{"title":"机器学习治疗癫痫:全面探索用于癫痫发作诊断的新型脑电图和磁共振成像技术","authors":"Naily Rehab, Yahia Siwar, Zaied Mourad","doi":"10.1007/s40846-024-00874-8","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>This work focuses on automated epileptic seizure diagnosis (ESD) and prediction (ESP) to clarify the expanding role of machine learning (ML) in epileptic analysis. It outlines the current approaches and challenges in the diagnosis and prognosis of epilepsy and examines the convergence of magnetic resonance imaging (MRI), electroencephalogram (EEG), and ML.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This paper lists current methods for segmentation, localization, feature extraction, diagnosis, and prognosis after providing a brief medical review to distinguish between different forms of epilepsy. A particular focus is on using ML to EEG and MRI data, describing classification techniques to differentiate normal and epileptic activity.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>We highlight the potential of ML-driven methods for computer-aided epilepsy diagnosis and prognosis. We discuss achievements, challenges, and future directions, including devising novel techniques for automated alerts and seizure frequency estimation with minimal computational burden.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>ML interfaces offer new possibilities for real-time seizure diagnosis in refractory epilepsy patients through wearables and implants. This discovery opens the door for improved diagnostic precision and individualized treatment plans in this field by using ML’s capabilities.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3><p>The graphical abstract presents the machine Learning (ML) workflow for epileptic seizure diagnosis (ES) in detail. It begins with collecting data, such as magnetic resonance imaging (MRI) and electroencephalogram (EEG) data. Subsequently, features were extracted from the MRI and EEG data and used to train and evaluate machine learning models. The trained models were then applied to ES classification. Finally, ML algorithms proved to have the potential to revolutionize the diagnosis and treatment of epilepsy. By enabling early detection and personalized treatment, ML algorithms can help improve patient outcomes and quality of life.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning for Epilepsy: A Comprehensive Exploration of Novel EEG and MRI Techniques for Seizure Diagnosis\",\"authors\":\"Naily Rehab, Yahia Siwar, Zaied Mourad\",\"doi\":\"10.1007/s40846-024-00874-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>This work focuses on automated epileptic seizure diagnosis (ESD) and prediction (ESP) to clarify the expanding role of machine learning (ML) in epileptic analysis. It outlines the current approaches and challenges in the diagnosis and prognosis of epilepsy and examines the convergence of magnetic resonance imaging (MRI), electroencephalogram (EEG), and ML.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>This paper lists current methods for segmentation, localization, feature extraction, diagnosis, and prognosis after providing a brief medical review to distinguish between different forms of epilepsy. A particular focus is on using ML to EEG and MRI data, describing classification techniques to differentiate normal and epileptic activity.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>We highlight the potential of ML-driven methods for computer-aided epilepsy diagnosis and prognosis. We discuss achievements, challenges, and future directions, including devising novel techniques for automated alerts and seizure frequency estimation with minimal computational burden.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>ML interfaces offer new possibilities for real-time seizure diagnosis in refractory epilepsy patients through wearables and implants. This discovery opens the door for improved diagnostic precision and individualized treatment plans in this field by using ML’s capabilities.</p><h3 data-test=\\\"abstract-sub-heading\\\">Graphical Abstract</h3><p>The graphical abstract presents the machine Learning (ML) workflow for epileptic seizure diagnosis (ES) in detail. It begins with collecting data, such as magnetic resonance imaging (MRI) and electroencephalogram (EEG) data. Subsequently, features were extracted from the MRI and EEG data and used to train and evaluate machine learning models. The trained models were then applied to ES classification. Finally, ML algorithms proved to have the potential to revolutionize the diagnosis and treatment of epilepsy. 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引用次数: 0
摘要
目的 本研究侧重于癫痫发作的自动诊断(ESD)和预测(ESP),以阐明机器学习(ML)在癫痫分析中不断扩大的作用。本文概述了癫痫诊断和预后方面的现有方法和挑战,并研究了磁共振成像(MRI)、脑电图(EEG)和 ML 的融合。结果我们强调了 ML 驱动的方法在计算机辅助癫痫诊断和预后方面的潜力。我们讨论了取得的成就、面临的挑战和未来的发展方向,包括以最小的计算负担设计出自动警报和癫痫发作频率估算的新技术。结论ML界面通过可穿戴设备和植入物为难治性癫痫患者的实时癫痫发作诊断提供了新的可能性。这一发现为利用 ML 的功能提高该领域的诊断精度和个性化治疗方案打开了大门。图解摘要图解摘要详细介绍了用于癫痫发作诊断(ES)的机器学习(ML)工作流程。它首先收集数据,如磁共振成像(MRI)和脑电图(EEG)数据。然后,从核磁共振成像和脑电图数据中提取特征,用于训练和评估机器学习模型。然后将训练好的模型应用于 ES 分类。最后,ML 算法被证明具有彻底改变癫痫诊断和治疗的潜力。通过实现早期检测和个性化治疗,ML 算法有助于改善患者的预后和生活质量。
Machine Learning for Epilepsy: A Comprehensive Exploration of Novel EEG and MRI Techniques for Seizure Diagnosis
Purpose
This work focuses on automated epileptic seizure diagnosis (ESD) and prediction (ESP) to clarify the expanding role of machine learning (ML) in epileptic analysis. It outlines the current approaches and challenges in the diagnosis and prognosis of epilepsy and examines the convergence of magnetic resonance imaging (MRI), electroencephalogram (EEG), and ML.
Methods
This paper lists current methods for segmentation, localization, feature extraction, diagnosis, and prognosis after providing a brief medical review to distinguish between different forms of epilepsy. A particular focus is on using ML to EEG and MRI data, describing classification techniques to differentiate normal and epileptic activity.
Results
We highlight the potential of ML-driven methods for computer-aided epilepsy diagnosis and prognosis. We discuss achievements, challenges, and future directions, including devising novel techniques for automated alerts and seizure frequency estimation with minimal computational burden.
Conclusion
ML interfaces offer new possibilities for real-time seizure diagnosis in refractory epilepsy patients through wearables and implants. This discovery opens the door for improved diagnostic precision and individualized treatment plans in this field by using ML’s capabilities.
Graphical Abstract
The graphical abstract presents the machine Learning (ML) workflow for epileptic seizure diagnosis (ES) in detail. It begins with collecting data, such as magnetic resonance imaging (MRI) and electroencephalogram (EEG) data. Subsequently, features were extracted from the MRI and EEG data and used to train and evaluate machine learning models. The trained models were then applied to ES classification. Finally, ML algorithms proved to have the potential to revolutionize the diagnosis and treatment of epilepsy. By enabling early detection and personalized treatment, ML algorithms can help improve patient outcomes and quality of life.
期刊介绍:
The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.