Pub Date : 2018-11-23DOI: 10.5772/INTECHOPEN.82209
Mariusz Marzec
measuring the heart rate and respiration using a visible light camera and a thermal imaging camera. The basic principles and assumptions that enable to use this type of techniques to assess the health of patients with a suspected infectious disease are discussed here. The research was carried out in a group of 10 students; the cameras were located approximately 50 cm from the subjects’ faces. The observations were carried out at rest and after exercises for a period of time equal to 30 seconds. The examination involved simultaneous reading of parameters of breath and electrocardiogram sensors (as reference data) and recording images from visible light and thermal imaging cameras. During respira -tion, the temperature in the facial area changed, and due to heart beating, the luminance in the facial area also changed. These changes were recorded as a series of images, from which the values representing the current state of the subject in quantitative form were extracted. As a result of the research, it was established that there was a relationship between signals received from the cameras and signals registered by breath and pulse sensors. The obtained results of identification of affected patients (in the study group) indicated the high potential of the proposed solution. According to the authors, the presented solution can be used to prepare an infectious disease screening system. The prediction of positive cases was 100%.
{"title":"Introductory Chapter: Non-Invasive Diagnostic Methods in Medicine","authors":"Mariusz Marzec","doi":"10.5772/INTECHOPEN.82209","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.82209","url":null,"abstract":"measuring the heart rate and respiration using a visible light camera and a thermal imaging camera. The basic principles and assumptions that enable to use this type of techniques to assess the health of patients with a suspected infectious disease are discussed here. The research was carried out in a group of 10 students; the cameras were located approximately 50 cm from the subjects’ faces. The observations were carried out at rest and after exercises for a period of time equal to 30 seconds. The examination involved simultaneous reading of parameters of breath and electrocardiogram sensors (as reference data) and recording images from visible light and thermal imaging cameras. During respira -tion, the temperature in the facial area changed, and due to heart beating, the luminance in the facial area also changed. These changes were recorded as a series of images, from which the values representing the current state of the subject in quantitative form were extracted. As a result of the research, it was established that there was a relationship between signals received from the cameras and signals registered by breath and pulse sensors. The obtained results of identification of affected patients (in the study group) indicated the high potential of the proposed solution. According to the authors, the presented solution can be used to prepare an infectious disease screening system. The prediction of positive cases was 100%.","PeriodicalId":363789,"journal":{"name":"Non-Invasive Diagnostic Methods - Image Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125572714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.80652
G. Sun, Toshiaki Negishi, T. Kirimoto, T. Matsui, Shigeto Abe
In recent years, much attention is being paid to research and development of tech - nology that provides noncontact measurement of vital signs, i.e., heart rate, respi - ration, and body temperature, which are important for understanding the state of a person’s health. As technology for sensing biological information has progressed, new biological measurement sensors have been developed successively. There have also been reports regarding methods for measuring respiration or heart rate using pressure sensors, microwave radar, air mattresses, or high-polymer piezoelectric film. The methods have wide-ranging applications, including systems for monitoring of elderly people, identification of sleep apnea, detection of patients suspected to have an infectious disease, and noncontact measurement of stress levels. In this chapter, the principles behind noncontact measurement of respiration and heartbeat using infrared/RGB facial-image analysis are discussed, along with the applications for such measurement in the detection of patients suspected to be suffering from infectious diseases.
{"title":"Noncontact Monitoring of Vital Signs with RGB and Infrared Camera and Its Application to Screening of Potential Infection","authors":"G. Sun, Toshiaki Negishi, T. Kirimoto, T. Matsui, Shigeto Abe","doi":"10.5772/INTECHOPEN.80652","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.80652","url":null,"abstract":"In recent years, much attention is being paid to research and development of tech - nology that provides noncontact measurement of vital signs, i.e., heart rate, respi - ration, and body temperature, which are important for understanding the state of a person’s health. As technology for sensing biological information has progressed, new biological measurement sensors have been developed successively. There have also been reports regarding methods for measuring respiration or heart rate using pressure sensors, microwave radar, air mattresses, or high-polymer piezoelectric film. The methods have wide-ranging applications, including systems for monitoring of elderly people, identification of sleep apnea, detection of patients suspected to have an infectious disease, and noncontact measurement of stress levels. In this chapter, the principles behind noncontact measurement of respiration and heartbeat using infrared/RGB facial-image analysis are discussed, along with the applications for such measurement in the detection of patients suspected to be suffering from infectious diseases.","PeriodicalId":363789,"journal":{"name":"Non-Invasive Diagnostic Methods - Image Processing","volume":"136 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120979279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.80592
S. Alam, Syoji Kobashi
This chapter introduces a spatiotemporal statistical shape model (stSSM) using brain MR image which will represent not only the statistical variability of shape but also a temporal change of the statistical variance with time. The proposed method applies expectation- maximization (EM)-based weighted principal component analysis (WPCA) using a temporal weight function, where E-step estimates Eigenvalues of every data using temporal Eigenvectors, and M-step updates Eigenvectors to maximize the variance. The method constructs stSSM whose Eigenvectors change with time. By assigning a predefined weight parameter for each subject according to subjects’ age, it calculates the weighted variance for time-specific stSSM. To validate the method, this study employed 105 adult subjects (age: 30–84 years old with mean ± SD = 60.61 ± 16.97) from OASIS database. stSSM constructed for time point 40–80 with a step of 2. The proposed method allows the characterization of typical deformation patterns and subject-specific shape changes in repeated time-series observations of several subjects where the modeling performance was observed by optimizing variance.
{"title":"Spatiotemporal Statistical Shape Model Construction for the Observation of Temporal Change in Human Brain Shape","authors":"S. Alam, Syoji Kobashi","doi":"10.5772/INTECHOPEN.80592","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.80592","url":null,"abstract":"This chapter introduces a spatiotemporal statistical shape model (stSSM) using brain MR image which will represent not only the statistical variability of shape but also a temporal change of the statistical variance with time. The proposed method applies expectation- maximization (EM)-based weighted principal component analysis (WPCA) using a temporal weight function, where E-step estimates Eigenvalues of every data using temporal Eigenvectors, and M-step updates Eigenvectors to maximize the variance. The method constructs stSSM whose Eigenvectors change with time. By assigning a predefined weight parameter for each subject according to subjects’ age, it calculates the weighted variance for time-specific stSSM. To validate the method, this study employed 105 adult subjects (age: 30–84 years old with mean ± SD = 60.61 ± 16.97) from OASIS database. stSSM constructed for time point 40–80 with a step of 2. The proposed method allows the characterization of typical deformation patterns and subject-specific shape changes in repeated time-series observations of several subjects where the modeling performance was observed by optimizing variance.","PeriodicalId":363789,"journal":{"name":"Non-Invasive Diagnostic Methods - Image Processing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124637798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.80223
Igor Victorovich. Lakhno, Bertha Patricia Guzmán-Velázquez, J. A. Díaz-Méndez
Monitoring of fetal cardiac activity is a well-known approach to the assessment of fetal health. The fetal heart rate can be measured using conventional cardiotocography (CTG). However, this method does not provide the beat-to-beat variability of the fetal heart rate because of the averaging nature of the autocorrelation function that is used to estimate the heart rate from a set of heart beats enclosed in the autocorrelation function window. Therefore, CTG presents important limitations for fetal arrhythmia diagnosis. CTG has a high rate of false positives and poor interand intra-observer reliability, such that fetal status and the perinatal outcome cannot be predicted reliably. Non-invasive fetal electrocardiography (NI-FECG) is a promising low-cost and non-invasive continuous fetal monitoring alternative. However, there is little that has been published to date on the clinical usability of NI-FECG. The chapter will include data on the accurate diagnosing of fetal distress based on heart rate variability (HRV). A fuzzy logic inference system was designed based on a set of fetal descriptors selected from the HRV responses, as evident descriptors of fetal well-being, to increase the sensitivity and specificity of detection. This approach is found to be rather prospective for the subsequent clinical implementation.
{"title":"Fuzzy Detection of Fetal Distress for Antenatal Monitoring in Pregnancy with Fetal Growth Restriction and Normal","authors":"Igor Victorovich. Lakhno, Bertha Patricia Guzmán-Velázquez, J. A. Díaz-Méndez","doi":"10.5772/INTECHOPEN.80223","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.80223","url":null,"abstract":"Monitoring of fetal cardiac activity is a well-known approach to the assessment of fetal health. The fetal heart rate can be measured using conventional cardiotocography (CTG). However, this method does not provide the beat-to-beat variability of the fetal heart rate because of the averaging nature of the autocorrelation function that is used to estimate the heart rate from a set of heart beats enclosed in the autocorrelation function window. Therefore, CTG presents important limitations for fetal arrhythmia diagnosis. CTG has a high rate of false positives and poor interand intra-observer reliability, such that fetal status and the perinatal outcome cannot be predicted reliably. Non-invasive fetal electrocardiography (NI-FECG) is a promising low-cost and non-invasive continuous fetal monitoring alternative. However, there is little that has been published to date on the clinical usability of NI-FECG. The chapter will include data on the accurate diagnosing of fetal distress based on heart rate variability (HRV). A fuzzy logic inference system was designed based on a set of fetal descriptors selected from the HRV responses, as evident descriptors of fetal well-being, to increase the sensitivity and specificity of detection. This approach is found to be rather prospective for the subsequent clinical implementation.","PeriodicalId":363789,"journal":{"name":"Non-Invasive Diagnostic Methods - Image Processing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127023684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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