Automatic Video-Oculography System for Detection of Minimal Hepatic Encephalopathy Using Machine Learning Tools

Author:

Calvo Córdoba Alberto1ORCID,García Cena Cecilia E.2ORCID,Montoliu Carmina34ORCID

Affiliation:

1. Escuela Técnica Superior de Ingenieros Industriales, Center for Automation and Robotics, UPM-CSIC, Universidad Politécnica de Madrid, José Gutiérrez Abascal St., 2, 28006 Madrid, Spain

2. Escuela Técnica Superior de Ingeniería y Diseño Industrial, Center for Automation and Robotics, UPM-CSIC, Universidad Politécnica de Madrid, Ronda de Valencia, 3, 28012 Madrid, Spain

3. Instituto de Investigación Sanitaria-INCLIVA, 46010 Valencia, Spain

4. Servicio de Medicina Digestiva, Hospital Clínico de Valencia, 46010 Valencia, Spain

Abstract

This article presents an automatic gaze-tracker system to assist in the detection of minimal hepatic encephalopathy by analyzing eye movements with machine learning tools. To record eye movements, we used video-oculography technology and developed automatic feature-extraction software as well as a machine learning algorithm to assist clinicians in the diagnosis. In order to validate the procedure, we selected a sample (n=47) of cirrhotic patients. Approximately half of them were diagnosed with minimal hepatic encephalopathy (MHE), a common neurological impairment in patients with liver disease. By using the actual gold standard, the Psychometric Hepatic Encephalopathy Score battery, PHES, patients were classified into two groups: cirrhotic patients with MHE and those without MHE. Eye movement tests were carried out on all participants. Using classical statistical concepts, we analyzed the significance of 150 eye movement features, and the most relevant (p-values ≤ 0.05) were selected for training machine learning algorithms. To summarize, while the PHES battery is a time-consuming exploration (between 25–40 min per patient), requiring expert training and not amenable to longitudinal analysis, the automatic video oculography is a simple test that takes between 7 and 10 min per patient and has a sensitivity and a specificity of 93%.

Funder

RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub

Comunidad de Madrid

Structural Funds of the EU

Ministerio de Ciencia e Innovación

Universidad de Valencia, Ayudas para Acciones Especiales

Agencia Valenciana de Innovación, Generalitat Valenciana

Consellería Educación, Generalitat Valenciana

F. Sarabia

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Wearable Near-Eye Tracking Technologies for Health: A Review;Bioengineering;2024-07-22

2. Diagnostic testing of patients with hepatic encephalopathy (review);Spravočnik vrača obŝej praktiki (Journal of Family Medicine);2024-04-20

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