EEG complexity-based algorithm using Multiscale Fuzzy Entropy: Towards detection of Alzheimer's disease

被引：9
作者：
Cataldo, Andrea [1 ]
Criscuolo, Sabatina [2 ]
De Benedetto, Egidio [2 ]
Masciullo, Antonio [1 ]
Pesola, Marisa [2 ]
Picone, Joseph [3 ]
Schiavoni, Raissa [1 ]
机构：
[1] Univ Salento, Dept Engn Innovat, Lecce, Italy
[2] Univ Naples Federico II, Dept Informat Technol & Elect Engn, Naples, Italy
[3] Temple Univ, Dept Elect & Comp Engn, Philadelphia, PA USA
来源：
MEASUREMENT | 2024年 / 225卷
关键词：
Alzheimer's disease; Complexity; Measures; Electroencephalography; Multiscale fuzzy entropy; Neurodegenerative diseases; MILD COGNITIVE IMPAIRMENT; QUANTITATIVE EEG; RESTING-STATE; SIGNAL;
D O I：
10.1016/j.measurement.2023.114040
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Alzheimer's Disease (AD) is a progressive neurodegenerative condition causing memory, attention, and language decline. Current AD diagnostic methods lack objectivity and non-invasiveness. While electroen-cephalography (EEG) holds promise for AD research, conventional EEG analysis methods have proven unsatisfactory. Non-linear dynamical approaches are considered more effective in assessing the brain's complex nature. Starting from these considerations, this study presents an entropy-based algorithm utilizing Multiscale Fuzzy Entropy (MFE) as a promising, effective AD diagnostic method. Computed across 20 different time scales for a public dataset, MFE showed a significant discriminative power. Notably, a trend inversion was observed in the results: AD subjects displayed higher complexity values for slow frequency bands compared to healthy controls, while the opposite was found in fast frequency bands. These findings underscore the potential of MFE in effectively distinguishing AD patients from healthy individuals, marking a significant advance towards more objective and reliable AD diagnosis strategies.
引用
收藏
页数：9
相关论文
共 56 条
[21]   Quantitative EEG in Alzheimer's disease: Cognitive state, resting state and association with disease severity [J].
Garn, Heinrich ;
Waser, Markus ;
Deistler, Manfred ;
Schmidt, Reinhold ;
Dal-Bianco, Peter ;
Ransmayr, Gerhard ;
Zeitlhofer, Josef ;
Schmidt, Helena ;
Seiler, Stephan ;
Sanin, Guenter ;
Caravias, Georg ;
Santer, Peter ;
Grossegger, Dieter ;
Fruehwirt, Wolfgang ;
Benke, Thomas .
INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY, 2014, 93 (03) :390-397
[22]   New interdependence feature of EEG signals as a biomarker of timing deficits evaluated in Attention-Deficit/Hyperactivity Disorder detection [J].
Ghaderyan, Peyvand ;
Moghaddam, Farima ;
Khoshnoud, Shiva ;
Shamsi, Mousa .
MEASUREMENT, 2022, 199
[23]   What is physiologic complexity and how does it change with aging and disease? [J].
Goldberger, AL ;
Peng, CK ;
Lipsitz, LA .
NEUROBIOLOGY OF AGING, 2002, 23 (01) :23-26
[24]   Multiscale Entropy Approaches and Their Applications [J].
Humeau-Heurtier, Anne .
ENTROPY, 2020, 22 (06)
[25]   Entropy and the Brain: An Overview [J].
Keshmiri, Soheil .
ENTROPY, 2020, 22 (09)
[26]   Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset [J].
Kim, Min-jae ;
Youn, Young Chul ;
Paik, Joonki .
NEUROIMAGE, 2023, 272
[27]   Entropic Measures of EEG Complexity in Alzheimer's Disease Through a Multivariate Multiscale Approach [J].
Labate, Domenico ;
La Foresta, Fabio ;
Morabito, Giuseppe ;
Palamara, Isabella ;
Morabito, Francesco Carlo .
IEEE SENSORS JOURNAL, 2013, 13 (09) :3284-3292
[28]   Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations [J].
Lau, Zen J. ;
Tam Pham ;
Chen, S. H. Annabel ;
Makowski, Dominique .
EUROPEAN JOURNAL OF NEUROSCIENCE, 2022, 56 (07) :5047-5069
[29]   Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study [J].
Li, Fali ;
Jiang, Lin ;
Liao, Yuanyuan ;
Si, Yajing ;
Yi, Chanli ;
Zhang, Yangsong ;
Zhu, Xianjun ;
Yang, Zhenglin ;
Yao, Dezhong ;
Cao, Zehong ;
Xu, Peng .
JOURNAL OF NEURAL ENGINEERING, 2021, 18 (04)
[30]   Feature Extraction and Identification of Alzheimer's Disease based on Latent Factor of Multi-Channel EEG [J].
Li, Kai ;
Wang, Jiang ;
Li, Shanshan ;
Yu, Haitao ;
Zhu, Lin ;
Liu, Jing ;
Wu, Lingyun .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2021, 29 :1557-1567
123456