Abstract
Parkinson's disease is a widespread neurodegenerative condition necessitating early diagnosis for effective intervention. This paper introduces an innovative method for diagnosing Parkinson's disease through the analysis of human EEG signals, employing a Support Vector Machine (SVM) classification model. this research presents novel contributions to enhance diagnostic accuracy and reliability. Our approach incorporates a comprehensive review of EEG signal analysis techniques and machine learning methods. Drawing from recent studies, we have engineered an advanced SVM-based model optimized for Parkinson's disease diagnosis. Utilizing cutting-edge feature engineering, extensive hyperparameter tuning, and kernel selection, our method achieves not only heightened diagnostic accuracy but also emphasizes model interpretability, catering to both clinicians and researchers. Moreover, ethical concerns in healthcare machine learning, such as data privacy and biases, are conscientiously addressed. We assess our method's performance through experiments on a diverse dataset comprising EEG recordings from Parkinson's disease patients and healthy controls, demonstrating significantly improved diagnostic accuracy compared to conventional techniques. In conclusion, this paper introduces an innovative SVM-based approach for diagnosing Parkinson's disease from human EEG signals. Building upon the IEEE framework and previous research, its novelty lies in the capacity to enhance diagnostic accuracy while upholding interpretability and ethical considerations for practical healthcare applications. These advances promise to revolutionize early Parkinson's disease detection and management, ultimately contributing to enhanced patient outcomes and quality of life.
Abstract (translated)
帕金森病是一种广泛的神经退行性疾病，需要早期诊断以实现有效的干预。本文介绍了一种通过分析人类脑电信号来诊断帕金森病的新颖方法，并采用支持向量机（SVM）分类模型。这项研究为提高诊断准确性和可靠性做出了新的贡献。我们的方法综合了脑电信号分析技术和机器学习方法的全面回顾。从最近的研究中，我们设计了一个优化帕金森病诊断的先进SVM模型。通过采用尖端特征工程、广泛的超参数调整和核选择，我们的方法实现了不仅是诊断准确度的大幅提高，而且强调了模型的可解释性，适用于临床医生和研究人员。此外，本文还认真处理了医疗机器学习领域中的伦理问题，如数据隐私和偏见等。我们通过在帕金森病患者和健康对照者的脑电记录上进行实验，评估了本方法的表现，结果表明，与传统技术相比，其诊断准确度有显著提高。总之，本文介绍了一种基于SVM的诊断帕金森病的新颖方法，其独特之处在于在提高诊断准确性的同时保持可解释性和伦理考虑，为实际医疗应用提供了变革性的支持。这些进步有望彻底改变早期帕金森病检测和管理，最终为患者带来更好的治疗效果和生活质量。
URL
https://arxiv.org/abs/2405.00741
