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ARTdECOS, adaptive evolving connectionist model and application to heart rate variability

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Abstract

ARTdECOS is a new artificial neural network model, which incorporates fuzzy adaptive resonance theory (ART) at its core and implements evolving connectionist system (ECOS) methodology to enable knowledge extraction from real time, ongoing processes. The model creates category clusters based on resonance threshold conditions with the fuzzy ART core. After a number of input presentations the model amalgamates categories based upon the fuzzy similarity of weight vectors. Four new parameters are introduced to control and evaluate category amalgamation: weight activation threshold, weight resonance threshold, sum of weight differences and minimum number of categories. A graphical visualization tool is presented that depicts category rules and their evolution. Further, a method is presented to normalize input data in real time without setting feature minimum and maximum levels a priori. ARTdECOS is shown to satisfactorily classify instances from the IRIS dataset into three categories in an unsupervised mode. ARTdECOS is implemented for examples of heart rate interval time series. Heart rate variability features are derived based upon fractal and time domains. Segmentation of the heart rate time series data is shown by category selection.

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Abbreviations

ECG:

Electrocardiogram

ART:

Adaptive resonance theory

ECOS:

Evolving connectionist systems

ARTdECOS:

Adaptive resonance theory with evolving connectionist systems

HRV:

Heart rate variability

ANN:

Artificial neural network

ANS:

Autonomic nervous system

FuNN:

Fuzzy neural network

FCM:

Fuzzy c-means

References

  • Asuncion A, Newman DJ (2007) UCI machine learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. University of California, School of Information and Computer Science, Irvine, CA. Accessed on 1 March 2011

  • Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm, Comput Geosci 10(2–3):191–203

    Google Scholar 

  • Carpenter GA, Tan AH (1995) Rule extraction: from neural architecture to symbolic representation. Connect Sci 7(1):3–27

    Article  Google Scholar 

  • Carpenter GA, Grossberg S, Rosen DB (1991) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4:759–771

    Google Scholar 

  • Dekker JM, Schouten EG, Klootwijk P, Pool J, Swenne CA, Kromhout D (1997) Heart rate variability from short electrocardiographic recordings predicts mortality from all causes in middle-aged and elderly men. Am J Epidemiol 145(10):899–908

    Google Scholar 

  • Esteller R, Vachtsevanos G, Echauz J, Litt B (2001) A comparison of waveform fractal dimension algorithms. IEEE Transact Circuits Syst–I: fundamental theory and applications 48(2):177–183

    Google Scholar 

  • Goldberger Ary L (1996) Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet 347:1312–1314

    Article  Google Scholar 

  • Hassett AL, Radvanski DC, Vaschillo EG, Vaschillo B, Sigal LH, Karavidas MK, Buyske S, Lehrer PM (2007) A pilot study of the efficacy of heart rate variability (HRV) biofeedback in patients with fibromyalgia. Appl Psychophysiol Biofeedback 32:1–10

    Google Scholar 

  • Higuchi T (1988) Approach to an irregular time series on the basis of fractal theory. Physica D 31(2):277–283

    Article  MathSciNet  MATH  Google Scholar 

  • Isawa H, Matsushita H, Nishio Y (2008) Fuzzy adaptive resonance theory combining overlapping category in consideration of connections. 2008 International Joint Conference on Neural Networks (IJCNN 2008), pp 3594–3599

  • Jessica DPM, Gevirtz RN, Scher B, Guarneri E (2004) Biofeedback treatment increases heart rate variability in patients with known coronary artery disease. Am Heart J 147(3):G1–G6

    Google Scholar 

  • Karavidas MK, Lehrer PM, Vaschillo E, Vaschillo B, Marin H, Buyske S, Malinovsky I, Radvanski D, Hassett A (2007) Preliminary results of an open label study of heart rate variability biofeedback for the treatment of major depression. Appl Psychophysiol Biofeedback 32:19–30

    Google Scholar 

  • Kasabov N (1998) The ECOS framework and the ‘eco’ training method for evolving connectionist systems. J Adv Comput Intell 2(6):1–8

    Google Scholar 

  • Kasabov Nikola (2009) Integrative connectionist learning systems inspired by nature: current models, future trends and challenges. Nat Comput 8(2):199–218

    Article  MathSciNet  MATH  Google Scholar 

  • Kasabov N, Kim JS, Watts M, Gray A (1997) FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition. Inf Sci 101(3–4):155–175

    Article  Google Scholar 

  • Kozma R, Swope JA, Kasabov NK, Williams MJA (1997) Multi-agent implementation of fractal analysis of fuzzy neural networks. Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, pp 162–165

  • Luczak H, Laurig W (1973) An analysis of heart rate variability. Ergonomics 16:85–97

    Article  Google Scholar 

  • Nolan RP, Kamath MV, Floras JS, Stanley J, Pang C, Picton P, Young QR (2005) Heart rate variability biofeedback as a behavioral neurocardiac intervention to enhance vagal heart rate control. Am Heart J 149:1137e1–1137e7

    Google Scholar 

  • Peng C-K, Havlin S, Stanley HE, Goldberger AL (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5(1):82–87

    Google Scholar 

  • Reiner R (2008) Integrating a portable biofeedback device into clinical practice for patients with anxiety disorders: results of a pilot study. Appl Psychophysiol Biofeedback 33:55–61

    Google Scholar 

  • Siepmann M, Aykac V, Unterdörfer J, Petrowski K, Mueck-Weymann M (2008) A pilot study on the effects of heart rate variability biofeedback in patients with depression and in healthy subjects. Appl Psychophysiol Biofeedback 33:195–201

    Google Scholar 

  • Swope J (2012) Feature selection and adaptive connectionist classification models and a system for biological time series analysis on the case study of heart rate variability. PhD Thesis, Otago University, Dunedin, New Zealand (to be published)

  • Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17:354–381

    Article  Google Scholar 

  • Zhang L, Wang G, Wang W (2006) A new fuzzy ART neural network based on dual competition and resonance technique. In: Wang J, Yi Z, Zurad JM, Lu BL, Yin H (eds) Advances in neural networks—ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, Proceedings Part I, Springer, Berlin, pp 792–797

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Authors and Affiliations

  1. Adaptive Wearables Pty Ltd, Yokine, WA, Australia

    Jay A. Swope

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  1. Jay A. Swope
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Correspondence to Jay A. Swope.

Appendix ARTdECOS process steps

Appendix ARTdECOS process steps

  1. A.

    Model preparation

    1. 1.

      Allocate memory space for the maximum allowable number of weight vectors, each having length of two times the maximum number of features.

    2. 2.

      Initialize all weight vector components to 1.

    3. 3.

      Set the active number of category nodes to 1.

    4. 4.

      Initialize the model parameters. Suggested values are:

      $$ \rho = 0. 8 2\quad \beta = 0. 7 6\quad \alpha = 0. 1\quad cn_{ \min } = 1\quad A_{W} = 0. 7 5\quad \rho_{W} = 0. 6 5 $$
    5. 5.

      Decide upon the method of choosing scaling limits.

  2. B.

    Input presentation for each instance

    1. 1.

      If adaptive scaling is used:

      1. a.

        Determine if new instance is outside current limits.

      2. b.

        Adjust scaling if necessary, suggested β S  = 0.9.

      3. c.

        Adjust existing weights if necessary.

      4. d.

        Record new scaling limits.

    2. 2.

      Normalize input vector in [0 1].

    3. 3.

      Determine complement of each input vector component.

    4. 4.

      Form new input vector from steps 2 and 3.

    5. 5.

      Determine activation and resonance of input vector with each category.

    6. 6.

      Find category having maximum activation and satisfying resonance condition.

    7. 7.

      If no category satisfies resonance condition, then create a new category.

    8. 8.

      Adapt winning category to current instance.

    9. 9.

      Record category selected for current instance.

  3. C.

    Category amalgamation

  4. 1.

    Determine if sufficient instances have been presented to begin amalgamation.

    1. a.

      This may be a set length of data or a particular dataset.

    2. b.

      Or, the maximum number of categories has been reached.

  5. 2.

    Determine the pair of weights having the highest activation relative to each other.

  6. 3.

    If the pair of weights in step two exceeds weight activation threshold and weight resonance threshold, then merge these two weights. If either threshold is not met, then stop here.

  7. 4.

    Reassign to the newly formed category index the winning category for each instance previously assigned to either merging category.

  8. 5.

    Decide upon further amalgamation.

    1. a.

      If amalgamation has been triggered by the maximum number of categories having been reached (step 1b), then stop here.

    2. b.

      If amalgamation is at a set length of data or a particular dataset (step 1a), then go back to step 2 unless the minimum set categories has been reached.

  9. 6.

    If the input data used in the modelling is available, then present the data to the amalgamated set of categories for reassignment. This is done without any additional learning in the weight vectors.

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Swope, J.A. ARTdECOS, adaptive evolving connectionist model and application to heart rate variability. Evolving Systems 3, 95–109 (2012). https://doi.org/10.1007/s12530-012-9049-2

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