The study presents the concept of a computationally efficient machine
learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in
an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG).
We computed different types of entropy from EEG signals and found that Fuzzy
Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We
also investigated different combinations of signal frequency ranges and EEG
channels to accurately diagnose PD. Finally, with a fewer number of features
(11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%.
The most prominent frequency range of EEG signals has been identified, and we
have found that high classification accuracy depends on low-frequency signal
components (0-4 Hz). Moreover, the most informative signals were mainly
received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore,
we assessed the accuracy of the diagnosis of PD using three different lengths
of EEG data (150-1000 samples). Because the computational complexity is reduced
by reducing the input data. As a result, we have achieved a maximum mean
accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with
a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By
reducing the number of features and segment lengths, the computational cost of
classification can be reduced. Lower-performance smart ML sensors can be used
in IoT environments for enhances human resilience to PD.

本研究提出了一个计算效率高的机器学习模型，利用静息 - 状态脑电图（rs-EEG）在物联网环境下对帕金森病进行诊断和监测。研究结果显示，使用模糊熵能够最准确地诊断和监测帕金森病。同时，在信号频率范围和脑电图通道的不同组合下，能够准确诊断帕金森病。通过减少特征数量，作者最高达到了 99.9% 的分类准确率。研究结果还发现，脑电信号中的低频成分（0-4 Hz）对分类准确率起到关键作用。此外，作者还评估了不同长度的脑电图数据对帕金森病诊断的准确率，结果表明减少输入数据可以降低计算复杂度。因此，可以在物联网环境中使用性能较低的智能机器学习传感器，从而增强人类对帕金森病的抵抗力。