Scholarlink

<p>Epilepsy, a neurological disorder marked by recurrent seizures, affects millions worldwide, yet predicting and managing seizures remains a major challenge. Recent advances in machine learning (ML) offer new opportunities to enhance diagnostic accuracy, treatment personalisation, and seizure forecasting. ML techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown strong performance in analysing EEG signals for seizure detection and prediction. Support vector machines (SVMs) and ensemble methods have further improved the classification of seizure types and phases. In neuroimaging, ML algorithms aid in localising epileptogenic zones, supporting pre-surgical evaluations. Emerging work also demonstrates the utility of ML in identifying biomarkers, optimising antiepileptic drug selection, and predicting patient-specific treatment responses. Beyond clinical data, deep learning applied to wearable sensor outputs enables real-time monitoring and forecasting of seizure risk, providing valuable tools for daily management. Despite these advancements, challenges remain, including data heterogeneity, interpretability of models, and clinical validation. Continued interdisciplinary research is essential to translate ML innovations into routine practice. By advancing diagnostics, tailoring therapies, and enabling proactive management, ML holds the potential to transform epilepsy care and improve outcomes and quality of life for patients.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities

  • 期刊论文

  • 发表日期：

    2026年4月9日

查看全文
  • Nitu L. Wankhede,
  • Saeed Alshahrani,
  • Arifullah Mohammed,
  • Brijesh G. Taksande,
  • Aman B. Upaganlawar,
  • Milind J. Umekar,
  • Spandana Rajendra Kopalli,
  • Sushruta Koppula,
  • Mayur B. Kale

摘要

Epilepsy, a neurological disorder marked by recurrent seizures, affects millions worldwide, yet predicting and managing seizures remains a major challenge. Recent advances in machine learning (ML) offer new opportunities to enhance diagnostic accuracy, treatment personalisation, and seizure forecasting. ML techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown strong performance in analysing EEG signals for seizure detection and prediction. Support vector machines (SVMs) and ensemble methods have further improved the classification of seizure types and phases. In neuroimaging, ML algorithms aid in localising epileptogenic zones, supporting pre-surgical evaluations. Emerging work also demonstrates the utility of ML in identifying biomarkers, optimising antiepileptic drug selection, and predicting patient-specific treatment responses. Beyond clinical data, deep learning applied to wearable sensor outputs enables real-time monitoring and forecasting of seizure risk, providing valuable tools for daily management. Despite these advancements, challenges remain, including data heterogeneity, interpretability of models, and clinical validation. Continued interdisciplinary research is essential to translate ML innovations into routine practice. By advancing diagnostics, tailoring therapies, and enabling proactive management, ML holds the potential to transform epilepsy care and improve outcomes and quality of life for patients.