ArticleMedical & biological engineering & computing2012
Signal feature extraction by multi-scale PCA and its application to respiratory sound classification.
Article in Medical & biological engineering & computing, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.Sensors (Basel, Switzerland) · 2023Review
- Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis.NPJ digital medicine · 2021Article
- Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN.Royal Society open science · 2017Article
- Multiparametric modeling of the ineffective efforts in assisted ventilation within an ICU.Medical & biological engineering & computing · 2016Article
- Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease.Journal of the American Medical Informatics Association : JAMIA · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34% for the classification of 689 real RS recording segments shows the promising performance of the presented method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.