将机器学习模型应用于利用脑电图多样化算法诊断偏头痛

Hye Kyeong Ko
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引用次数: 0

摘要

这项研究探讨系统研究算法如何利用时间收集分析来诊断偏头痛。通过使用各种算法和当前的统计资源(如脑电图活动和患者病史),这项任务将开发一个预测模型,以识别偏头痛症状和体征的开始时间,从而为患者提供及时和早期的治疗。研究结果将有助于比较这些算法如何影响偏头痛的预测准确性,以及它们如何及早预测偏头痛的出现,以便采取预防性干预措施。此外,还可以开展研究,检验该模型在患者实时监测中的应用能力,并确定算法的可操作输入。本作品介绍了新型机器学习算法软件,用于对体温、心率和脑电图指示等功能进行时间序列分析,从而识别偏头痛。论文深入探讨了利用机器学习算法识别偏头痛类型的想法,研究了准确排列适应症的预处理程序,并提供了为评估解决方案的功效而进行的研究结果。观察结果表明,建议的诊断框架能够准确识别偏头痛并对其进行分类,使医疗专业人员能够识别偏头痛的警告迹象,并预测发作开始的时间。这项研究表明,设备学习算法有可能正确、准确地诊断偏头痛,但要获得有关这种情况的更详细信息,还需要进行更多的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Machine Learning models to Diagnosing Migraines with EEG Diverse Algorithms
This study investigates how well time collection analysis may be used by system-studying algorithms to diagnose migraines. Through the use of various algorithms and current statistical resources, such as EEG activity and affected person histories, the mission will develop a predictive model to identify the start of migraine signs and symptoms, allowing for prompt and early management for sufferers. The results will help to compare how the algorithms affect migraine accuracy predictions and how well they forecast migraine presence early enough for preventative interventions. Furthermore, studies may be conducted to examine the model's ability to be employed in real-time patient monitoring and to identify actionable inputs from the algorithms. This work presents novel machine learning algorithms software for time series analysis of functions such as temperature, heart rate, and EEG indications, which can be used to identify migraines. The paper delves into the idea of utilizing machine learning algorithms to identify migraine styles, examines the pre-processing procedures to accurately arrange the indications, and provides the results of a study conducted to evaluate the efficacy of the solution. The observation's results show that the suggested diagnostic framework is capable of accurately identifying and categorizing migraines, enabling medical professionals to recognize the warning indications of migraine and predict when an attack would begin. The examination demonstrates the possibility of devices learning algorithms to correctly and accurately diagnose migraines, but more research is necessary to obtain more detailed information about this situation.
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