Effects of sampling rate on multiscale entropy of electroencephalogram time series

• Autorzy: Zheng Jinlin , Li Yan , Zhai Yawen , Zhang Nan , Yu Haoyang , Tang Chi , Yan Zheng , Luo Erping , Xie Kangning

Identyfikatory

DOI

10.1016/j.bbe.2022.12.007

• Języki publikacji: EN

Abstrakty

EN

A physiological system encompasses numerous components that function at various time scales. To characterize the scale-dependent feature, the multiscale entropy (MSE) analysis has been proposed to describe the complex processes on multiple time scales. However, MSE analysis uses the relative scale factors to reveal the time-related dynamics, which may cause in-comparability of results from diverse studies with inconsistent sampling rates. In this study, in addition to the conventional MSE with relative scale factors, we also expressed MSE with absolute time scales (MaSE). We compared the effects of sampling rates on MSE and MaSE of simulated and real EEG time series. The results show that the previously found phenomenon (down-sampling can increase sample entropy) is just the projection of the compressing effect of down-sampling on MSE. And we have also shown the compressing effect of down-sampling on MSE does not change MaSE’s profile, despite some minor right-sliding. In addition, by analyzing a public EEG dataset of emotional states, we have demonstrated improved classification rate after choosing appropriate sampling rate. We have finally proposed a working strategy to choose an appropriate sampling rate, and suggested using MaSE to avoid confusion caused by sampling rate inconsistency. This novel study may apply to a broad range of studies that would traditionally utilize sample entropy and MSE to analyze the complexity of an underlying dynamic process.

Słowa kluczowe

EN

sampling rate; sample entropy; multiscale entropy; electroencephalogram; EEG; power noise

PL

częstotliwość próbkowania; entropia próbkowa; entropia wieloskalowa; elektroencefalogram; EEG

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 1
• Strony: 233--245
• Opis fizyczny: Bibliogr. 46 poz., rys., tab., wykr.

Twórcy

autor

Zheng Jinlin

• School of Biomedical Engineering, Air Force Medical University, Xi’an, China
• Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
• College of Information Science and Engineering, Huaqiao University, Xiamen, China

autor

Li Yan

• School of Biomedical Engineering, Air Force Medical University, Xi’an, China

autor

Zhai Yawen

• School of Biomedical Engineering, Air Force Medical University, Xi’an, China
• School of Electronics and Information, Xi’an Polytechnic University, Xi’an, Shaanxi, China

autor

Zhang Nan

• School of Biomedical Engineering, Air Force Medical University, Xi’an, China
• School of Electronics and Information, Xi’an Polytechnic University, Xi’an, Shaanxi, China

autor

Yu Haoyang

• School of Biomedical Engineering, Air Force Medical University, Xi’an, China

autor

Tang Chi

• Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China

autor

Yan Zheng

• College of Information Science and Engineering, Huaqiao University, Xiamen, China

autor

Luo Erping

• School of Biomedical Engineering, Air Force Medical University, Xi’an, China

autor

Xie Kangning

• School of Biomedical Engineering, Air Force Medical University, Xi’an, China
• Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China

Bibliografia

• [1] Usman Syed Muhammad, Khalid Shehzad, Bashir Zafar. Epileptic seizure prediction using scalp electroencephalogram signals. Biocybernet Biomed Eng 2021;41(1):211-20.
• [2] Richman Joshua, Moorman Joseph. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol. Heart Circul Physiol 2000;278(H2039-49):07.
• [3] Costa Madalena, Goldberger Ary, Peng Chung-Kang. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002;89(068102):09.
• [4] Shuen-De Wu, Chiu-Wen Wu, Lee Kung-Yen, Lin Shiou-Gwo. Modified multiscale entropy for short-term time series analysis. Physica A 2013;392(23):5865-73.
• [5] Shi Wenbin, Shang Pengjian, Ma Yan, Sun Shuchen, Yeh Chien-Hung. A comparison study on stages of sleep: Quantifying multiscale complexity using higher moments on coarse-graining. Commun Nonlinear Sci Mar 2017;44:292-303.
• [6] Han Wei, Zhang Zunjing, Tang Chi, Yan Yili, Luo Erping, Xie Kangning. Power-law exponent modulated multiscale entropy: A complexity measure applied to physiologic time series. IEEE Access 2020;8:112725-34.
• [7] Jamil Zainab, Jamil Afshan, Majid Muhammad. Artifact removal from eeg signals recorded in non-restricted environment. Biocybernet Biomed Eng 2021;41(2):503-15.
• [8] Wang Xue, Liu Xiaofeng, Pang Wei, Jiang Aimin. Multiscale increment entropy: An approach for quantifying the physiological complexity of biomedical time series. Inf Sci 2022;586:279-93.
• [9] Keshmiri Soheil. Entropy and the brain: An overview. Entropy 2020;22(9).
• [10] Ma Yan, Shi Wenbin, Peng Chung-Kang, Yang Albert C. Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. Sleep Med Rev Feb 2018;37:85-93.
• [11] Pappalettera Chiara, Miraglia Francesca, Cotelli Maria, Rossini Paolo, Vecchio Fabrizio. Analysis of complexity in the eeg activity of parkinson’s disease patients by means of approximate entropy. GeroScience 2022.
• [12] Angulo-Ruiz Brenda Y, Munoz Vanesa, Rodríguez-Martínez Elena I, Cabello-Navarro Celia, Gómez Carlos M. Multiscale entropy of adhd children during resting state condition. Cogn Neurodyn 2022:1-23.
• [13] Gu Chao, Liu Zhong-Xu, Woltering Steven. Electroencephalography complexity in resting and task states in adults with attention-deficit/hyperactivity disorder. Brain Commun 2022;4(2):fcac054.
• [14] Zhai Yawen, Li Yan, Zhou Shengyi, Zhang Chenxu, Luo Erping, Tang Chi, Xie Kangning. Mental fatigue decreases complexity: Evidence from multiscale entropy analysis of instantaneous frequency variation in alpha rhythm. Front Human Neurosci 2022;16.
• [15] Chen Chunxiao, Li Jian, Xiong Lu. Multiscale entropy-based analysis and processing of eeg signal during watching 3dtv. Measurement 2018;125:432-7.
• [16] Zuo Xin, Zhang Chi, Hmlinen Timo, Hanbing Gao YuFu, Cong Fengyu. Cross-subject emotion recognition using fused entropy features of eeg. Entropy 2022;24(9).
• [17] Tian Pan, Jie Hu, Qi Jin, Ye Xian, Che Datian, Ding Ying, Peng Yinghong. A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture. Biocybernet Biomed Eng 2017;37(2):263-71.
• [18] Bai Dengxuan, Wenpo Yao, Wang Shuwang, Wang Jun. Multiscale weighted permutation entropy analysis of schizophrenia magnetoencephalograms. Entropy 2022;24:314.
• [19] Krishnan Palani Thanaraj, Raj Alex Noel Joseph, Balasubramanian Parvathavarthini, Chen Yuanzhu. Schizophrenia detection using multivariateempirical mode decomposition and entropy measures from multichannel eeg signal. Biocybernet Biomed Eng 2020;40 (3):1124-39.
• [20] Li Peng, Karmakar Chandan, Yearwood John, Venkatesh Svetha, Palaniswami Marimuthu, Liu Changchun. Detection of epileptic seizure based on entropy analysis of short-term eeg. Plos One 2018;13(e0193691):03.
• [21] Ando Momo, Nobukawa Sou, Kikuchi Mitsuru, Takahashi Tetsuya. Identification of electroencephalogram signals in alzheimer’s disease by multifractal and multiscale entropy analysis. Front Neurosci 2021;15:667614.
• [22] Espinosa Ricardo, Talero Jesica, Weinstein Alejandro. Effects of tau and sampling frequency on the regularity analysis of ecg and eeg signals using apen and sampen entropy estimators. Entropy 2020;22(11):1298.
• [23] Li Yan, Liu Juan, Tang Chi, Han Wei, Zhou Shengyi, Yang Siqi, et al. Multiscale entropy analysis of instantaneous frequency variation to overcome the cross-over artifact in rhythmic eeg. IEEE Access 2021;9:12896-905.
• [24] Fallahtafti Farahnaz, Wurdeman Shane, Yentes Jennifer. Sampling rate influences the regularity analysis of temporal domain measures of walking more than spatial domain measures. Gait Posture 2021;88:06.
• [25] Raffalt Peter, Mccamley John, Denton William, Yentes Jennifer. Sampling frequency influences sample entropy of kinematics during walking. Med Biol Eng Comput 2018;57:11.
• [26] Mesin Luca. Estimation of complexity of sampled biomedical continuous time signals using approximate entropy. Front Physiol 2018;9:710.
• [27] Valencia Jose Fernando, Porta Alberto, Vallverdu Montserrat, Claria Francesc, Baranowski Rafal, Orlowska-Baranowska Ewa, et al. Refined multiscale entropy: Application to 24-h holter recordings of heart period variability in healthy and aortic stenosis subjects. IE EE Trans Biomed Eng 2009;56 (9):2202-13.
• [28] Costa Madalena, Goldberger Ary L, Peng C-K. Multiscale entropy analysis of biological signals. Phys Rev E 2005;71:021906.
• [29] Courtiol Julie, Perdikis Dionysios, Petkoski Spase, Müller Viktor, Huys Raoul, Sleimen-Malkoun Rita, Jirsa Viktor K. The multiscale entropy: Guidelines for use and interpretation in brain signal analysis. J Neurosci Methods 2016;273:175-90.
• [30] Matran-Fernandez Ana, Poli Riccardo. Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces. PLoS ONE 2017;12(1- 28):05.
• [31] Koelstra Sander, Muhl Christian, Soleymani Mohammad, Lee Jong-Seok, Yazdani Ashkan, Ebrahimi Touradj, Pun Thierry, Nijholt Anton, Patras Ioannis. Deap: A database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 2011;3(1):18-31.
• [32] Gierzkiewicz Anna, Zgliczyński Piotr. From the sharkovskii theorem to periodic orbits for the rössler system. J Diff Eqs 2022;314:733-51.
• [33] Borowska Marta. Multiscale permutation lempel-ziv complexity measure for biomedical signal analysis: Interpretation and application to focal eeg signals. Entropy 2021;23(7):832.
• [34] Shen Kelly, McFadden Alison, McIntosh Anthony R. Signal complexity indicators of health status in clinical eeg. Sci Rep 2021;11(1):1-9.
• [35] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria; 2021.
• [36] Van Erven Tim, Harremos Peter. Rényi divergence and kullback-leibler divergence. IEEE Trans Inf Theory 2014;60 (7):3797-820.
• [37] Sengupta Biswa, Stemmler Martin, Friston Karl. Information and efficiency in the nervous system-a synthesis. PLoS Comput Biol 2013;9(e1003157):07.
• [38] Demertzi Athena, Tagliazucchi Enzo, Dehaene Stanislas, Deco Gustavo, Barttfeld Pablo, Raimondo Federico, et al. Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci Adv 2019;5(2): eaat7603.
• [39] Chen Xinnian, Solomon Irene C, Chon Ki H. Comparison of the use of approximate entropy and sample entropy: applications to neural respiratory signal. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE; 2006. p. 4212-5.
• [40] Popov Anton, Avilov Oleksii, Kanaykin Oleksii. Permutation entropy of eeg signals for different sampling rate and time lag combinations. In: 2013 Signal Processing Symposium (SPS). IEEE; 2013. p. 1-4.
• [41] Machura Lukasz, Wawrzkiewicz-Jałowiecka Agata, Bednarczyk Piotr, Trybek Paulina. Linking the sampling frequency with multiscale entropy to classify mitobk patchclamp data. Biomed Signal Process Control 2022;76:103680.
• [42] Misic Bratislav, Vakorin Vasily, Paus Tomas, McIntosh Anthony. Functional embedding predicts the variability of neural activity. Front Syst Neurosci 2011;5(90):11.
• [43] McIntosh A, Vakorin Vasily, Kovacevic Natasha, Wang H. Andreea Diaconescu, and Andrea Protzner. Spatiotemporal dependency of age-related changes in brain signal variability. Cerebral Cortex (New York, N.Y. 1991;24(02):2013.
• [44] Vakorin Vasily A, Lippé Sarah, McIntosh Anthony R. Variability of brain signals processed locally transforms into higher connectivity with brain development. J Neurosci 2011;31(17):6405-13.
• [45] Angsuwatanakul Thanate, O’Reilly Jamie, Ounjai Kajornvut, Kaewkamnerdpong Boonserm, Iramina Keiji. Multiscale entropy as a new feature for eeg and fnirs analysis. Entropy 2020;22(2).
• [46] He Huili. Multiscale fuzzy entropy based on local mean decomposition and fisher rule for eeg feature extraction in human motion analysis. EAI Endorsed Trans Scalable Informat Syst 2021;9(35):11.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)