RT Journal Article
T1 A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis
A1 Stoean, Ruxandra
A1 Stoean, Catalin
A1 Becerra-García, Roberto Antonio
A1 García-Bermúdez, Rodolfo
A1 Atencia-Ruiz, Miguel Alejandro
A1 García-Lagos, Francisco
A1 Velázquez-Pérez, Luis
A1 Joya-Caparrós, Gonzalo
K1 Aprendizaje automático (Inteligencia artificial)
K1 Informática médica
K1 Redes neuronales artificiales
AB Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories are sometimes hard to be distinguished because of samples showing characteristics of both labels in turn in several repetitions of the screening procedure. To this end, the current research appoints a pre-processing clustering step (through self-organizing maps) to group the data based on shape similarity and relabel it accordingly. Subsequently, a deep learning approach (a tandem of convolutional and long short-term memory networks) performs the training classification phase on the ‘cleaned’ samples. The dual methodology was applied for the computational diagnosis of electrooculography tests within spino-cerebral ataxia of type 2. The accuracy obtained for the discrimination into three classes was of 78.24%. The improvement that this duo brings over the deep learner alone does not stem from significantly higher accuracy results when the performance is considered for all classes. The major finding of this combination is that half of the presymptomatic cases were correctly found, in opposition to the single deep model, where this category was sacrificed by the learner in favor of a good accuracy overall. A high accuracy in general is desirable for any medical task, however the correct identification of cases before the symptoms become evident is more important.
PB PLOS
YR 2020
FD 2020-07-21
LK https://hdl.handle.net/10630/41468
UL https://hdl.handle.net/10630/41468
LA eng
NO Stoean R, Stoean C, Becerra-García R, García-Bermúdez R, Atencia M, García-Lagos F, Luis Velázquez-Pérez et Gonzalo Joya (2020) A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis. PLoS ONE 15(7): e0236401. https://doi.org/10.1371/journal.pone.0236401
NO Romanian Ministry of Research and Innovation
NO University of Malaga-Andalucia-Tech . Plan Propio de Investigación y Transferencia
NO Ministerio de Ciencia, Innovación y Universidades, Gobierno de España Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026