Fault Diagnosis in Highly Dependable Medical Wearable Systems

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Oliveira, CC (Author) Other; José Machado da Silva (Author) FEUP

Journal

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

Vol. 32 No. 4

Pages: 467-479

ISSN: 0923-8174

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 7 Citations

INSPEC

Scientific classification

CORDIS: Technological sciences > Engineering > Electronic engineering

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Associated Projects

SIVIC - Sistema Portátil Integrado de Vigilância Cardiovascular

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13