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A novel baseline perspective visibility graph for time series analysis |
| Huang-Jing Ni(倪黄晶)1,2, Zi-Jie Song(宋紫婕)3, Jiao-Long Qin(秦姣龙)4, Ye Wu(吴烨)4, Shi-Le Qi(戚世乐)2,†, and Ming Song(宋明)5,6,‡ |
1 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2 Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
3 School of Chemistry and Life Sciences, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
4 Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;
5 Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
6 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
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Abstract The natural visibility graph method has been widely used in physiological signal analysis, but it fails to accurately handle signals with data points below the baseline. Such signals are common across various physiological measurements, including electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI), and are crucial for insights into physiological phenomena. This study introduces a novel method, the baseline perspective visibility graph (BPVG), which can analyze time series by accurately capturing connectivity across data points both above and below the baseline. We present the BPVG construction process and validate its performance using simulated signals. Results demonstrate that BPVG accurately translates periodic, random, and fractal signals into regular, random, and scale-free networks respectively, exhibiting diverse degree distribution traits. Furthermore, we apply BPVG to classify Alzheimer's disease (AD) patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI (rs-fMRI) data. Utilizing degree distribution entropy derived from BPVG networks, our results exceed the best accuracy benchmark (77.01%) in EEG analysis, especially at channels F4 (78.46%) and O1 (81.54%). Additionally, our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%. These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment. In conclusion, BPVG's validation across both simulated and real data confirms its capability to capture comprehensive information from time series, irrespective of baseline constraints, providing a novel method for studying neural physiological signals.
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Received: 16 February 2025
Revised: 07 April 2025
Accepted manuscript online: 21 April 2025
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PACS:
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05.45.Tp
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(Time series analysis)
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87.19.lf
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(MRI: anatomic, functional, spectral, diffusion)
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87.19.le
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(EEG and MEG)
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05.10.-a
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(Computational methods in statistical physics and nonlinear dynamics)
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| Fund: Project supported by the National Key Research and Development Program of China (Grant No. 2023YFF1204803), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20190736), the Fundamental Research Funds for the Central Universities (Grant No. NJ2024029), and the National Natural Science Foundation of China (Grant Nos. 81701346 and 62201265). |
Corresponding Authors:
Shi-Le Qi, Ming Song
E-mail: shile.qi@nuaa.edu.cn;msong@nlpr.ia.ac.cn
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Cite this article:
Huang-Jing Ni(倪黄晶), Zi-Jie Song(宋紫婕), Jiao-Long Qin(秦姣龙), Ye Wu(吴烨), Shi-Le Qi(戚世乐), and Ming Song(宋明) A novel baseline perspective visibility graph for time series analysis 2025 Chin. Phys. B 34 080504
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[1] Lacasa L, Luque J, Luque B, Ballesteros F and Nu O J C 2008 Proc. Natl. Acad. Sci. USA 105 4972
[2] Li X and Dong Z 2011 Phys. Rev. E 84 062901
[3] Shao Z G 2010 Appl. Phys. Lett. 96 073703
[4] Ahmadlou M, Adeli H and Adeli A 2010 Journal of Neural Transmission 117 1099
[5] Zhu G, Li Y and Wen P Paul 2014 Computer Methods and Programs in Biomedicine 115 64
[6] Sannino S, Stramaglia S, Lacasa L and Marinazzo D 2017 Network Neuroscience 1 208
[7] Zheng M, Domanskyi S, Piermarocchi C and Mias G I 2021 Sci. Rep. 11 5623
[8] Schridde U, Khubchandani M, Motelow J E, Sanganahalli B G, Hyder F and Blumenfeld H 2008 Cerebral Cortex 18 1814
[9] Rathakrishnan R, Moeller F, Levan P, Dubeau F and Gotman J 2010 Epilepsia 51 1837
[10] Gründler T O J, Cavanagh J F, Figueroa C M, Frank M J and Allen J J B 2009 Neuropsychologia 47 1978
[11] Falkenstein M, Hoormann J, Christ S and Hohnsbein J 2000 Biological Psychology 51 87
[12] Hasson U, Iacovacci J, Davis B, Flanagan R, Tagliazucchi E, Laufs H and Lacasa L 2018 Sci. Rep. 8 3557
[13] Zhou T, Jin N, Gao Z and Luo Y 2012 Acta Sin. Phys. 61 030506 (in Chinese)
[14] Andreas M, D. T K, Nikolaos G, G. T M, Theodora A, Panagiotis I and T. T A 2021 Diagnostics 11 1437
[15] Tzimourta K D, Afrantou T, Ioannidis P, Karatzikou M, Tzallas A T, Giannakeas N, Astrakas L G, Angelidis P, Glavas E, Grigoriadis N, Tsalikakis D G and Tsipouras M G 2019 Computers and Electrical Engineering 76 198
[16] Miltiadous A, Tzimourta K D, Afrantou T, Ioannidis P, Grigoriadis N, Tsalikakis D G, Angelidis P, Tsipouras M G, Glavas E, Giannakeas N and Tzallas A T 2023 Data 8 95
[17] Miltiadous A, Tzimourta K D, Afrantou T, Ioannidis P, Grigoriadis N, Tsalikakis D G, Angelidis P, Tsipouras M G, Glavas E, Giannakeas N and Tzallas A T 2023 OpenNeuro
[18] Dzianok P and Kublik E 2024 Scientific Data 11 276
[19] Yan C G, Wang X D, Zuo X N and Zang Y F 2016 Neuroinformatics 14 339
[20] Yan C and Zang Y 2010 Frontiers in Systems Neuroscience 4 13
[21] Ashburner J 2007 NeuroImage 38 95
[22] Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, Yang Z, Chu C, Xie S, Laird A R, Fox P T, Eickhoff S B, Yu C and Jiang T 2016 Cerebral Cortex 26 3508
[23] Lin W, Gao Q, Du M, Chen W and Tong T 2021 Computers in Biology and Medicine 134 104478
[24] Dai Z, Yan C, Wang Z, Wang J, Xia M, Li K and He Y 2012 NeuroImage 59 2187
[25] Jones C and Wiesner K 2022 Entropy 24 1182
[26] Abásolo D, Hornero R, Gómez C, García M and López M 2006 Medical Engineering & Physics 28 315
[27] Jeong J, Gore J C and Peterson B S 2001 Clinical Neurophysiology 112 827
[28] Choi J, Ku B, You Y G, Jo M, Kwon M, Choi Y, Jung S, Ryu S, Park E, Go H, Kim G, Cha W and Kim J U 2019 Sci. Rep. 9 10468
[29] Ando M, Nobukawa S, Kikuchi M and Takahashi T 2021 Frontiers in Neuroscience 15 667614
[30] Del Percio C, Lopez S, Noce G, Lizio R, Tucci F, Soricelli A, Ferri R, Nobili F, Arnaldi D, Famà F, Buttinelli C, Giubilei F, Marizzoni M, Güntekin B, Yener G, Stocchi F, Vacca L, Frisoni G B and Babiloni C 2023 Clinical EEG and Neuroscience 54 21
[31] Kim H T, Kim B Y, Park E, Kim J W, Hwang E W, Han S and Cho S 2005 Future generation computer systems 21 1124
[32] Wong S, Bertoux M, Savage G, Hodges J R, Piguet O and Hornberger M 2016 Journal of Alzheimer’s Disease 51 889
[33] Wu H, Song Y, Chen S, Ge H, Yan Z, Qi W, Yuan Q, Liang X, Lin X and Chen J 2022 Frontiers in Neuroscience 16 876568
[34] Morgen K, Ramirez A, Frölich L, Tost H, Plichta M M, Kölsch H, Rakebrandt F, Rienhoff O, Jessen F, Peters O, Jahn H, Luckhaus C, Hüll M, Gertz H J, Schröder J, Hampel H, Teipel S J, Pantel J, Heuser I, Wiltfang J, Rüther E, Kornhuber J, Maier W and Meyer-Lindenberg A 2014 Alzheimer’s & Dementia 10 S269
[35] Liu Y B, Wang X J, Tan L, Tan C C and Xu W 2023 Journal of Alzheimer’s Disease 96 1651
[36] Clarke H, Messaritaki E, Dimitriadis S I and Metzler-Baddeley C 2022 Brain Connectivity 12 26
[37] Cavanna A E and Trimble M R 2006 Brain 129 564
[38] Yu M, Sporns O and Saykin A J 2021 Nat. Rev. Neurol. 17 545
[39] Chiesa P A, Cavedo E, Vergallo A, Lista S, Potier M C, Habert M O, Dubois B, Thiebaut de Schotten M and Hampel H 2019 Alzheimer’s & Dementia 15 940
[40] Chan D, Fox N C, Scahill R I, CrumWR, Whitwell J L, Leschziner G, Rossor A M, Stevens J M, Cipolotti L and Rossor M N 2001 Annals of Neurology 49 433
[41] Fung C W, Guo J, Fu H, Figueroa H Y, Konofagou E E and Duff K E 2020 Sci. Adv. 6 eabc8098
[42] Uhlhaas P J, Pantel J, Lanfermann H, Prvulovic D, Haenschel C, Maurer K and Linden D E J 2008 Dementia and Geriatric Cognitive Disorders 25 465
[43] Jeong S H, Cha J, Jung J H, Yun M, Sohn Y H, Chung S J and Lee P H 2023 JAD 94 1133
[44] Toniolo S, Serra L, Olivito G, Marra C, Bozzali M and Cercignani M 2018 Frontiers in Cellular Neuroscience 12 430
[45] Tang F, Zhu D, MaW, Yao Q, Li Q and Shi J 2021 Frontiers in Neurology 12 645171
[46] Bose R, Samanta K, Modak S and Chatterjee S 2021 IEEE Journal of Biomedical and Health Informatics 25 685
[47] Gao Z K, Guo W, Cai Q, Ma C, Zhang Y B and Kurths J 2019 Chaos 29 073119
[48] Song Z, Deng B, Wang J and Wang R 2019 IEEE Transactions on Biomedical Engineering 66 41
[49] Wen T, Chen H and Cheong K H 2022 Nonlinear Dyn. 110 2979
[50] Xuan Q, Zhou J, Qiu K, Chen Z, Xu D, Zheng S and Yang X 2022 IEEE Transactions on Network Science and Engineering 9 1516
[51] Tang J F, Xia L, Li G L, Fu J, Duan S and Wang L 2024 Chin. Phys. B 33 037302 |
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