Abstract
We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.
Highlights
Degenerative disorders of the central nervous system (CNS) such as Huntington’s disease (HD), Parkinson’s Disease (PD), Alzheimer’s Disease (AD), and mild cognitive impairment (MCI) affect the human motor system and exhibit a set of similar deficits, such as cognitive impairment and motor dysfunctions [1]
We propose a digitized data collection methodology via a smart electronic consumer device interface adapted for patients with CNS disorders, resulting in feature extraction for model inputs, and propose hybrid healthy vs. impaired person classification models for the tasks aimed at the evaluation of CNS
We investigated the following classification methods: SVM, artificial neural networks (ANNs) with a multilayer perceptron (MLP), K-nearest neighbors (KNNs), sequential minimal optimization (SMO) [51], linear discriminant analysis (LDA) [52], Fisher’s linear discriminant analysis (FLDA) [53], deep learning networks (DNNs), random forests [54], Bayes nets [55], naive
Summary
Degenerative disorders of the central nervous system (CNS) such as Huntington’s disease (HD), Parkinson’s Disease (PD), Alzheimer’s Disease (AD), and mild cognitive impairment (MCI) affect the human motor system and exhibit a set of similar deficits, such as cognitive impairment and motor dysfunctions [1]. Examples of such dysfunctions are a reduced speech rate [2], higher daily caloric intake [3], increased rigidity, reduced dexterity, and essential tremors [4]. Any auxiliary measure (e.g., a digital tool for health state self-assessment) created to improve the daily life of patients and medical doctors is beneficial
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