Authors:
Guilherme Campos
1
and
João Quintas
2
Affiliations:
1
Telecomunicações e Informática (DETI) – Universidade de Aveiro (UA) and Instituto de Engenharia Electrónica e Informática de Aveiro (IEETA) – Universidade de Aveiro (UA), Portugal
;
2
Instituto de Sistemas e Robótica (ISR) – Instituto Superior Técnico (IST), Portugal
Keyword(s):
Adventitious Lung Sounds, Automatic Detection Algorithms, Annotation, Agreement, Performance Metrics, Validation.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Data Engineering
;
Databases and Datawarehousing
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Pattern Recognition and Machine Learning
;
Society, e-Business and e-Government
;
Software Systems in Medicine
;
Web Information Systems and Technologies
Abstract:
The development of computerised diagnosis tools based on lung auscultation necessitates appropriate
validation. So far, this work front has received insufficient attention from researchers; validation studies found
in the literature are largely flawed. We believe that building open-access crowd-sourced information systems
based on large-scale repositories of respiratory sound files is an essential task and should be urgently
addressed. Most diagnosis tools are based on automatic adventitious lung sound (ALS) detection algorithms.
The gold standards required to assess their performance can only be obtained by human expert annotation of
a statistically significant set of respiratory sound files; given the inevitable subjectivity of the process,
statistical agreement criteria must be applied to multiple independent annotations obtained for each file. For
these reasons, the information systems we propose should provide simple, efficient annotation tools; facilitate
the formation of credi
ble annotation panels; apply appropriate agreement criteria and metrics to generate goldstandard
ALS annotation files and, based on them, allow easy quantitative assessment of detection algorithm
performance.
(More)