ABSTRACT

Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson’s disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CM_TMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CM_TMSOM method performs better than current parametric models.

摘要

神经科学是不同科学学科的组合，它研究神经系统以了解生物学基础。最近，通过考虑不同的统计回归模型，在帕金森病等神经退行性疾病的诊断中的应用变得非常有前景。然而，当实验数据包含离其他观察异常距离的异常观察时，众所周知的统计回归模型可能会给神经退行性疾病的诊断提供误导性结果。本研究的主要成果包括一种新的数学支持方法，除了统计回归模型之外，还可以识别和处理异常值观测值没有直接消除人类面临的巨大和新出现的挑战，例如神经退行性疾病。通过这种方法，提出了一种名为 CM _T MSOM 的新方法，该方法具有强大的凸和连续优化技术（称为圆锥二次规划）的贡献。该方法基于均值偏移离群值回归模型，是结合 M 估计的鲁棒性和 Tikhonov 正则化的稳定性而开发的。我们将我们的方法和其他参数模型应用于帕金森远程监测数据集，这是神经科学中的真实数据集。然后，我们通过使用众所周知的无方法性能度量来比较这些方法。结果表明，CM _T MSOM 方法的性能优于当前的参数模型。