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Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical s...
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According to the World Health Organization (WHO), epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due...
Background: As the most prevalent noncontagious neurologic condition, epilepsy is an important cause of mortality and disability in children, and its etiology is an important issue. Objectives: Epilepsy may be induced by different risk factors, some of which may be unclear; therefore, this study aimed to evaluate the risk factors of seizures in chi...
Electroencephalography (EEG) is essential for tracking brain activity and identifying seizure effects. However, epileptic behaviour can only be detected after a specialist has carefully analysed all EEG recordings along with a proper history of the patient. A skilled physician is required for the right epilepsy diagnosis and therapy. But most of th...
![Figure 2.6: An example of scalp potential [17].](profile/Duong-Nhu-5/publication/368761956/figure/fig4/AS:11431281122210616@1677243091465/An-example-of-scalp-potential-17_Q320.jpg)
Epilepsy is the most common chronic neurological disorder. Clinical neurologists use Electroencephalography (EEG) to record the voltage fluctuations within the brain via surface scalp electrodes. The EEG recording of an epileptic patient may show interictal epileptiform discharges (IEDs), which are intermittent electrophysiological events occurring...
Neurologists visually inspect electroencephalogram (EEG) reports to get the epilepsy diagnosis. Scholars have suggested automated techniques to detect the ailment due to the lengthy process and global shortage of specialists. Most research in the past years has been conducted utilizing machine learning methods. But following the development of deep...
Citations
... Beta waves, linked with active cognitive engagement, have been reported to increase phase-amplitude coupling with gamma waves during seizures, offering a potential biomarker for seizure detection [43]. Alterations in gamma activity have been correlated with the onset and spread of seizure activity, making it a potential feature for seizure detection algorithms [44]. Lastly, Hjorth complexity extends the analysis by offering a measure of the signal's overall volatility and unpredictability, which is inherently higher during seizures. ...
Objective:
The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.
Approach:
The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design (CAD) modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial EEG recordings in epilepsy patients.
Significance:
The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording. This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.
... Researchers have been studying automated seizure identification from EEG data since the 1970s [9]. Models are created to distinguish between brain signal patterns and epileptic seizure patterns. ...
Natural Language Processing (NLP) is an ever-evolving field of computer science that involves the development of algorithms that can process, analyze and understand human language. One of the most exciting areas of NLP is the creation of NLP language models with applications across almost every industry. However, most people only associate NLP with its traditional use in language translation, sentiment analysis, and chatbots. In reality, there are many less-common uses for NLP models that have the potential to transform businesses, improve customer experiences, and even save lives. In the healthcare industry, NLP models can be used to analyze unstructured medical data and help diagnose and treat patients more efficiently. For example, NLP can be used to analyze clinical notes, lab results, and other data combing through vast amounts of data to identify patterns and create targeted treatment plans. NLP-based medical diagnosis is still in its infancy, but it has the potential to revolutionize the healthcare industry in the coming years. This article explores a less common use of machine-learning language models built on transformed EEG data for epilepsy prediction using the Kolmogorov-Chaitin algorithmic complexity as the first step in generating text-like data, which are finally used for building machine learning models.
... The timely and accurate detection of seizures is critical for the appropriate diagnosis and management of epilepsy. Seizure detection using wearables enables healthcare professionals to devise tailored treatment plans and monitor the efficacy of therapeutic interventions [5][6][7][8]. Furthermore, the identification of seizure patterns can help prevent potential injury and improve the overall quality of life of epilepsy patients [9][10][11]. ...
This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.
... One of the most widespread algorithms that researchers have used for EEG classification is the SVM classifier algorithm. The SVM is a supervised ML model that can classify high-dimensional feature space based on the hyperplane (Shanir et al., 2018;Wang et al., 2022). This classification algorithm reduces the time required for the learning phase by transforming the prediction problem into an optimization problem and has better accuracy and speed than the other algorithms. ...
Background
Decision‐making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers’ personality traits (i.e., risk‐taking or risk‐averse) in recent years. Although there are findings of signal decision‐making and brain activity, the implementation of an intelligent brain‐based technique to predict risk‐averse and risk‐taking managers is still in doubt.
Methods
This study proposes an electroencephalogram (EEG)‐based intelligent system to distinguish risk‐taking managers from risk‐averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time‐frequency domain analysis method, was used on resting‐state EEG data to extract statistical features. Then, a two‐step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features.
Results
Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1‐measure, indicating that machine learning (ML) models can distinguish between risk‐taking and risk‐averse managers using the features extracted from the alpha frequency band in 10 s analysis window size.
Conclusions
The findings of this study demonstrate the potential of using intelligent (ML‐based) systems in distinguish between risk‐taking and risk‐averse managers using biological signals.
... This might be because of the metamorphic pattern of focal seizures in EEG in which, as the seizure ends, rhythmic waves or sequential spikes change to a slow wave pattern that gradually decreases in frequency. [12] Also, this might be because most of the aware focal seizures may not be associated with discernible changes in routine scalp EEG [13], and the present study had 27.27 % focal aware seizures with normal EEG patterns. This might also be because focal and generalized seizure disorders show some overlap of both clinical and electrographic manifestations, and the entity of unihemispheric epilepsies blurs the boundaries further [14]. ...
Seizure disorders are a major public health problem in a developing country like India. Epilepsy characterized by recurrent unprovoked seizures is a common heterogeneous neurological problem in children that exerts a significant medical, physical, psychological, social, and economic challenge. This study evaluated the importance of the available diagnostic modalities, EEG and MRI, which could influence the management, prognosis and recurrence of unprovoked seizures. The aims and objectives: To determine the role of Electroencephalography and MRI BRAIN in evaluating children presenting with unprovoked seizures. Methodology: The present study was a hospital-based observational study carried out during the period of January 2021 to December 2021 of 70 children who presented with unprovoked seizures to the Department of Paediatrics, Niloufer Hospital, Hyderabad. Results: Among the 70 children who were investigated, EEG showed abnormal findings in 45 (64.29 %) cases. Out of these, the majority of 32 (45.71 %) cases had generalized seizures, and 13 (18.57 %) cases were focal seizures. MRI showed abnormal findings in 30 (42.86 %) cases, and an equal percentage of cases, 15 (21.43 %) of generalized seizures and focal seizures, were having abnormal MRI findings out of the 30 cases with abnormal MRI findings. But, when studied among the individual seizure subtype, a major proportion of focal seizure (68.18 %) cases out of 22 focal seizures had abnormal MRI findings when compared to 31.25 % of generalized seizure cases out of 48 generalized seizure cases with abnormal MRI findings. Conclusion: MRI can identify most of the structural brain abnormalities, and EEG is useful to clearly identify the region of the epileptogenic foci. Therefore, EEG and MRI were useful in identifying a possible cause for unprovoked seizures in children
... Additionally, the procedure may be required in some applications such as wearable devices where using a large number of channels is impractical [26]. Channel selection can be performed using different approaches, whether they are statistical approaches [11,22,36,62,75,76,113], data-driven approaches [14,88,[116][117][118], wrapper approaches [119], or from prior knowledge based on previous studies [120,121]. ...
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
Electroencephalography (EEG) is a non-invasive technique that is used to record the electrical activity of the brain. EEG signals are widely used in the diagnosis of various neurological and psychiatric disorders. EEG signals are complex and noisy, and thus, it is difficult to classify them accurately. In this paper, we have evaluated the performance of two popular machine learning algorithms, namely, Random Forest (RF) and Support Vector Machine (SVM), for classifying EEG signals. The performance of the algorithms was evaluated on a publicly available dataset of EEG signals. The analysis has been done on Bonn University EEG database; the analysis of methodologies signifies that the proposed improved random forest method performs superior to that of conventional random forest as well as support vector machine-based approach.
Neurons are simple cell structure of the neuronal system. Brain networks are formed from a single neuron to highly complex, interconnected neurons (nearly 100 billion).The interconnected neurons function includes excitation and inhibition activity. The imbalance between excitation and inhibition mechanisms of neurons, causing seizure/epilepsy, are known as an ictogenic mechanism. In particular, ictogenic mechanisms caused by genetic factors are attributed to epilepsy development. Study of epileptic genetic polymorphism, mitochondrial genes, transporter genes, and signalling pathways are reported by several molecular genetic tools including NGS, WGS, and WES. The genetics of epilepsy reached to peaks and still extending the branches to solve the mysteries behind the brain and epileptic/seizure causing genes. Genes such as, AQP4, SESN3, ARX, NTNG1, NTNG2 and WWOX requires more attention. In light of the aforementioned, this review examines the brain network during epilepsy, as well as contemporary studies on molecular genetics, gene annotation and analysis, epilepsy treatments before conclusion.
Acanthamoeba doğada yaygın olarak bulunan patojenlerdir. Çeşitli su kaynakları, toprak, hava, toz, tıbbi cihazlar ve kullanımı artan kontakt lensler gibi birçok yerde yaşamlarını sürdürebilirler. Bu kaynaklardan herhangi bir yolla insanlara bulaşta oküler enfeksiyon olan Acanthamoeba keratiti, beyinde ölümcül sonuçlanan granülomatöz amibik ensefalit, kutanöz akanthamoebiasis, otitis, kronik sinüzit, kutanöz ülser gibi çeşitli parazitozlara sebep olurlar. Bu çalışmada literatürdeki Acanthamoeba’da tolerans testleri hakkında yapılan çalışmaların genel olarak sunulması amaçlanmıştır. Acanthamoeba spp.’nin termotolerans ve osmotolerans deneylerinin araştırılması için PubMed, Google Akademik arama motorları kullanılarak geniş bir literatür taraması yapılmıştır. Yaptığımız literatür taramaları sonucunda ülkemizde ve dünyada Acanthamoeba’nın termotolerans ve osmotolerans deneyleri hakkında oldukça fazla çalışma bulunmaktadır. Elde ettiğimiz verilere göre farklı genotiplere sahip hem çevresel hem de klinik vakalardan izole edilen suşların bir kısmını yüksek sıcaklık (37°C-42 °C) ve yüksek osmolarite seviyelerini (0,5 M-1 M mannitol) tolere edebilirken, diğer birçok suşun özellikle keratit gibi klinik vakalardan izole edilen suşların bu seviyeleri tolere edemediği rapor edilmiştir. Bu sebeple termotolerans ve osmotolerans deneylerinin sonuçlarında tutarsızlık oldukça fazladır. Bu tutarsızlıkların açıklığa kavuşabilmesi ve patojenitesini kesin olarak belirlemek için in vivo deneyler dahil olmak üzere daha fazla deneylere ihtiyaç vardır.
