A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia

PLoS ONE, Год журнала: 2025, Номер 20(3), С. e0320219 - e0320219

Опубликована: Март 25, 2025

Neuroinflammation is a key feature of human neurodisease including neuropathy and neurodegenerative disease driven by the activation microglia, immune cells nervous system. During microglia release pro-inflammatory cytokines as well reactive oxygen species (ROS) that can drive local neuronal glial damage. Phytocannabinoids are an important class naturally occurring compounds found in cannabis plant ( Cannabis sativa ) interact with body’s endocannabinoid receptor Cannabidiol (CBD) prototype phytocannabinoid anti-inflammatory properties observed animal models. We measured ROS (HMC3) using CellROX, fluorescent dynamic indicator. tested effect CBD on level presence three known activators: lipopolysaccharide (LPS), amyloid beta (A β 42 ), immunodeficiency virus (HIV) glycoprotein (GP120). Confocal microscopy images within were coupled to deep learning model convolutional neural network (CNN) predict responses. Our study demonstrates platform be used assessment image measure.

Язык: Английский

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EEG epilepsy seizure prediction: the post-processing stage as a chronology

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 3, 2024

Abstract Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity completely controlling a patient’s epilepsy, prediction plays significant role in clinical management and providing new therapeutic options such as warning or intervention devices. Seizure algorithms aim identify preictal period that Electroencephalogram (EEG) signals can capture. However, this is associated with substantial heterogeneity, varying among even between seizures from same patient. The present work proposes patient-specific algorithm using post-processing techniques explore existence set chronological events brain activity precedes seizures. study was conducted 37 Temporal Lobe Epilepsy (TLE) EPILEPSIAE database. designed methodology combines univariate linear features classifier based on Support Vector Machines (SVM) two handle pre-seizure temporality an easily explainable way, employing knowledge network theory. Chronological Firing Power approach, we considered sequence three separated time. Cumulative assumed overlapping events. These methodologies were compared approach typical machine learning pipeline. We Prediction horizon (SPH) 5 mins analyzed several values for Occurrence Period (SOP) duration, 10 55 mins. Our results showed may improve performance. This strategy performed above chance 62% patients, whereas only validated 49% its models.

Язык: Английский

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The goal of explaining black boxes in EEG seizure prediction is not to explain models’ decisions

Epilepsia Open, Год журнала: 2023, Номер 8(2), С. 285 - 297

Опубликована: Апрель 19, 2023

Many state-of-the-art methods for seizure prediction, using the electroencephalogram, are based on machine learning models that black boxes, weakening trust of clinicians in them high-risk decisions. Seizure prediction concerns a multidimensional time-series problem performs continuous sliding window analysis and classification. In this work, we make critical review which explanations increase models' decisions predicting seizures. We developed three methodologies to explore their explainability potential. These contain different levels model transparency: logistic regression, an ensemble 15 support vector machines, convolutional neural networks. For each methodology, evaluated quasi-prospectively performance 40 patients (testing data comprised 2055 hours 104 seizures). selected with good poor explain Then, grounded theory, how these helped specialists (data scientists working epilepsy) understand obtained dynamics. four lessons better communication between clinicians. found goal is not system's but improve system itself. Model transparency most significant factor explaining decision prediction. Even when intuitive features, it hard brain dynamics relationship models. achieve understanding by developing, parallel, several systems explicitly deal signal changes help develop complete formulation.

Язык: Английский

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Comparison between epileptic seizure prediction and forecasting based on machine learning

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 7, 2024

Epilepsy affects around 1% of the population worldwide. Anti-epileptic drugs are an excellent option for controlling seizure occurrence but do not work one-third patients. Warning devices employing prediction or forecasting algorithms could bring patients new-found comfort and quality life. These would attempt to detect a seizure's preictal period, transitional moment between regular brain activity seizure, relay this information user. Over years, many studies using Electroencephalogram-based methodologies have been developed, triggering alarm when detecting period. Recent suggested shift in view from forecasting. Seizure takes probabilistic approach problem question instead crisp prediction. In field study, triggered symbolize detection period is substituted by constant risk assessment analysis. The present aims explore capable establish comparison with results. Using 40 EPILEPSIAE database, we developed several patient-specific different classifiers (a Logistic Regression, 15 Support Vector Machines ensemble, Shallow Neural Networks ensemble). Results show increase sensitivity relative up 146% number that displayed improvement over chance 300%. results suggest methodology may be more suitable warning than one.

Язык: Английский

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Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Апрель 8, 2024

Abstract Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients’ quality life, as timely intervention can mitigate the impact seizures. In research field, it critical identify preictal interval, transition from regular brain activity seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies prediction, few been clinically applicable. Recent underlined dynamic nature EEG data, characterised by data changes time, known concept drifts, highlighting need automated methods detect and adapt these changes. study, we investigate effectiveness automatic drift adaptation in seizure prediction. Three patient-specific approaches 10-minute horizon are compared: algorithm incorporating window adjustment method optimising performance Support Vector Machines (Backwards-Landmark Window), data-batch (seizures) selection using logistic regression (Seizure-batch Regression), integration classifiers (Dynamic Weighted Ensemble). These incorporate retraining process after each use combination univariate linear features SVM classifiers. The Firing Power was used post-processing technique generate alarms before were compared control approach on typical machine learning pipeline, considering group 37 Temporal Lobe Epilepsy EPILEPSIAE database. best-performing Window) achieved results 0.75 ± 0.33 sensitivity 1.03 1.00 false positive rate per hour. This new strategy performed above chance 89% surrogate predictor, whereas only validated 46%.

Язык: Английский

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Evaluation of the Relation between Ictal EEG Features and XAI Explanations

Brain Sciences, Год журнала: 2024, Номер 14(4), С. 306 - 306

Опубликована: Март 25, 2024

Epilepsy is a neurological disease with one of the highest rates incidence worldwide. Although EEG crucial tool for its diagnosis, manual detection epileptic seizures time consuming. Automated methods are needed to streamline this process; although there already several works that have achieved this, process by which it executed remains black box prevents understanding ways in machine learning algorithms make their decisions. A state-of-the-art deep model seizure and three databases were chosen study. The developed models trained evaluated under different conditions (i.e., distinct levels overlap among data windows). classifiers best performance selected, then Shapley Additive Explanations (SHAPs) Local Interpretable Model-Agnostic (LIMEs) employed estimate importance value each channel Spearman’s rank correlation coefficient was computed between features signals values. results show database training may affect classifier’s performance. most significant accuracy 0.84, 0.73, 0.64 CHB-MIT, Siena, TUSZ datasets, respectively. In addition, displayed negligible or low Finally, concluded values (generated SHAP LIME) been absent even high-performance models.

Язык: Английский

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Deep learning for automated detection of generalized paroxysmal fast activity in Lennox–Gastaut syndrome

Epilepsy & Behavior, Год журнала: 2023, Номер 147, С. 109418 - 109418

Опубликована: Сен. 6, 2023

Язык: Английский

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Classifier Combination Supported by the Sleep-Wake Cycle Improves EEG Seizure Prediction Performance

IEEE Transactions on Biomedical Engineering, Год журнала: 2024, Номер 71(8), С. 2341 - 2351

Опубликована: Фев. 21, 2024

Seizure prediction is a promising solution to improve the quality of life for drug-resistant patients, which concerns nearly 30% patients with epilepsy. The present study aimed ascertain impact incorporating sleep-wake information in seizure prediction.

Язык: Английский

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On the performance of seizure prediction machine learning methods across different databases: the sample and alarm-based perspectives

Frontiers in Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Июль 15, 2024

Epilepsy affects 1% of the global population, with approximately one-third patients resistant to anti-seizure medications (ASMs), posing risks physical injuries and psychological issues. Seizure prediction algorithms aim enhance quality life for these individuals by providing timely alerts. This study presents a patient-specific seizure algorithm applied diverse databases (EPILEPSIAE, CHB-MIT, AES, Ecosystem). The proposed undergoes standardized framework, including data preprocessing, feature extraction, training, testing, postprocessing. Various necessitate adaptations in algorithm, considering differences availability characteristics. exhibited variable performance across databases, taking into account sensitivity, FPR/h, specificity, AUC score. distinguishes between sample-based approaches, which often yield better results disregarding temporal aspect seizures, alarm-based simulate real-life conditions but produce less favorable outcomes. Statistical assessment reveals challenges surpassing chance levels, emphasizing rarity events. Comparative analyses existing studies highlight complexity assessments, given methodologies dataset variations. Rigorous aiming outcomes, importance realistic assumptions comprehensive, long-term, systematically structured datasets future research.

Язык: Английский

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An EEG-based Automatic Classification Model for Epilepsy with Explainable Artificial Intelligence

Опубликована: Июнь 14, 2024

Язык: Английский

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