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Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network

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Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network

dc.contributor.author	Pedrero Sánchez, José Francisco
dc.contributor.author	Belda Lois, Juan Manuel
dc.contributor.author	Serra Añó, Pilar
dc.contributor.author	Inglés, Marta
dc.contributor.author	López Pascual, Juan
dc.date.accessioned	2023-06-13T07:33:50Z
dc.date.available	2023-06-14T04:45:06Z
dc.date.issued	2022	es_ES
dc.identifier.citation	José Francisco Pedrero-Sánchez, Juan-Manuel Belda-Lois, Pilar Serra-Añó, Marta Inglés, Juan López-Pascual (2022). Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network. Biomedical Signal Processing and Control, 75, 103617.	es_ES
dc.identifier.uri	https://hdl.handle.net/10550/87868
dc.description.abstract	Signal processing, for delimitation of the target events and parametrization, is usually required when instrumented assessment is conducted to determine an individual’s functional status. However, these procedures may rule out relevant information obtained by sensors. To prevent this, the use of models based on neural networks that automatically extract relevant features from the raw signal may improve the characterization of the functional status. Thus, the aim of the study was to determine the classification accuracy of a multi-head convolutional layered neural network (CNN) using a simple functional mobility test in people with different conditions. The raw data from an inertial sensor embedded in a smartphone worn by 90 volunteers (i.e. 30 volunteers with Alzheimer’s disease, 30 with Parkinson’s disease and 30 healthy elderly people) was obtained. The CNN classification accuracy was compared to that of the two parametric classifiers, namely, linear discriminant analysis and multilayer perceptron, a neural network-based classifier. As a result, the validation process revealed that the CNN classifier correctly assigned 100% of the participants to each group. The best accuracy in pathology classification for the two parametric classifiers ranged from 55% to 88%. Therefore, the CNN model provided enhanced classification accuracy as compared to the parametric approaches, even better than the neural network-based classifier. Non parametrization may increase relevant information, thus enhancing pathology impact characterization.	es_ES
dc.language.iso	en	es_ES
dc.publisher	Elsevier	es_ES
dc.subject	alzheimer disease	es_ES
dc.subject	parkinson’s disease	es_ES
dc.subject	inertial sensor	es_ES
dc.subject	multi-branch convolutional classifier	es_ES
dc.subject	functional assessment	es_ES
dc.title	Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network	es_ES
dc.type	journal article	es_ES
dc.subject.unesco	UNESCO::CIENCIAS TECNOLÓGICAS	es_ES
dc.identifier.doi	10.1016/j.bspc.2022.103617	es_ES
dc.accrualmethod	CI	es_ES
dc.embargo.terms	0 days	es_ES
dc.type.hasVersion	VoR	es_ES
dc.rights.accessRights	open access	es_ES