Evolving Computational Dynamical Systems to Recognise Abnormal Human Motor Function
Artificial biochemical networks (ABNs) are a class of computational automata whose architectures are motivated by the organisation of genetic and metabolic networks. In this work, we investigate whether evolved ABNs can carry out classification when stimulated with time series data collected from human subjects with and without Parkinson’s disease. The evolved ABNs have accuracies in the region of 80-90%, significantly higher than the diagnostic accuracies typically found in initial clinical diagnosis. We also show that relatively simple ABNs, comprising only a small number of discrete maps, are able to recognise the abnormal patterns of motor function associated with Parkinson’s disease.
KeywordsMetabolic Network Biochemical Network Cartesian Genetic Programming Initial Clinical Diagnosis Movement Time Series
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- 5.Levine, C.B., Fahrbach, K.R., Siderowf, A.D., Estok, R.P., Ludensky, V.M., Ross, S.D.: Diagnosis and treatment of Parkinson’s disease: a systematic review of the literature. Evid. Rep. Technol. Assess. (Summ.) (57), 1–4 (2003)Google Scholar
- 6.Lones, M.A., Smith, S.L.: Objective assessment of visuo-spatial ability using implicit context representation cartesian genetic programming. In: Smith, S.L., Cagnoni, S. (eds.) Genetic and Evolutionary Computation: Medical Applications, John Wiley & Sons, Chichester (2010)Google Scholar
- 8.Lones, M.A., Tyrrell, A.M., Stepney, S., Caves, L.S.D.: Controlling legged robots with coupled artificial biochemical networks. In: Lenaerts, T., et al. (eds.) Proc. 11th European Conference on the Synthesis and Simulation of Living Systems, Advances in Artificial Life, ECAL 2011, pp. 465–472. MIT Press (August 2011)Google Scholar
- 9.National Institute for Health and Clinical Excellence: Parkinson’s disease: diagnosis and management in primary and secondary care. Royal College of Physicians (2006), http://www.nice.org.uk/CG035