• Asian-Australasian Journal of Animal Sciences
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• 제23권8호
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• pp.1096-1104
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• 2010

This bio-acoustic study was aimed at classifying the different porcine wasting diseases through sound analysis with emphasis given to differences in the acoustic footprints of coughs in porcine circo virus type 2 (PCV2), porcine reproductive and respiratory syndrome (PRRS) virus and Mycoplasma hyopneumoniae (MH) - infected pigs from a normal cough. A total of 36 pigs (Yorkshire${\times}$Landrace${\times}$Duroc) with average weight ranging between 25-30 kg were studied, and blood samples of the suspected infected pigs were collected and subjected to serological analysis to determine PCV2, PRRS and MH. Sounds emitted by coughing pigs were recorded individually for 30 minutes depending on cough attacks by a digital camcorder placed within a meter distance from the animal. Recorded signals were digitalized in a PC using the Cool Edit Program, classified through labeling method, and analyzed by one-way analysis of variance and discriminant analysis. Input features after classification showed that normal cough had the highest pitch level compared to other infectious diseases (p<0.002) but not statistically different from PRRS and MH. PCV2 differed statistically (p<0.002) from the normal cough and PRRS but not from MH. MH had the highest intensity and all coughs differed statistically from each other (p<0.0001). PCV2 was statistically different from others (p<0.0001) in formants 1, 2, 3 and 4. There was no statistical difference in duration between different porcine diseases and the normal cough (p>0.6863). Mechanisms of cough sound creation in the airway could be used to explain these observed acoustic differences and these findings indicated that the existence of acoustically different cough patterns depend on causes or the animals' respiratory system conditions. Conclusively, differences in the status of lungs results in different cough sounds. Finally, this study could be useful in supporting an early detection method based on the on-line cough counter algorithm for the initial diagnosis of sick animals in breeding farms.

• International journal of advanced smart convergence
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• 제9권4호
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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%.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
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• pp.43-46
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• 2023

호흡기 관련 전염병의 주된 증상인 기침은 공기 중에 감염된 병원균을 퍼트리며 비감염자가 해당 병원균에 노출된 경우 높은 확률로 해당 전염병에 감염될 위험이 있다. 또한 사람들이 많이 모이는 공공장소 및 실내 공간에서의 기침 탐지 및 조치는 전염병의 대규모 유행을 예방할 수 있는 효율적인 방법이다. 따라서 본 논문에서는 탐지해야 하는 기침 소리 및 일상생활 속 발생할 수 있는 기침과 유사한 배경 소리 들을 Mel-Spectrogram으로 변환한 후 시각화된 특징을 CNN 모델에 학습시켜 기침 탐지를 진행하며, 일반적으로 사용되는 사전 학습된 CNN 모델에 제안된 Attention 모듈의 적용이 기침 탐지 성능 향상에 도움이 됨을 입증하였다.

• Journal of Microbiology and Biotechnology
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• 제28권3호
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• pp.465-472
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• 2018

Our aim was to determine the detection rate of respiratory viruses (RVs) in feces of patients with acute viral respiratory infection (AVRI) and the detection rate of diarrheal viruses (DVs) in nasopharyngeal samples from patients with acute viral gastroenteritis. The relationships between the presence of fecal RVs or nasopharyngeal DVs and their impacts on the clinical severity were also investigated. A total of 144 fecal specimens were collected from AVRI patients and 95 nasopharyngeal specimens were collected from acute viral gastroenteritis patients. Clinical characteristics and laboratory profiles were compared between subgroups on the basis of the presence or absence of virus in the specimens. The detection rate of RVs in feces was 17.4% (25/144), whereas the detection rate for viruses identical to the respiratory pathogen was 10.4% (identical group, 15/144). Within the identical group, adenovirus (86.7%, 13/15) was most commonly found. Patients in the identical group showed statistically higher values for C-reactive protein, mean age, increased frequency of vomiting, and decreased frequency of chest film involvement and cough (p < 0.05). The detection rate of nasopharyngeal DVs among acute viral gastroenteritis patients was 19.0% (18/95), and in the identical group it was 15.8% (15/95). Norovirus group II and enteric adenovirus were the major pathogens detected in the identical group. There were no significant differences in clinical characteristics and laboratory profiles between the subgroups. In conclusion, the major pathogens of fecal RV and nasopharyngeal DV were adenovirus and norovirus group II, respectively. However, their relationship with the clinical symptoms or disease severity is unclear.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 추계학술발표대회
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• pp.746-748
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• 2018

전 세계적으로 인플루엔자에 의해 매년 29~64만의 사망자가 발생하며 사회, 경제적 피해를 일으키고 있다. 기침에 의해 생성된 비말은 인플루엔자의 주요 전파 방법으로, 기침 감지 기술을 통해 확산 방지가 가능하다. 이전의 기침 감지에 대한 연구는 기침 소리와 전통적인 기계학습기법을 사용하였다. 본 논문은 기침 소리와 더불어 기침 시 발생하는 신체의 움직임 정보를 동시에 학습하는 멀티모달 딥러닝 기반의 기침 감지 모델을 제안한다. 도출된 모델과 기존의 모델과의 성능 비교를 통해 제안한 모델이 이전의 기침 감지 모델보다 정확한 기침 인식이 가능함을 보였다. 본 논문이 제안하는 모델은 스마트 워치와 같은 웨어러블 기기에 적용되면 인플루엔자의 확산 방지에 크게 기여할 수 있을 것이다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 춘계학술발표대회
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• pp.439-441
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• 2018

딥 러닝의 높은 성능으로 여러 분야에 사용되며 기침 탐지에서도 수행된다. 이 때 기침과 유사한 재채기, 큰 소리는 단일 데이터만으로는 구분하기에 한계가 있다. 본 논문에서는 기존의 오디오 데이터와 오디오 데이터를 인코딩 한 스펙트로그램 이미지 데이터를 함께 학습하는 멀티 모달 딥 러닝을 적용하는 방법을 사용한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 춘계학술발표대회
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• pp.428-430
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• 2018

본 논문에서는 독감확산 예측을 위한 웨어러블 센서를 이용한 기침 감지 모델을 제안한다. 서로 상이한 기침 신체데이터를 사용하고 기침 감지 알고리즘의 구현없이 기계가 학습하는 방식인 멀티모달 DNN을 이용하여 설계하였다. 또한 웨어러블 센서를 통해 실생활의 기침 오디오 데이터와 기침 3축 가속도 데이터를 수집하였고, 두 개의 데이터중 하나의 데이터만으로도 감지를 위한 학습이 가능토록하기 위해 각각 MFCC와 FFT를 이용하여 특징 벡터를 추출하는 방법을 이용하였다.

• 한국임상수의학회지
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• 제35권6호
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• pp.273-275
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• 2018

A 6-month-old male cat was presented for investigation of depression, loss of appetite, dehydration, pale conjunctival mucous membrane, weight loss, fast heart and respiratory rates, nasal discharge and cough. Nasal swabs collected from the studied cat. As the results of bacterial culture with nasal swabs, it was suspected with Mycoplasma spp. Also, Mycoplasma species was detected by the PCR reaction with Mycoplasma genus primers. At species PCR assay, the specimens evaluated for the presence of M. felis, M. arginini, M. gateae, and Acholeplasma laidlawii and the result was visualization of bands from 238 bp in agarose gel 1.5% showing M. felis amplicons in samples. In conclusion, we detected M. felis in a cat with respiratory disease. PCR was able to detect successfully M. felis infection in cats.

• Clinical and Experimental Pediatrics
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• 제64권12호
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• pp.652-660
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• 2021

Background: Viral load and shedding duration are highly associated with the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, limited studies have reported on viral load or shedding in children and adolescents infected with sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Purpose: This study aimed to investigate the natural course of viral load in asymptomatic or mild pediatric cases. Methods: Thirty-one children (<18 years) with confirmed SARS-CoV-2 infection were hospitalized and enrolled in this study. Viral loads were evaluated in nasopharyngeal swab samples using real-time reverse transcription polymerase chain reaction (E, RdRp, N genes). cycle threshold (Ct) values were measured when patients met the clinical criteria to be released from quarantine. Results: The mean age of the patients was 9.8 years, 18 (58%) had mild disease, and 13 (42%) were asymptomatic. Most children were infected by adult family members, most commonly by their mothers. The most common symptoms were fever and sputum (26%), followed by cough and runny nose. Nine patients (29%) had a high or intermediate viral load (Ct value≤30) when they had no clinical symptoms. Viral load showed no difference between symptomatic and asymptomatic patients. Viral rebounds were found in 15 cases (48%), which contributed to prolonged viral detection. The mean duration of viral detection was 25.6 days. Viral loads were significantly lower in patients with viral rebounds than in those with no rebound (E, P=0.003; RdRp, P=0.01; N, P=0.02). Conclusion: Our study showed that many pediatric patients with coronavirus disease 2019 (COVID-19) experienced viral rebound and showed viral detection for more than 3 weeks. Further studies are needed to investigate the relationship between viral rebound and infectiousness in COVID-19.

• Pediatric Infection and Vaccine
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• 제21권1호
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• pp.22-28
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• 2014

목적: 임상적으로 중증 세균성 감염과 바이러스성 질환의 감별이 어려운 어린 영아에서 호흡기 바이러스를 검출하고 이것과 연관된 임상적 위험인자를 분석하였다. 방법: 2011년 9월부터 2012년 8월까지 생후 90일 이하 영아 중 패혈증을 포함한 감염성 질환이 의심된 227명을 대상으로 비인두 검체를 채취하였으며 임상적 특성에 대한 후향적 연구를 시행하였다. 채취한 검체 내 호흡기 바이러스의 검출은 real-time PCR 검사를 통해 측정되었다. 결과: 총 157명(69.2%)의 영아에서 한 가지 이상의 호흡기 바이러스가 검출되었다. 빈도는 RSV (75명), RV (42명), influenza virus (18명), parainfluenza virus (15명), human metapneumovirus (9명), coronavirus (9명), adenovirus (4명), bocavirus (3명) 순이었다. 이 중 24명(10.6%)에서 세균성 감염을 진단하였다. 기침, 호흡기 질환의 가족력이 있는 경우 혹은 가을/겨울 철에 호흡기 바이러스가 의미있게 높은 빈도로 검출되었으며 로지스틱 회귀분석에서도 같은 경향을 확인하였다. 가을과 겨울에는 세균성 감염 환자보다 그렇지 않은 환자에서 호흡기 바이러스 검출이 유의하게 많은 것을 알 수 있었다. 결론: 호흡기 바이러스는 감염성 질환이 의심되어 입원한 어린 영아의 중요한 병원체이며 그 검출률은 호흡기 증상, 가을/겨울철 발생, 호흡기 증상의 가족력이 있는 환자에서 유의하게 높았다.