Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

Göğüş, Fatma Zehra; Karlık, Bekir; Harman, Güneş

doi:10.1080/18756891.2016.1204110

Cited by 11 publications

(4 citation statements)

References 29 publications

(49 reference statements)

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“…Secondly, these crucial features are used for detecting or classifying adventitious respiratory sounds [14]. Commonly preferred methods for feature extraction in the literature are MFCCs [9], spectral features [15], Fourier [16], The Autoregressive Model [17], and Wavelet coefficients [18]. For classification, algorithms such as ANN [19], SVM [20], GMM [11], k-NN [9], and LR models [13] are used.…”

Section: Methodsmentioning

confidence: 99%

Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods

DEMİRCİ

Koçyiğit

Kızılırmak

et al. 2021

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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The computerized respiratory sound analysis systems provide vital information concerning the current condition of the lung. These systems, used by physicians for the diagnosis of diseases, help to classify respiratory sounds. Because each physician has different knowledge and experience, there is a problem with diagnosing and treating respiratory system diseases. This study will help the physician to decide in various difficult diagnostic situations easily. For this purpose, different machine learning classifiers and feature extraction models have been constituted to classify respiratory sounds as healthy and patient then its results were compared. In this study, Empirical Mode Decomposition, Mel Frequency Cepstral Coefficients, and Wavelet Transform methods are used for feature extraction, while k Nearest Neighbor, Artificial Neural Networks, and Support Vector Machines are used for classification. The best accuracy was 98.8% by using combination Mel Frequency Cepstral Coefficient and k Nearest Neighbor methods.

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Section: Methodsmentioning

confidence: 99%

Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods

DEMİRCİ

Koçyiğit

Kızılırmak

et al. 2021

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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“…Asthma is one of lung diseases that produces wheeze sound. Asthma is caused by an obstruction of the airways [25]. Meanwhile, the crackle is a nonmusical, discontinuous, and short duration lung sound.…”

Section: Lung Sound Datasetmentioning

confidence: 99%

Lung sound classification using multiresolution Higuchi fractal dimension measurement

Rizal

Hidayat

Nugroho

et al. 2023

IJECE

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Lung sound is one indicator of abnormalities in the lungs and respiratory tract. Research for automatic lung sound classification has become one of the interests for researchers because lung disease is one of the diseases with the most sufferers in the world. The use of lung sounds as a source of information because of the ease in data acquisition and auscultation is a standard method in examining pulmonary function. This study simulated the potential use of Higuchi fractal dimension (HFD) as a feature extraction method for lung sound classification. HFD calculations were run on a series of k values to generate some HFD values as features. According to the simulation results, the proposed method could produce an accuracy of up to 97.98% for five classes of lung sound data. The results also suggested that the shift in HFD values over the selection of a time interval k can be used for lung sound classification.

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“…Spectrum analysis performance was recorded in classification accuracy (CA) as 85.67% for AR, 84.67% for ARMA, and 80.33% for FFT, but that accuracy is only for limited data points new or large test data inputted, then accuracy is not guaranteed. It only checks the normal and COPD individuals, not identifying the other abnormal RS classes such as wheeze, rhonchi, and stridor ( 33 ).…”

Section: Related Workmentioning

confidence: 99%

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

et al. 2021

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Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

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Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

Cited by 11 publications

References 29 publications

Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods

Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods

Lung sound classification using multiresolution Higuchi fractal dimension measurement

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

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