Potential Role of Wearable, Ambulatory and Home Monitoring Systems for Patients with Neurodegenerative Diseases and Their Caregivers

Meriggi, P.; Castiglioni, Paolo; Rizzo, Francesco; Gower, Valerio; Andrich, Renzo; Rabuffetti, Marco; Ferrarin, Maurizio; Rienzo, Marco Di

doi:10.4108/icst.pervasivehealth.2011.246109

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Neurodegenerative disease (NDD) is an umbrella term used to describe neurological disorders due to the failure or malfunctioning of neurons in motor, sensory and cognitive system [1]. Neurodegenerative disorders include Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Huntington's disease (HD) and Parkinson's disease (PD).…”

Section: Introductionmentioning

confidence: 99%

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Shah

Habib

Aziz

et al. 2020

JPRI

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Background: The medical researchers are developing different non-invasive methods for early detection of Neurodegenerative Diseases (NDDs) when pharmacological interventions are still possible to further prevent the disease progression. The NDDs are associated with the degradation in the complex gait dynamics and motor activity. The classification of gait data using machine learning techniques can assist the physicians for early diagnosis of the neural disorder when clinical manifestation of the diseases is not yet apparent. Aims: The present study was undertaken to classify the control and NDD subjects using decision trees based classifiers (Random Forest (RF), J48 and REPTree). Methodology: The data used in the study comprises of 16 control, 20 Huntington’s Disease (HD), 15 Parkinson’s Disease (PD), and 13 Amyotrophic Lateral Sclerosis (ALS) subjects, which were taken from publicly available database from Physionet. The age range of control subjects was 20-74, HD subjects was 36-70, PD subjects was 44-80, and ALS subjects was 29-71. There were 13 attributes associated with the data. Important features/attributes of the data were selected using correlation feature selection - subset evaluation (cfs) method. Three tree based machine learning algorithms (RF, J48 and REPTree) were used to classify the control and NDD subjects. The performance of classifiers were evaluated using Precision, Recall, F-Measure, MAE and RMSE. Results: In order to evaluate the performance of tree based classifiers, two different settings of data i.e. complete features and selected features were used. In classifying control vs HD subjects, RF provides the robust separation with classification accuracy of 84.79% using complete features and 83.94% using selected features. While in classifying control vs PD subjects, and control vs ALS subjects, RF also provides the best separation with classification accuracy of 86.51% and 94.95% respectively using complete features and 85.19% and 93.64% respectively using selected features. Conclusion: The variability analysis of physiological signals provides a valuable non-invasive tool for quantifying the system of dynamics of healthy subjects and to examine the alternations in the controlling mechanism of these systems with aging and disease. It is concluded that selected features encode adequate information about neural control of the gait. Moreover, the selected features along with tree based machine learning algorithms can play a vital for early detection of NDDs, when pharmacological interventions are still possible.

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Section: Introductionmentioning

confidence: 99%

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Shah

Habib

Aziz

et al. 2020

JPRI

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“…NDDs is an umbrella term used to describe medical conditions that directly affect the neurons within the brain [9]. These include Parkinson's disease (PD), Alzheimer's disease (AD), Huntington disease (HD), and Amyotrophic Lateral Sclerosis (ALS) among others.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

Iram

Jumeily

Fergus

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-The optimization and evaluation of a pattern recognition system requires different problems like multi-class and imbalanced datasets be addressed. This paper presents the classification of multi-class datasets which present more challenges when compare to binary class datasets in machine learning. Furthermore, it argues that the performance evaluation of a classification model for multi-class imbalanced datasets in terms of simple "accuracy rate" can possibly provide misleading results. Other parameters such as failure avoidance, true identification of positive and negative instances of a class and class discrimination are also very important. We, in this paper, hypothesize that "misclassification of true positive patterns should not necessarily be categorized as false negative while evaluating a classifier for multi-class datasets", a common practice that has been observed in the existing literature. In order to address these hidden challenges for the generalization of a particular classifier, several evaluation metrics are compared for a multi-class dataset with four classes; three of them belong to different neurodegenerative diseases and one to control subjects. Three classifiers, linear discriminant, quadratic discriminant and Parzen are selected to demonstrate the results with examples.

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“…Neurodegenerative disease is an umbrella term used to describe medical conditions that directly affect the neurons within the brain (Meriggi et al, 2011). These include Parkinson's, Alzheimer's, Huntington's, and ALS among others.…”

Section: Neurodegenerative Diseasesmentioning

confidence: 99%

A classifier fusion strategy to improve the early detection of neurodegenerative diseases

Iram

Fergus

Al‐Jumeily

et al. 2015

IJAISC

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People in developed countries are living longer, and this has resulted in the prevalence of age-related diseases like Alzheimer's and dementia. Many believe that the early detection of neurodegenerative diseases will provide a much more sustainable framework for dealing with age-related diseases in the future. This paper considers this idea and proposes a new classifier fusion strategy that combines classification algorithms and rules (voting, product, mean, median, maximum and minimum) to measure specific behaviours in people suffering with neurodegenerative diseases. More specifically, the fusion strategy analyses the stride-to-stride intervals in gait and its correlation with neurological functions. This approach is compared with base level classifiers (a single classification algorithm) using a set of feature vectors associated with gait patterns obtained from neurodegenerative patients and healthy people. The results show that the fusion strategy improves classification. Our experiments successfully show that a fusion strategy generates better results and classifies subjects more accurately than base level classifiers.Keywords: combining classifiers; pattern recognition; behaviour classification; machine learning; Huntington's disease; Parkinson's disease amyotrophic lateral sclerosis; ALS; movement signals; artificial intelligence.Reference to this paper should be made as follows: Iram, S., Fergus, P., Al-Jumeily, D., Hussain, A. and Randles, M. (2015) 'A classifier fusion strategy to improve the early detection of neurodegenerative diseases', Int.

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Potential Role of Wearable, Ambulatory and Home Monitoring Systems for Patients with Neurodegenerative Diseases and Their Caregivers

Cited by 4 publications

References 18 publications

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

A classifier fusion strategy to improve the early detection of neurodegenerative diseases

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