Respiratory Disease Diagnosis through Comprehensive Analysis of
Spectrograms of Lung Sounds
Authors: Mitsuke Takuma , Shimakawa Hiromitsu , Harada Humiko 
Published: July 2023

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.