Shoya Makihira, Naoto Murakami, Tsunahiko Hirano, K. Doi, K. Matsunaga, S. Nishifuji, Shota Nakashima
{"title":"利用生物声传感器区分呼气声和吸气声的方法","authors":"Shoya Makihira, Naoto Murakami, Tsunahiko Hirano, K. Doi, K. Matsunaga, S. Nishifuji, Shota Nakashima","doi":"10.12792/icisip2021.022","DOIUrl":null,"url":null,"abstract":"The number of deaths from COPD in 2019 is about 6% of all deaths worldwide. The prevalence of COPD is expected to increase worldwide. Pulmonary function testing, so called spirometry is used in the diagnosis and severity assessment of COPD. There is a simple test method using expiratory and inspiratory time. Systems to measure expiratory and inspiratory time do not proposed. In this study, we propose a novel distinction method between expiratory and inspiratory sounds using biological sound sensors. The biological sound sensor consists of two units: holding and sensor units. The former fixes the sensor unit. The latter obtains biological sounds and adopts a polyurethane elastomer to match the acoustic impedance. The respiratory sounds are extracted by applying a bandpass filter to the biological sounds. Furthermore, Harmonic/Percussive Sound Separation is applied to the respiratory sounds to reduce the residual vascular sounds. The classifier between expiratory and inspiratory sounds is built with a soft margin Support Vector Machine. The feature is the power spectrum extracted from the spectrogram of respiratory sound. The classifier was built for each subject from the two respiration patterns. The proposed method was verified by the accuracy, precision, recall, and F-score. The obtained distinction accuracy was up to 86.8%, and it was possible to distinguish between expiratory and inspiratory sounds with high accuracy.","PeriodicalId":431446,"journal":{"name":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distinction Method between Expiratory and Inspiratory Sounds Using Biological Sound Sensor\",\"authors\":\"Shoya Makihira, Naoto Murakami, Tsunahiko Hirano, K. Doi, K. Matsunaga, S. Nishifuji, Shota Nakashima\",\"doi\":\"10.12792/icisip2021.022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The number of deaths from COPD in 2019 is about 6% of all deaths worldwide. The prevalence of COPD is expected to increase worldwide. Pulmonary function testing, so called spirometry is used in the diagnosis and severity assessment of COPD. There is a simple test method using expiratory and inspiratory time. Systems to measure expiratory and inspiratory time do not proposed. In this study, we propose a novel distinction method between expiratory and inspiratory sounds using biological sound sensors. The biological sound sensor consists of two units: holding and sensor units. The former fixes the sensor unit. The latter obtains biological sounds and adopts a polyurethane elastomer to match the acoustic impedance. The respiratory sounds are extracted by applying a bandpass filter to the biological sounds. Furthermore, Harmonic/Percussive Sound Separation is applied to the respiratory sounds to reduce the residual vascular sounds. The classifier between expiratory and inspiratory sounds is built with a soft margin Support Vector Machine. The feature is the power spectrum extracted from the spectrogram of respiratory sound. The classifier was built for each subject from the two respiration patterns. The proposed method was verified by the accuracy, precision, recall, and F-score. The obtained distinction accuracy was up to 86.8%, and it was possible to distinguish between expiratory and inspiratory sounds with high accuracy.\",\"PeriodicalId\":431446,\"journal\":{\"name\":\"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12792/icisip2021.022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12792/icisip2021.022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Distinction Method between Expiratory and Inspiratory Sounds Using Biological Sound Sensor
The number of deaths from COPD in 2019 is about 6% of all deaths worldwide. The prevalence of COPD is expected to increase worldwide. Pulmonary function testing, so called spirometry is used in the diagnosis and severity assessment of COPD. There is a simple test method using expiratory and inspiratory time. Systems to measure expiratory and inspiratory time do not proposed. In this study, we propose a novel distinction method between expiratory and inspiratory sounds using biological sound sensors. The biological sound sensor consists of two units: holding and sensor units. The former fixes the sensor unit. The latter obtains biological sounds and adopts a polyurethane elastomer to match the acoustic impedance. The respiratory sounds are extracted by applying a bandpass filter to the biological sounds. Furthermore, Harmonic/Percussive Sound Separation is applied to the respiratory sounds to reduce the residual vascular sounds. The classifier between expiratory and inspiratory sounds is built with a soft margin Support Vector Machine. The feature is the power spectrum extracted from the spectrogram of respiratory sound. The classifier was built for each subject from the two respiration patterns. The proposed method was verified by the accuracy, precision, recall, and F-score. The obtained distinction accuracy was up to 86.8%, and it was possible to distinguish between expiratory and inspiratory sounds with high accuracy.