Respiratory Disease Diagnosis through Comprehensive Analysis of Spectrograms of Lung Sounds

Abstract: The study proposes a digital stethoscope to assist physicians who are not respiratory specialists to diagnose specific diseases from lung sounds in local areas which lacks specialists and advanced medical equipment. The digital stethoscope presents a spectrogram of the auscultatory sound that illustrates changes in its feature values to visually present the area where the abnormal sound occurs. It not only assists non-specialist physicians in diagnosis but also provides easier explanations for patients. Accurate auscultation requires specialized knowledge and experience. Non-specialist physicians have difficulty distinguishing the characteristics of abnormal sounds among lung sounds which include a wide variety of sounds. Even if they find an abnormal sound suspected to be a disease, physicians will provide patients with only oral diagnosis explanations which would prevent patients from understanding their conditions in depth. The proposed method converts lung sound data collected by a digital stethoscope into a visual spectrogram showing the frequency features. The method uses the short-time Fourier transform as a method to extract the frequency features of lung sounds for each short segment in the whole time series of the lung sound. The converted spectrogram is used to detect disease-specific abnormal sounds comprehensively. The degree of abnormal sounds that appears in the inspiratory and expiratory phases varies with disease progression. The proposed method identifies abnormal sounds in the inspiratory and expiratory phases. Based on the inspiratory and expiratory phases recorded in the diagnostic by specialists, the method derives whether the abnormal sound occurs in each phase. It enables the method to detect specific respiratory diseases along with the degree of their progression. Furthermore, it also allows the method to visually present the location of detected abnormal sounds to patients. This paper uses a short-time Fourier transform as a method for extracting frequency features of lung sounds in a certain range. It also shows that feature extraction as a spectrogram that emphasizes the low frequency band, which is the human audible range, is effective in assisting the non-specialist physician in diagnosis. This paper prepares experimental data from real diagnoses so that data noise and disease features can be taken into account. The diagnostic accuracy is evaluated with a method of segmenting spectrogram images to extract frequency features at specific times. The method constructs a model for detecting fine crackles using the machine learning algorithm Decision Tree. The experimental results have shown that the accuracy of detecting fine crackles is 0.89. The high accuracy obtained from this model allows us to confirm the effectiveness of the proposed method for supporting for non-specialist physicians to distinguish abnormal sounds. The successful detection of fine crackles in the expiratory or inspiratory phase suggests that the progression degree of the disease could be estimated with the proposed digital stethoscope. The digital stethoscope will make it possible to accurately diagnose respiratory diseases along with a comprehensive explanation of diagnosis results to patients in areas with poor medical facilities. It also provides non-specialists with dependable diagnostic aid.

Keywords: Spectrogram, Digital Stethoscope, Decision Tree, Anomalous Sound Detection

DOI: 10.54941/ahfe1004064

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