Evolving classifiers to recognize the movement characteristics of Parkinson’s Disease patients

TY - JOUR

T1 - Evolving classifiers to recognize the movement characteristics of Parkinson’s Disease patients

AU - Lones, Michael Adam

AU - Smith, Stephen Leslie

AU - Alty, Jane Elizabeth

AU - Lacy, Stuart

AU - Possin, Katherine

AU - Jamieson, Stuart

AU - Tyrrell, Andy

PY - 2014/8

Y1 - 2014/8

N2 - Parkinson’s disease is a debilitating neurological condition that affects approximately 1 in 500 people and often leads to severe disability. To improve clinical care, better assessment tools are needed that increase the accuracy of differential diagnosis and disease monitoring. In this paper, we report how we have used evolutionary algorithms to induce classifiers capable of recognising the movement characteristics of Parkinson’s disease patients. These diagnostically-relevant patterns of movement are known to occur over multiple time scales. To capture this, we used two different classifier architectures: sliding-window genetic programming classifiers, which model over-represented local patterns that occur within time series data, and artificial biochemical networks, computational dynamical systems that respond to dynamical patterns occurring over longer time scales. Classifiers were trained and validated using movement recordings of 49 patients and 41 age-matched controls collected during a recent clinical study. By combining classifiers with diverse behaviours, we were able to construct classifier ensembles with diagnostic accuracies in the region of 95%, comparable to the accuracies achieved by expert clinicians. Further analysis indicated a number of features of diagnostic relevance, including the differential effect of handedness and the over-representation of certain patterns of acceleration.

AB - Parkinson’s disease is a debilitating neurological condition that affects approximately 1 in 500 people and often leads to severe disability. To improve clinical care, better assessment tools are needed that increase the accuracy of differential diagnosis and disease monitoring. In this paper, we report how we have used evolutionary algorithms to induce classifiers capable of recognising the movement characteristics of Parkinson’s disease patients. These diagnostically-relevant patterns of movement are known to occur over multiple time scales. To capture this, we used two different classifier architectures: sliding-window genetic programming classifiers, which model over-represented local patterns that occur within time series data, and artificial biochemical networks, computational dynamical systems that respond to dynamical patterns occurring over longer time scales. Classifiers were trained and validated using movement recordings of 49 patients and 41 age-matched controls collected during a recent clinical study. By combining classifiers with diverse behaviours, we were able to construct classifier ensembles with diagnostic accuracies in the region of 95%, comparable to the accuracies achieved by expert clinicians. Further analysis indicated a number of features of diagnostic relevance, including the differential effect of handedness and the over-representation of certain patterns of acceleration.

U2 - 10.1109/TEVC.2013.2281532

DO - 10.1109/TEVC.2013.2281532

M3 - Article

SN - 1089-778X

VL - 18

SP - 559

EP - 576

JO - IEEE Transactions on Evolutionary Computation

JF - IEEE Transactions on Evolutionary Computation

IS - 4

ER -