Fault Diagnosis in Highly Dependable Medical Wearable Systems

• Tipo: Artigo em Revista Científica Internacional
• Data: 2016

Autores

Oliveira, CC (Autor) Outra; José Machado da Silva (Autor) FEUP

Revista

Título: Journal of Electronic Testing: Theory and Applications (JETTA)

Vol. 32 Nº 4

Páginas: 467-479

ISSN: 0923-8174

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 7 Citações

INSPEC

Classificação Científica

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrónica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Projetos Associados

SIVIC - Sistema Portátil Integrado de Vigilância Cardiovascular

Outras Informações

ID Authenticus: P-00K-P1N

DOI: 10.1007/s10836-016-5602-4

Abstract (EN): High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13