{"title":"利用信息融合技术远程光学传感心音以进行生物识别","authors":"Haobo Li;Marija Vaskeviciute;Ilya Starshynov;Daniele Faccio","doi":"10.1109/TIM.2024.3457967","DOIUrl":null,"url":null,"abstract":"Machine learning (ML) has extensive applications in healthcare for the classification of vital and health signals from raw data. This method both improves the existing measurement techniques and provides opportunities for developing new approaches. In this work, we present two different information fusion methods for classifying the heart sound signals recorded by a new-generation optical stethoscope. The optical stethoscope consists of a green laser diode and a high-speed industrial camera; the laser is pointed to the human neck and chest area and the camera receives the reflected laser speckles. The proposed fusion schemes then combine the output of VGG-19 and Bi-LSTM neural networks at the feature and decision levels and fuse the data from different measurement points on the body. For the decision-level fusion, a weighted sum function is utilized to merge the information of two networks, whereas, for the feature level, a fully connected layer block is implemented to cascade the output vectors from the last layer of VGG-19 and Bi-LSTM. Both methods show the subsequent improvement of 10% or more compared to the single network thanks to the difference in extracted information between convolution and recurrent neural networks. When training and testing the proposed fusion methods on the combination of neck and chest data, the mitral and tricuspid valve auscultation positions demonstrate better average classification performance than the aortic and pulmonary valve positions, with validation via ten iterations of training and testing of the ML models.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remote Optical Sensing of Heart Sounds for Biometric Identification Using Information Fusion\",\"authors\":\"Haobo Li;Marija Vaskeviciute;Ilya Starshynov;Daniele Faccio\",\"doi\":\"10.1109/TIM.2024.3457967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning (ML) has extensive applications in healthcare for the classification of vital and health signals from raw data. This method both improves the existing measurement techniques and provides opportunities for developing new approaches. In this work, we present two different information fusion methods for classifying the heart sound signals recorded by a new-generation optical stethoscope. The optical stethoscope consists of a green laser diode and a high-speed industrial camera; the laser is pointed to the human neck and chest area and the camera receives the reflected laser speckles. The proposed fusion schemes then combine the output of VGG-19 and Bi-LSTM neural networks at the feature and decision levels and fuse the data from different measurement points on the body. For the decision-level fusion, a weighted sum function is utilized to merge the information of two networks, whereas, for the feature level, a fully connected layer block is implemented to cascade the output vectors from the last layer of VGG-19 and Bi-LSTM. Both methods show the subsequent improvement of 10% or more compared to the single network thanks to the difference in extracted information between convolution and recurrent neural networks. When training and testing the proposed fusion methods on the combination of neck and chest data, the mitral and tricuspid valve auscultation positions demonstrate better average classification performance than the aortic and pulmonary valve positions, with validation via ten iterations of training and testing of the ML models.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10684282/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10684282/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Remote Optical Sensing of Heart Sounds for Biometric Identification Using Information Fusion
Machine learning (ML) has extensive applications in healthcare for the classification of vital and health signals from raw data. This method both improves the existing measurement techniques and provides opportunities for developing new approaches. In this work, we present two different information fusion methods for classifying the heart sound signals recorded by a new-generation optical stethoscope. The optical stethoscope consists of a green laser diode and a high-speed industrial camera; the laser is pointed to the human neck and chest area and the camera receives the reflected laser speckles. The proposed fusion schemes then combine the output of VGG-19 and Bi-LSTM neural networks at the feature and decision levels and fuse the data from different measurement points on the body. For the decision-level fusion, a weighted sum function is utilized to merge the information of two networks, whereas, for the feature level, a fully connected layer block is implemented to cascade the output vectors from the last layer of VGG-19 and Bi-LSTM. Both methods show the subsequent improvement of 10% or more compared to the single network thanks to the difference in extracted information between convolution and recurrent neural networks. When training and testing the proposed fusion methods on the combination of neck and chest data, the mitral and tricuspid valve auscultation positions demonstrate better average classification performance than the aortic and pulmonary valve positions, with validation via ten iterations of training and testing of the ML models.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.