A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.

ABSTRACT: 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 co

SUBMITTER: Stoean R 

PROVIDER: S-EPMC7373280 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature