Multiscale Hjorth descriptor for lung sound classification

Rizal, Achmad; Hidayat, Risanuri; Nugroho, Hanung Adi

doi:10.1063/1.4958601

Cited by 17 publications

(33 citation statements)

References 13 publications

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“…This method produced 30 features and later this would be reduced to 10 features. In a previous study, the use of Hjorth descriptors for the extraction of lung sounds showed promising accuracy (Rizal et al, 2015a;2015b). From the test result, the proposed method was shown to provide higher accuracy compared to previous research (Rizal et al, 2015b).…”

Section: Introductionmentioning

confidence: 78%

“…The Hjorth descriptor usage for lung sound analysis has been presented in some papers (Rizal et al, 2015a;2015b). In the paper by Rizal et al (2015a), the Hjorth descriptor was calculated on the entire signal and produced an accuracy of 77% for five classes of data.…”

Section: Related Workmentioning

confidence: 99%

“…In the paper by Rizal et al (2015a), the Hjorth descriptor was calculated on the entire signal and produced an accuracy of 77% for five classes of data. With the same data, the Hjorth descriptors were calculated using a multiscale scheme (Rizal et al, 2015b). The resulting accuracy reached 95.06%.…”

Section: Related Workmentioning

confidence: 99%

“…There were 81 data sources that consisted of 18 normal bronchial, 13 asthmas, 15 crackle sounds, 15 stridors and 20 plural rubs. The same data was also used in some previous studies (Rizal et al, 2015a;2015b).…”

Section: Lung Sound Datamentioning

confidence: 99%

“…The signal complexity of lung sounds is evidenced by their fractal properties (Gnitecki and Moussavi, 2005) caused by the structure of the lungs that have self-similarity properties (Kitaoka et al, 1999). As a biological signal, the lung sound is also suspected to have a multiscale nature so that the multiscale analysis of lung sounds produces higher accuracy (Costa et al, 2002;Rizal et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

Rizal¹,

Hidayat²,

Nugroho³

2017

American Journal of Applied Sciences

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Lung sound is produced by the respiration process in the human respiratory tract. It contains information about the health of the respiratory organs. Lung sound is non-stationary signals and complex signals. One method for the analysis of non-stationary signals often used for the analysis of lung sounds is Empirical Mode Decomposition (EMD). EMD is used to view the Instantaneous Frequency (IF) of the lung sound to differentiate the types of lung sounds. Features extraction directly on Intrinsic Mode Function (IMF) of EMD result is rarely performed in the lung sound analysis. In this research, the EMD was used to obtain IMF of lung sounds. IMF from lung sounds was then analyzed using the Hjorth descriptors. As a classifier, we used Multilayer Perceptron (MLP) with a three-fold cross validation (3fold CV) for validation. From the test, it was found that activity parameter in the first 10 IMF yielded 98.8% accuracy on five classes of data tested. The proposed method showed the excellence of the measurement of the Hjorth descriptor on IMF for feature extraction in lung sound classification.

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Section: Introductionmentioning

confidence: 78%