (subjective phenomenon which precedes and indicates an epileptic seizure onset) associated to a physiological anomaly. In this research, a non-invasive seizure prediction methodology is developed in order to improve the quality of life of the patients with epilepsy, alerting them about potential seizure and avoiding falls, injuries, wounds or even death. The research addresses the recognition of patterns in electroencephalographic (EEG) and electrocardiographic (ECG) signals taken from 7 patients with focal epilepsy whom are treated at the Instituto de Epilepsia y Parkinson del Eje Cafetero-NEUROCENTRO-. The biosignals were independently analyzed, at least 15 minutes before the seizure onset and in periods with no seizure were considered. The methodology considers the generation of features computed over the discrete wavelet transform of the EEG signal and others related to the heart rate variability in the ECG signal. Using feature selection techniques such as Sequential Forward Selection (SFS) with classification algorithms as cost functions (linear-Bayes and k-nearest neighbors classifier), we found which features have the most relevant information about pre-ictal state and which of them are the most appropriated for seizure forecasting, therefore we found that ECG signal could be a potential resource for predicting epileptic seizures, and we concluded that there are patterns in EEG and ECG signals that, via machine learning algorithms, can predict the epileptic seizure onset.

Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.