• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.101-102
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• 2007

Heart diseases are critical and should be detected as soon as possible. A stethoscope is a simple device to find cardiac disorder but requires keen experiences in heart sounds. We evaluate a cardiac disorder classifier by using heart sounds recorded by a digital wireless stethoscope developed in this work. The classifier uses hidden Markov models with circular state transition to model the heart sounds. We train the classifier using two kinds of data: One recorded by using our stethoscope and the other sampled from a clean heart sound database. In classification experiments using 165 sound clips, the classifier shows the classification accuracy of 82% in classifying 6 cardiac disorder categories.

• Tuberculosis and Respiratory Diseases
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• v.86 no.4
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.237-243
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• 2014

Heart sounds of patient's chest could be heard using an analog stethoscope. However, auscultation of a heart sound can be diagnosed differently by each doctor hearing it. Therefore the condition of each patient is determined by the subjective comments based on the hearing ability of a physician who has years of experience. In this paper, through analysis of heart sound and heart rate of the patient's condition, we will define minutely how to diagnose the condition of patient using a wireless digital stethoscope diagnostic system. And it is possible to perform an objective medical diagnosis by applying LCR (Level Crossing Rate) and to show the relationship of a disease using this system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.722-727
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• 2013

Medical treatment for animals is very difficult as the opinions of pets' masters take priority over treatment regardless of the seriousness of pets' disease or the needs of medical treatment. In case that a pet has heart disease, especially, it is difficult to get the direct answer from the pet's master on the decision or confirmation of treatment. For those reasons, it is almost impossible to predict and treat the pet before an emergency like the heart failure or an unexpected death happens. Using stethoscope can be the first diagnosis method to check the heart or any kinds of disease inside the body. High-tech equipments like CT, X-ray or Ultrasound can be used, but they can only be used as a second choice of diagnosis method since it requires professional skills and its high price. That's why stethoscope is still the best diagnostic tool when one makes the first diagnosis. In this study, we give a detailed account of digital diagnosis system in which veterinarians can analyze the sound from stethoscope without bringing it to their ears and make a diagnosis wherever they are. And we suggest a new concept of diagnosis system surrounding, which shows the relativeness of disease through Level Crossing Rate(LCR) and energy level from the stethoscope sound made in this system.

• Journal of Biomedical Engineering Research
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• v.43 no.2
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• pp.124-130
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• 2022

In this paper, we proposed a portable electronic cardiogram and stethoscope (ECGS) that can simultaneously perform the electrocardiogram (ECG) and auscultation tests to increase the reliability of diagnosis of heart disease. To measure the ECG and heart sound (HS) at the same time, three ECG electrodes and a microphone sensor were combined into a triangular shape with a width of 90 mm and a height of 97 mm that can be held in one hand. In order to prevent skin problems when they contact the patient's skin, a capacitive coupled electrode was selected as the ECG electrode and a silicone material was used in a chest piece with the microphone sensor. For the signals measured from the electrodes and the chest piece, filters were respectively configured to pass only the signals of 0.01-100 Hz and 20-250 Hz, which are frequency bands for ECG and HS. The filtered ECG and HS analog signals were converted into digital signals and transmitted to a PC using wireless communication for monitoring them. The HS could be auscultated simultaneously using an earphone. The monitored ECG had an SNR of about 34 dB and a P-QRS-T waveform is clearly visible. In addition, the HS had an SNR of about 28 dB and both S₁ and S₂ are clearly visible. It is expected that it can aid doctors' inexperience in analyzing the ECG and HS.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.41-47
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• 2008

This paper describes a wearable electro-stethoscope module for home-healthcare system. The module is consisted of a microphone, an instrumentation amplifier, a filter, a power amplifier etc. and is light and small. The phonogram signal from the module shows good performance. The test for the material and size of the sound collector of the chest piece is performed and the results is reflected on the prototype product. If the module is connected to wired or wireless communication network, so people can check their health without going hospital.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.57-63
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• 2012

Effective use of stethoscope is very important for primary clinical diagnosis for the increasing cardiovascular and respiratory disease. This study developed the contact vibration sensor using piezopolymer film which minimizes the ambient noise, and signal processing algorithm was applied for providing better auscultation sounds compare to the existing electronic stethoscopes. Especially, low frequency heart sounds were acquired without distortion, and the quality of lung sounds were improved. Also, auscultating sounds could be transmitted using bluetooth, which made possible to be used for the u-healthcare environment. Results of this study, auscultation of heart and lung sounds, could be applied to the convergence industry of medical and information communication technology through remote diagnosis.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.949-958
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• 2022

Objective: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). Materials and Methods: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. Results: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. Conclusion: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.