Achmad Rizal

Determining lung sound characterization using Hjorth descriptor

Lung sounds provide important information about the health of the lungs and airways. Lung sounds have a special and distinguishable pattern related to abnormalities that might occur in the lungs or respiration tract. Automatic lung sound recognition is directed to reduce subjectivity in assessing lung sounds. Hjorth descriptor is one method used for observing natural biological signals. Hjorth descriptor was measured to reveal biological signals complexity. In this paper, lung sound characteristics were measured using Hjorth descriptors. Hjorth descriptor will be used to look at complexity of lung sounds. Hjorth descriptors of lung sounds are measured in the time domain and the frequency domain, and clustered using the K-means clustering method. These parameters were tested as to whether they could function as features in automatic lung sound recognition. The experimental results show that the Hjorth descriptor in the time domain is promising in order to be used as a feature for lung sound classification.

Multiscale Hjorth descriptor for lung sound classification

The air flow during the respiration process produces lung sound and provide information on lung health. Automatic lung sound recognition becomes one of the areas of interest to researchers in the field of biomedical signal processing. Signal complexity measurement becomes one of features extraction method for lung sound analysis. Some signal complexity measurement technique that is often used for example are entropy, fractal dimension, and high-order statistics. In this study conducted multiscale Hjorth descriptor measurements on lung sounds for lung sound classification. The results showed that the complexity on a scale of 1-5 yield 95.06% accuracy. Multiscale analysis succeeded in improving the accuracy of the lung sound classification. The higher the scale that is used does not guarantee to increase the accuracy.

Pulmonary crackle feature extraction using tsallis entropy for automatic lung sound classification

pulmonary crackle sound is produced by an abnormality in the respiratory tract. Pulmonary crackle sound is one of lung sound that is discontinuous, short duration and appears on the inspiratory phase, expiratory phase or both. Various methods are used by researchers to detect crackle sound automatically, for example using entropy measurement. Tsallis entropy is a measure of the entropy that has nonextensivity property. Tsallis entropy is often used to measure rapidly changing signals. Crackle sound has both of properties, so hopefully, Tsallis entropy can be utilized as feature extraction techniques for pulmonary crackle sound. The test results showed the use of Tsallis entropy with nonextensivity order of q = 2, 3, and 4 produce the highest accuracy. Using MLP and 3fold crossvalidation, an accuracy of 95.35%, Sensitivity of 90.48%, and 100% Specificity are achieved. The advantage of this method is the fewer number of features produced and simple computation. Tests using data classes and the number of larger data required in future studies.

Signal Domain in Respiratory Sound Analysis: Methods, Application and Future Development

The development of digital signal processing technology encourages researchers to develop better methods for automatic lungs sound recognition system than the existing ones. Lung sounds were originally assessed manually according to doctors expertise. Signal processing techniques are intended to reduce subjectivity factor. Signal processing techniques for lung sound recognition are developed by researchers based on their point of view to the lung sounds. Several researchers developed signal processing methods in a time domain. Meanwhile, other researchers developed signal processing techniques in a frequency domain or combined some signal domains. This paper describes the sensor used, the dataset used and the characteristics of extraction techniques as well as the classifier in the system developed by the previous researchers. In the final section, we describe some possible development of the future potential application of lung sound analysis.

Lung sounds classification using spectrogram's first order statistics features

Lung sounds can indicate a persons health condition. Lung sounds are generated from the air flow in the respiratory tract. Various of signal processing techniques are used for lung sounds analysis to reduce the subjectivity of the lung sound analysis. In this study, we propose lung sound signal analysis using first order statistic texture analysis on the spectrogram. The mean, variance, skewness, kurtosis, and entropy are used as features of each lung sound. These features are analyzed using K-NN with two methods of distance measurement. The proposed method achieves an accuracy of 96.3% for 81 data.

Fractal Dimension for Lung Sound Classification in Multiscale Scheme.

Lung sound is a biological signal with the information of respiratory system health. Health lung sound can be differentiated from other pathological sounds by auscultation. This difference can be objectively analyzed by a number of digital signal processing techniques. One method in analyzing the lung sound is signal complexity analysis using fractal dimension. To improve the accuracy of lung sound classification, Fractal Dimension (FD) is calculated in the multiscale signal using the coarse-grained procedure. The combination of FD and multiscale process generates the more comprehensive information of lung sound. This study used seven types of FD and three types of the classifier. The result showed that Petrosian C in signal with the scale of 1-5 and SVM with fine Gaussian kernel had the highest accuracy of 99% for five classes of lung sound data. The proposed method can be used as an alternative method for computerized lung sound analysis to assist the doctors in the early diagnosis of lung disease.