• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.220-224
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• 2000

A new auscultation system for the detection of breath sound form trachea was developed in house. Small size microphone(panasonic pin microphone) was encapsuled in a housing for resonant effect, and hardware for the sound detection was fabricated. Pulmonary function test results were compared with the parameters extracted from frequency spectrum of breath sound obtained from the developed system. Results showed that the peak frequency and relative ratio of integral values between low(80∼400Hz) and high(400∼800Hz) frequency ranges revealed the significant differences. Developed system could be used for distinguishing normal subject and the patients who have pulmonary disease.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.91-98
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• 2018

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.

• ETRI Journal
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• v.37 no.4
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• pp.824-832
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• 2015

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.

• The Journal of Korean Medicine
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• v.32 no.6
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• pp.10-17
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• 2011

Background: Vibration response imaging (VRI) is a new technology that records energy generated by airflow during the respiration cycle. Analysis of lung sound using VRI may overcome the limitations of auscultation. Objectives: To set a VRI standard for healthy Koreans, we conducted a clinical assessment to evaluate breath sound images and quantification in healthy subjects and compared the findings with reported breath sound characteristics. Methods: Recordings were performed using the VRIxp. Eighty subjects took a deep breath four times during a 12-second interval while sitting upright. The quantitative aspect was analyzed using the VRI quantitative lung data (QLD) for total left lung, total right lung and for six lung regions: left upper lung (LUL), left middle lung (LML), left lower lung (LLL), right upper lung (RUL), right middle lung (RML), right lower lung (RLL). The qualitative aspect was provided through image assessments by three reviewers. Results: In all regions the left lung had significantly higher QLD than the right lung (P<0.005, paired t-test). The inter-rater agreement was 0.78. 84% of the images were found normal by the final assessment. Among the 16% (n=13) of images with abnormal final assessment, the most common flawed features were dynamic image (77%, n=10) and maximum energy frame (MEF) shape (77%, n=10). No significant differences were found between males and females for QLD but there were significant differences in qualitative aspects including dynamic images, MEF shape, and missing LLL. Conclusion: The characteristics of healthy Koreans are similar to those of Western subjects reported previously. VRI is easy to use and objective, and so is helpful to diagnose patients with respiratory diseases and to monitor the progress of diseases after medical treatments.

• Clinical and Experimental Pediatrics
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• v.53 no.2
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• pp.244-247
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• 2010

Spontaneous pneumomediastinum in the absence of predisposing risk factors has been rarely observed in full-term neonates. A 3-day-old neonate, delivered vaginally at term without any perinatal complications or signs of respiratory difficulty, was referred to the Seoul National University Children's Hospital because of reduced heart sound detected during routine neonatal examination. Chest computed tomography (CT) showed air collection in the anterior mediastinum. The baby developed respiratory distress on the fourth day and required supplemental oxygen. On the seventh day, there was no sign of respiratory difficulty, and x-ray examination showed no demonstrable pneumomediastinum. Hence, careful neonatal physical examination is essential during the postnatal assessment of newborns, and spontaneous pneumomediastinum should be considered when a healthy newborn presents with reduced heart sound.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12B
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• pp.1123-1130
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• 2008

Baby's diseases diagnosis has inconvenient for received direct coming to help that order expression ability was insufficiency which consciousness situation concern about the infant health because of birth rate and decrease the marriage rate and divorce rate. So in this paper through the infant crying sound about home a foundation which infant diseases develop the system comparison normal infant with take a infant that analysis the extract the voice analytics component. Especially this paper propose about the methodology for development system that infant cold, infant pneumonia, infant asthma among extract the crying sound feature part for infant respiratory diseases discussion the most easy has involved the infant. So infant respiratory put case stimulus diseases about all voice organs and experiment the analysis method through the bandwidth about phonetics analysis component that comparison normal infant with take a respiratory infant. Through these method, we were extracted to results that infant's frequency bandwidth suffering from respiratory diseases than a normal infant is short.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• The Korean Journal of Rehabilitation Nursing
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• v.10 no.2
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• pp.108-115
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• 2007

Purpose: This study was to investigate the effects of respiratory rehabilitation training on the respiratory functions of hospitalized cervical spinal cord injury patients. Methods: One group pre and post test design was used. Subjects were 20 cervical spinal cord injury inpatients of the national rehabilitation center. Training program consisted of air cumulation training, manual assisted coughing training, and abdominal breathing. Trained rehabilitation nurse implemented 20 minutes program twice a day for 4 weeks. Respiratory function was measured as peak coughing flow rate, and perceived respiratory difficulty after activity on wheel chair for 30 minutes and during speaking and singing. Perceived respiratory difficulty was measured with modified Borg scale. Also content analysis was done with the result of open ended question about subjective feeling about training. All variables were measured 3 times before, 2weeks and 4 weeks after the program. Results: Peak coughing flow rate significantly improved after compared to before training. Also all three perceived respiratory difficulty variables decreased significantly after training. In the content analysis, 'it's easier to cough up phlegm' was the most frequent answered subjective feeling. 'Sound at speaking and coughing became louder', 'respiratory volume increased', and 'comfortable chest feeling' were frequent answered subjective feeling, in order. Conclusion: Although it is preliminary since no control group, respiratory rehabilitation training was found to be effective to improve respiratory function in terms of peak coughing flow rate, perceived respiratory difficulty, and subjective feeling. It is necessary further systemic research to investigate the effects of respiratory rehabilitation training.

• Journal of Veterinary Clinics
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• v.35 no.2
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• pp.57-59
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• 2018

A seven-month-old Hanwoo steer was presented immediately after transport with respiratory symptoms including a nasal discharge, depression, and anorexia. Though repeated treatments, bronchopneumonia had not been improved and had persisted for 10 months. Then, obstructive breath sound was heard. A cyst adjacent to the epiglottis could be observed with respiratory endoscopy. Consequently, chronic bronchopneumonia induced laryngeal cyst formation, resulting in obstructive dyspnea. And respiratory endoscopy may be useful for differentiating the causes of dyspnea in bovine clinical practice.