Arpaia Pasquale, Cacciapuoti Maria, Cataldo Andrea, Criscuolo Sabatina, De Benedetto Egidio, Masciullo Antonio, Pesola Marisa, Schiavoni Raissa
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy.
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy.
Sensors (Basel). 2024 Dec 5;24(23):7794. doi: 10.3390/s24237794.
This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer's disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as well as the selection of key parameters such as embedding dimension () and similarity criterion (), which often result in inconsistent outcomes when applied to multivariate data, such as electroencephalography signals. To address these challenges and to generalize the possibility of adopting Multiscale Fuzzy Entropy as a diagnostic tool for Alzheimer's disease, this research explores amplitude transformation preprocessing on electroencephalography signals in Multiscale Fuzzy Entropy calculation across varying parameters. The statistical analysis of the obtained results demonstrates that amplitude transformation preprocessing significantly enhances Multiscale Fuzzy Entropy's ability to detect Alzheimer's disease, achieving higher and more consistent significant comparison percentages, with an average of 73.2% across all parameter combinations, compared with only one raw data combination exceeding 65%. Clustering analysis corroborates these findings, showing that amplitude transformation improves the differentiation between Alzheimer's disease patients and healthy subjects. These results highlight the potential of amplitude transformation to stabilize Multiscale Fuzzy Entropy performance, making it a more reliable tool for early Alzheimer's disease detection.
本研究探讨了幅度变换在增强多尺度模糊熵使用脑电图信号检测阿尔茨海默病的性能和鲁棒性方面的有效性。多尺度模糊熵是一种复杂性度量，对信号幅度的个体内和个体间变化以及诸如嵌入维数（）和相似性准则（）等关键参数的选择特别敏感，当应用于多变量数据（如脑电图信号）时，这些参数往往会导致不一致的结果。为了应对这些挑战并推广将多尺度模糊熵用作阿尔茨海默病诊断工具的可能性，本研究在多尺度模糊熵计算中针对不同参数对脑电图信号进行幅度变换预处理。对所得结果的统计分析表明，幅度变换预处理显著增强了多尺度模糊熵检测阿尔茨海默病的能力，实现了更高且更一致的显著比较百分比，所有参数组合的平均百分比为73.2%，而仅一种原始数据组合超过65%。聚类分析证实了这些发现，表明幅度变换改善了阿尔茨海默病患者与健康受试者之间的区分。这些结果突出了幅度变换稳定多尺度模糊熵性能的潜力，使其成为早期阿尔茨海默病检测更可靠的工具。
