MEMS Resonant Microphone Array for Lung Sound Classification

Lung sound is one of the parameters of respiratory health. This sound has a specific character if there is a disease in the lungs. In some cases, it is difficult to distinguish one type of lung sound to another. It takes the expertise, experience and sensitivity of clinicians to avoid misdiagnosis. Therefore, many studies have proposed a feature extraction method combined with automatic classification method for the detection of lung disease through lung sound analysis. Since the complex nature of biological signals which are produced by complex processes, the multiscale method is an interesting feature extraction method to be developed. This study proposes an empirical mode decomposition (EMD) and a modified gray level difference (GLD) for a lung sound classification. The EMD was used to decompose the signal, and then GLD was measured on each decomposed signal as a feature set. There are five classes of lung sounds which were simulated in this study, including normal, wheeze, crackle, pleural rub, and stridor. Performance evaluation was carried out using a multilayer perceptron (MLP) and 3-fold cross-validation. This proposed method yielded the highest accuracy of 96.97%. This study outperformed several previous studies which were simulated on the same dataset. It is hoped that in the future, the proposed methods can be tested on larger datasets to determine the robustness of the methods.

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Convolutional neural networks based efficient approach for classification of lung diseases

Health Information Science and Systems ◽

10.1007/s13755-019-0091-3 ◽

2019 ◽

Vol 8 (1) ◽

Cited By ~ 8

Author(s):

Fatih Demir ◽

Abdulkadir Sengur ◽

Varun Bajaj

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Lung Diseases ◽

Support Vector ◽

Lung Sounds ◽

Lung Sound ◽

Frequency Method ◽

Time Frequency ◽

Sound Classification

AbstractTreatment of lung diseases, which are the third most common cause of death in the world, is of great importance in the medical field. Many studies using lung sounds recorded with stethoscope have been conducted in the literature in order to diagnose the lung diseases with artificial intelligence-compatible devices and to assist the experts in their diagnosis. In this paper, ICBHI 2017 database which includes different sample frequencies, noise and background sounds was used for the classification of lung sounds. The lung sound signals were initially converted to spectrogram images by using time–frequency method. The short time Fourier transform (STFT) method was considered as time–frequency transformation. Two deep learning based approaches were used for lung sound classification. In the first approach, a pre-trained deep convolutional neural networks (CNN) model was used for feature extraction and a support vector machine (SVM) classifier was used in classification of the lung sounds. In the second approach, the pre-trained deep CNN model was fine-tuned (transfer learning) via spectrogram images for lung sound classification. The accuracies of the proposed methods were tested by using the ten-fold cross validation. The accuracies for the first and second proposed methods were 65.5% and 63.09%, respectively. The obtained accuracies were then compared with some of the existing results and it was seen that obtained scores were better than the other results.

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