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An automatic segmentation framework of quasi-periodic time series through graph structure

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Abstract

The segmentation of quasi-periodic time series (QTS) is crucial for modeling analysis in industrial and medical fields. However, it is challenging to automatically and effectively split different types of QTSs under the same settings. To address this issue, we propose an enhanced graph-based QTS automatic segmentation (EGQAS) framework that integrates an enhanced graph structure and hybrid clustering. The enhanced graph structure improves the stability of QTS segmentation, especially for a large number of QTS, by using edge weight filtering and aggregation. Hybrid clustering, which consists of hierarchical clustering and a modified k-means algorithm, removes clusters with outliers and incomplete divisions to improve the integrity of the final QTS split points. For four different types of public datasets, EGQAS outperforms the current state-of-the-art baselines, demonstrating its better adaptability. In tests with the MIT-BIH arrhythmia database (MITDB), EGQAS proves itself to be effective and stable with a large number of data.

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Availability of data and materials

All datasets used in these research are public datasets. they are available in the PhysioBank repository (https://archive.physionet.org/cgi-bin/atm/ATM).

Code Availability

The implementation of the code is available at: (https://github.com/a651063771/EGQAS_code)

References

  1. Qi W, Su H (2022) A cybertwin based multimodal network for ecg patterns monitoring using deep learning. IEEE Trans Ind Inf 18(10):6663–6670

    Article  Google Scholar 

  2. Zhao A, Li J, Dong J et al (2021) Multimodal gait recognition for neurodegenerative diseases. IEEE Trans Cybern 52(9):9439–9453

    Article  Google Scholar 

  3. Qiu S, Zhao H, Jiang N et al (2022) Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges. Inf Fusion 80(1):241–265

    Article  Google Scholar 

  4. Prifti E, Fall A, Davogustto G et al (2021) Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long qt syndrome. Eur Heart J 42(38):3948–3961

  5. Sun L, Wang Y, Qu Z et al (2021) Beatclass: a sustainable ecg classification system in iot-based ehealth. IEEE Internet of Things Journal 9(10):7178–7195

    Article  Google Scholar 

  6. Kung BH, Hu PY, Huang CC et al (2020) An efficient ecg classification system using resource-saving architecture and random forest. IEEE J Biomed Health Inf 25(6):1904–1914

    Article  Google Scholar 

  7. Li J, Tobore I, Liu Y et al (2021) Non-invasive monitoring of three glucose ranges based on ecg by using dbscan-cnn. IEEE J Biomed Health Inf 25(9):3340–3350

    Article  Google Scholar 

  8. Liu F, Zhou X, Cao J et al (2022) Anomaly detection in quasi-periodic time series based on automatic data segmentation and attentional lstm-cnn. IEEE Trans Knowl Data Eng 34(6):2626–2640

    Article  Google Scholar 

  9. Du N, Cao Q, Yu L et al (2021) Fm-ecg: A fine-grained multi-label framework for ecg image classification. Inf Sci 549(1):164–177

    Article  MathSciNet  Google Scholar 

  10. Pokaprakarn T, Kitzmiller RR, Moorman R et al (2021) Sequence to sequence ecg cardiac rhythm classification using convolutional recurrent neural networks. IEEE J Biomed Health Inf 26(2):572–580

    Article  Google Scholar 

  11. Sadoughi A, Shamsollahi MB, Fatemizadeh E et al (2021) Detection of apnea bradycardia from ecg signals of preterm infants using layered hidden markov model. Ann Biomed Eng 49(1):2159–2169

    Article  Google Scholar 

  12. Huang G, Zhang Y, Cao J et al (2014) Online mining abnormal period patterns from multiple medical sensor data streams. World Wide Web 17(4):569–587

    Article  Google Scholar 

  13. Ma J, Sun L, Wang H et al (2016) Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans Internet Technol 16(1):1–20

    Article  Google Scholar 

  14. Kim YK, Lee M, Song HS et al (2022) Automatic cardiac arrhythmia classification using residual network combined with long short-term memory. IEEE Trans Instrum Meas 71(1):1–17

    Google Scholar 

  15. Panigrahi S, Pattanayak RM, Sethy PK et al (2021) Forecasting of sunspot time series using a hybridization of arima, ets and svm methods. Sol Phys 296(1):1–19

    Article  Google Scholar 

  16. Li R, Zheng S, Duan C et al (2021) Multistage attention resu-net for semantic segmentation of fine-resolution remote sensing images. IEEE Eng Manag Rev 19(1):1–5

    Google Scholar 

  17. Wang H, He S, Liu T et al (2022) Qrs detection of ecg signal using u-net and dbscan. Multimedia Tools Appl 81(10):13,319-13,333

    Article  Google Scholar 

  18. Lytvynenko I, Lupenko S, Onyskiv P et al (2021) Modeling and methods of statistical processing of a vector rhytmocardiosignal. Open Bioinform J 14(1):73–86

    Article  Google Scholar 

  19. Cui Y, Levine M, Zhou Z (2021) Estimation and inference of time-varying auto-covariance under complex trend: A difference-based approach. Electronic J Stat 15(2):4264–4294

    Article  MathSciNet  MATH  Google Scholar 

  20. Livieris IE, Stavroyiannis S, Pintelas E et al (2020) A novel validation framework to enhance deep learning models in time-series forecasting. Neural Comput & Applic 32(1):17,149-17,167

    Article  Google Scholar 

  21. Auno S, Lauronen L, Wilenius J et al (2021) Detrended fluctuation analysis in the presurgical evaluation of parietal lobe epilepsy patients. Clin Neurophysiol 132(7):1515–1525

    Article  Google Scholar 

  22. Dutta S, Dey J, Mishra D et al (2021) Prediction of insulation sensitive parameters of power transformer using detrended fluctuation analysis based method. IEEE Trans Power Deliv 37(3):1963–1973

  23. Kibet K, Cheboi J, Agak T (2022) Credit to private sector, investor protection, foreign exchange rate, corruption perception and economic growth nexus among comesa countries. Econ Res 6(3):108–122

    Google Scholar 

  24. Hsieh YL, Hsieh YD, Wang W (2022) Time series and mel-frequency cepstrum coefficient analyses of venous pulsatile tinnitus vascular sound and flow velocity sensed by transcranial/retroauricular doppler ultrasound approaches. Sensors and Materials 34(7):2791–2807

    Article  Google Scholar 

  25. Khelil K, Berrezzek F, Bouadjila T (2021) Ga-based design of optimal discrete wavelet filters for efficient wind speed forecasting. Neural Comput & Applic 33(9):4373–4386

    Article  Google Scholar 

  26. Wang W, Zhang G, Yang L et al (2019) Revisiting signal processing with spectrogram analysis on eeg, ecg and speech signals. Futur Gener Comput Syst 98(1):227–232

    Article  Google Scholar 

  27. Manju B, Sneha M (2020) Ecg denoising using wiener filter and kalman filter. Procedia Comput Sci 171(1):273–281

  28. Kumar A, Tomar H, Mehla VK et al (2021) Stationary wavelet transform based ecg signal denoising method. ISA Trans 114(1):251–262

    Article  Google Scholar 

  29. Guedri H, Bajahzar A, Belmabrouk H (2021) Ecg compression with douglas-peucker algorithm and fractal interpolation. Math Biosci Eng 18(4):3502–3521

    Article  MathSciNet  MATH  Google Scholar 

  30. Jain R, Semwal VB, Kaushik P (2022) Stride segmentation of inertial sensor data using statistical methods for different walking activities. Robotica 40(8):2567–2580

    Article  Google Scholar 

  31. von Sachs R (2020) Nonparametric spectral analysis of multivariate time series. Annu Rev Stati Appl 7(1):361–386

    Article  MathSciNet  Google Scholar 

  32. Abduh Z, Nehary EA, Wahed MA et al (2019) Classification of heart sounds using fractional fourier transform based mel-frequency spectral coefficients and stacked autoencoder deep neural network. J Med Imaging Health Inf 9(1):1–8

    Article  Google Scholar 

  33. Malali A, Hiriyannaiah S, Siddesh G et al (2020) Supervised ecg wave segmentation using convolutional lstm. ICT Express 6(3):166–169

    Article  Google Scholar 

  34. Li H, Lv Z, Li J et al (2023) Traffic flow forecasting in the covid-19: A deep spatial-temporal model based on discrete wavelet transformation. ACM Trans Knowl Disc Data 17(5):1–28

    MathSciNet  Google Scholar 

  35. Zhang C, Cui L, Zhang Q et al (2022) Online anomaly detection for aeroengine gas path based on piecewise linear representation and support vector data description. IEEE Sensors J 22(23):22,808-22,816

    Article  Google Scholar 

  36. Ma DL, Zhang YL (2019) Time series piecewise linear representation based on trend feature points. In: Green Intelligent Transportation Systems: Proceedings of the 8th International Conference on Green Intelligent Transportation Systems and Safety, Springer, pp 19–28

  37. Kong X, Bi Y, Glass DH (2020) Detecting anomalies in sequential data augmented with new features. Artif Intell Rev 53(1):625–652

  38. Zhang C, Wang X, Ren Y, et al (2020) Fault diagnosis based on improved piecewise linear representation for three-capacity water tank. In: 2020 Chinese Automation Congress (CAC), IEEE, pp 4054–4059

  39. Mohammadi M, Fazlali M, Hosseinzadeh M (2021) Accelerating louvain community detection algorithm on graphic processing unit. J Supercomput 77(6):6056–6077

    Article  Google Scholar 

  40. Seifikar M, Farzi S, Barati M (2020) C-blondel: an efficient louvain-based dynamic community detection algorithm. IEEE Trans Comput Soc Syst 7(2):308–318

    Article  Google Scholar 

  41. Shirazi S, Baziyad H, Ahmadi N et al (2019) A new application of louvain algorithm for identifying disease fields using big data techniques. J Biostat Epidemiol 5(3):183–193

  42. Yildirim Ö (2018) A novel wavelet sequence based on deep bidirectional lstm network model for ECG signal classification. Comput Biol Med 96(1):189–202

    Article  Google Scholar 

  43. Biswas D, Simões-Capela N, Van Hoof C et al (2019) Heart rate estimation from wrist-worn photoplethysmography: A review. IEEE Sensors J 19(16):6560–6570

    Article  Google Scholar 

  44. Fan GF, Zhang LZ, Yu M et al (2022) Applications of random forest in multivariable response surface for short-term load forecasting. Int J Electr Power Energy Syst 139(1):108,073

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Acknowledgements

This work was supported in part by the National Key R &D Program of China [No.J2019-V-0001-0092], in part by Major Science and Technology Project of Sichuan Province [No. 2022YFG0174]

Funding

This work was supported in part by the National Key R &D Program of China [No.J2019-V-0001-0092], in part by Major Science and Technology Project of Sichuan Province [No. 2022YFG0174]

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Authors and Affiliations

  1. School of Computer Science, Southwest Petroleum University, Chengdu, 610500, China

    Xiaolan Tang, Desheng Zheng, Zhengyu Li & Yong Zhou

  2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    Gebre S. Kebede & Xiaoyu Li

  3. Institute of Electronics and Information Industry Technology of Kash, Silk Road Talent Building, Shenka Avenue, 844000, Kash, Xinjiang, People’s Republic of China

    Desheng Zheng & Xiaoyu Li

  4. AECC Sichuan Gas Turbine Establishment, MianYang, 621700, People’s Republic of China

    Chao Lu

  5. Sichuan Cancer Hospital & Institute Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610000, People’s Republic of China

    Lintao Li

  6. Department of Chemistry, Physics and Atmospheric Science, Jackson State University, Jackson, MS, 39217, USA

    Shan Yang

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  1. Xiaolan Tang
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Appendix A Complete list of results

Appendix A Complete list of results

In this appendix, we provide Table 5 with the complete contents of Table 3 and Table 6 for the number of anomalies and quasi-periodic types for different quality data sets in MITDB.

Table 5 The overall performance of different QTS segmentation algorithms on MITDB
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Table 6 The number of anomalies and quasi-periodic types for different quality datasets in MITDB
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Tang, X., Zheng, D., Kebede, G.S. et al. An automatic segmentation framework of quasi-periodic time series through graph structure. Appl Intell 53, 23482–23499 (2023). https://doi.org/10.1007/s10489-023-04814-y

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