Reliable Seizure Prediction Using Physiological Signals and Machine Learning

使用生理信号和机器学习进行可靠的癫痫发作预测

基本信息

批准号：
10629373
负责人：
Gregory A Worrell
金额：
$ 58.68万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10629373
关键词：
Anticonvulsants Automobile Driving Bilateral Biological Markers Body Temperature Brain Canis familiaris Chronic Classification Clinical Data Analytics Development Device Designs Devices Dose Drug Side Effects Effectiveness Electric Stimulation Electrical Stimulation of the Brain Electrocardiogram Electroencephalography Electrophysiology (science)Epilepsy Event Evoked Potentials Focal Seizure Goals Grant Hippocampus Human Immune Sera Immunologic Factors Immunologic Markers Immunologics Implant Implantable Injection/Infusion Ports Incidence Individual Injury Intelligence Investigation Life Machine Learning Modeling Morbidity - disease rate Neural Network Simulation Partial Epilepsies Patients Pattern Performance Persons Physiological Probability Psychological Impact Research Risk Sampling Seizures Series Signal Transduction Stream Temperature Temperature Sense Therapy trial Time Work circadian convolutional neural network deep learning electric impedance epileptiform implantable device improved learning strategy machine learning method machine learning model multimodal data multimodality pharmacologic prevent prospective psychologic response side effect support vector machine targeted treatment

项目摘要

For most individuals living with epilepsy, seizures are relatively infrequent events occupying a small fraction of their life. Despite spending as little as 0.01% of their lives having seizures (typically only minutes per month), people with epilepsy take anti-seizure drugs (ASD) daily, suffer ASD related side effects, and spend their lives dreading when the next seizure will strike. The apparent randomness of seizures is associated with significant psychological consequences. In addition, despite daily ASD, approximately 1/3 of patients continue to have seizures. We hypothesize that epilepsy can be more effectively treated, both the seizures and their psychological impact, by providing patients with real-time seizure forecasting. There is strong evidence that focal epilepsy is associated with a variable seizure risk that may enable adaptive therapy targeting periods of high seizure probability. Periods of low seizure probability could require lower ASD doses, reducing exposure and side effects. We propose that high seizure probability states will respond to adaptive electrical brain stimulation (aEBS). In addition, patients could alter their activities during periods of high seizure probability to reduce injury and manage their ASD and activities. The hypotheses driving this proposal are that 1.) seizures can be prevented (reduced incidence) by targeted EBS therapy during the pre-ictal state 2.) seizures are not random events, and that brain states associated with low and high seizure probability can be reliably classified using machine learning methods applied to physiologic signals and used to adaptively change EBS parameters. 3.) Furthermore, we propose forecasting can be improved using multi-modal features beyond passive iEEG recordings, including active brain probing with electrical stimulation (impedance & evoked potentials), core temperature, ECG and serum immunological markers. Goal: Develop reliable seizure forecasting (>90% sensitivity) with few false positives (<1% time in warning) and demonstrate modulation of seizure risk and reduction of focal seizures using aEBS.

对于大多数患有癫痫病的人来说，癫痫发作是相对罕见的事件他们的生活。尽管他们的生活只有0.01％的癫痫发作（通常每月只有几分钟），但患有癫痫的人每天服用抗Seizure药物（ASD），遭受与ASD相关的副作用，并度过一生下一次癫痫发作会罢工时令人恐惧。癫痫发作的明显随机性与显着相关心理后果。此外，尽管每天ASD，大约有1/3的患者继续癫痫发作。我们假设癫痫可以更有效地治疗癫痫病，包括癫痫发作及其通过为患者提供实时癫痫发作的预测，心理影响。有强有力的证据表明，局灶性癫痫与可变的癫痫发作风险有关，该风险可能使人适应治疗靶向高癫痫发作概率的时期。低癫痫发作概率的时期可能需要较低 ASD剂量，减少暴露和副作用。我们建议高癫痫发作概率状态将作出回应自适应电脑刺激（AEB）。此外，患者可以在期间改变其活动减少伤害并管理其ASD和活动的高癫痫发作概率。驾驶此提案的假设是1.）可以防止癫痫发作（降低发生率） eBS疗法在第2个状态2.）癫痫发作不是随机事件，而与大脑状态相关的事件使用机器学习方法可靠地对低和高癫痫发作概率可靠地分类生理信号，用于自适应更改EBS参数。 3.）此外，我们提出了预测可以使用无源IEEG录音以外的多模式功能（包括主动脑探测）来改进具有电刺激（阻抗和诱发电位），核心温度，ECG和血清免疫学标记。目标：开发可靠的癫痫发作预测（> 90％灵敏度），很少有误报（<1％的时间<1％警告）并证明使用AEB的癫痫发作风险和减少局灶性癫痫发作的调节。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

DOI：
10.1109/tbme.2016.2586475
发表时间：
2017-05
期刊：
IEEE transactions on bio-medical engineering
影响因子：
0
作者：
Shiao HT;Cherkassky V;Lee J;Veber B;Patterson EE;Brinkmann BH;Worrell GA
通讯作者：
Worrell GA

Intracranial electrophysiological recordings from the human brain during memory tasks with pupillometry.

DOI：
10.1038/s41597-021-01099-z
发表时间：
2022-01-13
期刊：
Scientific data
影响因子：
9.8
作者：
Cimbalnik J;Dolezal J;Topçu Ç;Lech M;Marks VS;Joseph B;Dobias M;Van Gompel J;Worrell G;Kucewicz M
通讯作者：
Kucewicz M

Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast.

DOI：
10.1111/epi.16785
发表时间：
2021-03
期刊：
Epilepsia
影响因子：
5.6
作者：
Payne DE;Dell KL;Karoly PJ;Kremen V;Gerla V;Kuhlmann L;Worrell GA;Cook MJ;Grayden DB;Freestone DR
通讯作者：
Freestone DR

Seizure Forecasting and the Preictal State in Canine Epilepsy.