Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?
So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six common...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/3/396 |
| _version_ | 1797611988549894144 |
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| author | Jing Liu Huibin Lu Xiuru Zhang Xiaoli Li Lei Wang Shimin Yin Dong Cui |
| author_facet | Jing Liu Huibin Lu Xiuru Zhang Xiaoli Li Lei Wang Shimin Yin Dong Cui |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale. |
| first_indexed | 2024-03-11T06:36:03Z |
| format | Article |
| id | doaj.art-90cf71d7a7b64834bd444e21ca6e4381 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T06:36:03Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-90cf71d7a7b64834bd444e21ca6e43812023-11-17T10:55:38ZengMDPI AGEntropy1099-43002023-02-0125339610.3390/e25030396Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?Jing Liu0Huibin Lu1Xiuru Zhang2Xiaoli Li3Lei Wang4Shimin Yin5Dong Cui6Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaHebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaHebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaNational Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, ChinaNeurology Department, Chinese People’s Liberation Army Rocket Force Characteristic Medical Center, Beijing 100088, ChinaNeurology Department, Chinese People’s Liberation Army Rocket Force Characteristic Medical Center, Beijing 100088, ChinaHebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaSo far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.https://www.mdpi.com/1099-4300/25/3/396complexitycorrelationmulti-channel couplingmultivariate multi-scale entropymild cognitive impairmentelectroencephalograph |
| spellingShingle | Jing Liu Huibin Lu Xiuru Zhang Xiaoli Li Lei Wang Shimin Yin Dong Cui Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? Entropy complexity correlation multi-channel coupling multivariate multi-scale entropy mild cognitive impairment electroencephalograph |
| title | Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? |
| title_full | Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? |
| title_fullStr | Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? |
| title_full_unstemmed | Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? |
| title_short | Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? |
| title_sort | which multivariate multi scale entropy algorithm is more suitable for analyzing the eeg characteristics of mild cognitive impairment |
| topic | complexity correlation multi-channel coupling multivariate multi-scale entropy mild cognitive impairment electroencephalograph |
| url | https://www.mdpi.com/1099-4300/25/3/396 |
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