A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds

Şen, İpek; Saraçlar, Murat; Kahya, Yasemin P.

doi:10.1109/tbme.2015.2403616

Cited by 57 publications

(29 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study in [57] compared the performance of a GMM and a SVM for the classification of normal and abnormal recordings. An AR model was used as a feature set using LOOCV.…”

Section: Resultsmentioning

confidence: 99%

Automatic adventitious respiratory sound analysis: A systematic review

2017

View full text Add to dashboard Cite

BackgroundAutomatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established.ObjectiveTo provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works.Data sourcesA systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification.Study selectionOnly articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated.Data extractionInvestigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved.Data synthesisA total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis.LimitationsDirect comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions.ConclusionA review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventiona...

show abstract

“…The study in [57] compared the performance of a GMM and a SVM for the classification of normal and abnormal recordings. An AR model was used as a feature set using LOOCV.…”

Section: Resultsmentioning

confidence: 99%

Automatic adventitious respiratory sound analysis: A systematic review

2017

View full text Add to dashboard Cite

show abstract

“…Correct classification rates of all lung sound signals range between %83 and 93%. In the majority of studies [9][10][11][12][13][14][15], the binary cases such as healthy versus pathological or normal versus abnormal sounds classifications has been considered [18]. As well as Fourier transform, autoregressive (AR) and autoregressive moving average (ARMA) models are commonly used in many studies [2,19,20,21] for spectral analysis.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

Göğüş¹,

Karlık²,

Harman³

2016

IJCIS

View full text Add to dashboard Cite

This study presents detection of pulmonary disorders using different spectral analysis methods such as fast Fourier transform, autoregressive and the autoregressive moving average. Power spectral densities of the sounds were estimated through these methods. Feature vectors were constructed by extracting statistical features from the PSDs. Created feature vectors were used as inputs into the artificial neural networks. Then performances of spectral analysis methods were compared according to classification accuracies, sensitivities and specificities. In this aspect, the study is a comparative study of different spectral analysis methods.

show abstract

“…There are many results in the literature that address the problem of informative gene selection for DNA microarray classification. A large group of these results [10][11][12][13][14][15][16] are based on support vector machine (SVM) and its variants, a class of non-probabilistic machine learning algorithms that seek a nonlinear decision boundary efficiently. For instance, fMRI-based hierarchical SVM was applied to the automatic classification and grading of liver fibrosis in [11].…”

Section: Introductionmentioning

confidence: 99%

Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information

Wang¹,

Yang²,

Lu³

2019

Applied Mathematical Modelling

View full text Add to dashboard Cite

Due to the advantage of achieving a better performance under weak regularization, elastic net has attracted wide attention in statistics, machine learning, bioinformatics, and other fields. In particular, a variation of the elastic net, adaptive elastic net (AEN), integrates the adaptive grouping effect. In this paper, we aim to develop a new algorithm: Adaptive Elastic Net with Conditional Mutual Information (AEN-CMI) that further improves AEN by incorporating conditional mutual information into the gene selection process. We apply this new algorithm to screen significant genes for two kinds of cancers: colon cancer and leukemia. Compared with other algorithms including Support Vector Machine, Classic Elastic Net and Adaptive Elastic Net, the proposed algorithm, AEN-CMI, obtains the best classification performance using the least number of genes.

show abstract

A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds

Abstract: This study proposes new methodologies for diagnostic classification of pulmonary sounds, and suggests using a hierarchical framework for the first time.

Cited by 57 publications

References 11 publications

Automatic adventitious respiratory sound analysis: A systematic review

Automatic adventitious respiratory sound analysis: A systematic review

Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information

Contact Info

Product

Resources

About