Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011370
M. N. Kurnaz, T. Ölmez
A method is presented to determine features of heart sounds. A wavelet transform is applied to a window of two periods of heart sounds. Two analyses are realized for the signals in the window: segmentation of the first and second heart sounds, and extraction of the features. After the segmentation, feature vectors are formed by using the wavelet detail coefficients at the sixth decomposition level. The best feature elements are analyzed by using dynamic programming.
{"title":"Determination of features for heart sounds by using wavelet transforms","authors":"M. N. Kurnaz, T. Ölmez","doi":"10.1109/CBMS.2002.1011370","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011370","url":null,"abstract":"A method is presented to determine features of heart sounds. A wavelet transform is applied to a window of two periods of heart sounds. Two analyses are realized for the signals in the window: segmentation of the first and second heart sounds, and extraction of the features. After the segmentation, feature vectors are formed by using the wavelet detail coefficients at the sixth decomposition level. The best feature elements are analyzed by using dynamic programming.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716014","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011391
Kudakwashe Dube, Bing Wu, J. Grimson
Computer-based support for the incorporation of clinical practice guidelines and protocols into daily practice has recently attracted a lot of research interest within the healthcare informatics area. The aim is not only to provide support for the flexible specification and execution of clinical guidelines or protocols but also the dynamic management of these guidelines or protocols. This paper presents a framework and architecture for the management of clinical protocols whose specification and execution models are based on the event-condition-action (ECA) rule paradigm.
{"title":"Framework and architecture for the management of event-condition-action (ECA) rule-based clinical protocols","authors":"Kudakwashe Dube, Bing Wu, J. Grimson","doi":"10.1109/CBMS.2002.1011391","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011391","url":null,"abstract":"Computer-based support for the incorporation of clinical practice guidelines and protocols into daily practice has recently attracted a lot of research interest within the healthcare informatics area. The aim is not only to provide support for the flexible specification and execution of clinical guidelines or protocols but also the dynamic management of these guidelines or protocols. This paper presents a framework and architecture for the management of clinical protocols whose specification and execution models are based on the event-condition-action (ECA) rule paradigm.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"25 7-8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114040797","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011383
Matjaz Bevk, I. Kononenko
The article deals with the problem of texture description. It presents a statistical approach. Specifically, it introduces the use of first- and second-order statistics on texture color spaces. At the end of the article, we also give estimations of the computational time complexities of the parameter calculations presented in this article and describe our experience on one application domain. This study is a preliminary preparation for the application of these methods to medical images.
{"title":"A statistical approach to texture description of medical images: a preliminary study","authors":"Matjaz Bevk, I. Kononenko","doi":"10.1109/CBMS.2002.1011383","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011383","url":null,"abstract":"The article deals with the problem of texture description. It presents a statistical approach. Specifically, it introduces the use of first- and second-order statistics on texture color spaces. At the end of the article, we also give estimations of the computational time complexities of the parameter calculations presented in this article and describe our experience on one application domain. This study is a preliminary preparation for the application of these methods to medical images.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131912820","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011346
L. Ngalamou, Harold Campbell
Presents a system called the "Information Appliance for Diabetic Patients" (IADP), which is designed for the monitoring of diabetic patients. The system is an embedded microcomputer that can be used for self- or remote monitoring. IADP is able to store a "patient profile" (PP). Each PP consists of at least the patient's name, age, sex, address, weight, occupation, known allergies, medical history and the nearest hospital's telephone number and the doctor's phone and page numbers. In addition, each profile has a database of the patient's blood sugar, blood pressure, body mass index, periodical urine analysis (protein, glucose, bodies), diet, drug intakes (including dosage quantity and time taken) and exercise periods. IADP also creates schedules for a patient, notifying him as to when his medication should be taken; the required amount is based on factors such as age, weight and daily activities. IADP has the ability to communicate with the external world for remote monitoring using a modem link.
{"title":"Diabetic information appliance","authors":"L. Ngalamou, Harold Campbell","doi":"10.1109/CBMS.2002.1011346","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011346","url":null,"abstract":"Presents a system called the \"Information Appliance for Diabetic Patients\" (IADP), which is designed for the monitoring of diabetic patients. The system is an embedded microcomputer that can be used for self- or remote monitoring. IADP is able to store a \"patient profile\" (PP). Each PP consists of at least the patient's name, age, sex, address, weight, occupation, known allergies, medical history and the nearest hospital's telephone number and the doctor's phone and page numbers. In addition, each profile has a database of the patient's blood sugar, blood pressure, body mass index, periodical urine analysis (protein, glucose, bodies), diet, drug intakes (including dosage quantity and time taken) and exercise periods. IADP also creates schedules for a patient, notifying him as to when his medication should be taken; the required amount is based on factors such as age, weight and daily activities. IADP has the ability to communicate with the external world for remote monitoring using a modem link.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132038845","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011376
C. Dafonte, Á. Gómez, Bernardino Arcay Varela, A. Martínez, J. Pereira
Advances in telemedicine technology have led to intelligent monitoring systems that are capable of helping medical experts in their decision-making process. These systems imply the introduction of a bed-side computer that supervises the patient and collects, stores and visualizes all the information provided by the medical devices in an accessible way. This paper describes the telemedicine system that is currently being developed by our research team for the intelligent telemonitoring of patients at an intensive care unit (ICU). Concretely, the paper focuses on our 3D visualization module, which shows a virtual model of the patient and allows the clinical staff to visualize the patient's evolution in a rapid and clear manner.
{"title":"3D visualization module in a telemedicine project","authors":"C. Dafonte, Á. Gómez, Bernardino Arcay Varela, A. Martínez, J. Pereira","doi":"10.1109/CBMS.2002.1011376","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011376","url":null,"abstract":"Advances in telemedicine technology have led to intelligent monitoring systems that are capable of helping medical experts in their decision-making process. These systems imply the introduction of a bed-side computer that supervises the patient and collects, stores and visualizes all the information provided by the medical devices in an accessible way. This paper describes the telemedicine system that is currently being developed by our research team for the intelligent telemonitoring of patients at an intensive care unit (ICU). Concretely, the paper focuses on our 3D visualization module, which shows a virtual model of the patient and allows the clinical staff to visualize the patient's evolution in a rapid and clear manner.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589987","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011387
B. Potočnik, B. Cigale, D. Zazula
The paper deals with the problem of processing and interpretation of clinically recorded ultrasound images for the reason of following the growth of dominant ovarian follicles in a day-to-day manner. A part of the XUltra project achievements is presented. We propose three different automatic computer-based follicle identification algorithms. The first one is based on cellular neural networks. The second one is based on region growing segmentation method, while the third one processes entire image sequence with a predictor-corrector recognition scheme. The recognition rate of follicles with these algorithms goes up to 78%, while the misidentification rate is around 15%.
{"title":"The XUltra project-automated analysis of ovarian ultrasound images","authors":"B. Potočnik, B. Cigale, D. Zazula","doi":"10.1109/CBMS.2002.1011387","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011387","url":null,"abstract":"The paper deals with the problem of processing and interpretation of clinically recorded ultrasound images for the reason of following the growth of dominant ovarian follicles in a day-to-day manner. A part of the XUltra project achievements is presented. We propose three different automatic computer-based follicle identification algorithms. The first one is based on cellular neural networks. The second one is based on region growing segmentation method, while the third one processes entire image sequence with a predictor-corrector recognition scheme. The recognition rate of follicles with these algorithms goes up to 78%, while the misidentification rate is around 15%.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"13 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126105199","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011365
S. Abidi, K. Hoe
The application of data mining techniques to medical data is certainly beneficial for researchers interested in discerning the complexity of healthcare processes in real-life operational situations. We present a methodology, together with its computational implementation, for the automated extraction of data-defining CNF symbolic rules from medical data-sets comprising both annotated and un-annotated attributes. We propose a hybrid approach for symbolic rule extraction which features a sequence of methods including data clustering, data discretization and eventually symbolic rule discovery via rough set approximation. We present a generic data mining workbench that can generate cluster/class-defining symbolic rules from medical data, such that the resultant symbolic rules are directly applicable to medical rule-based expert systems.
{"title":"Symbolic exposition of medical data-sets: a data mining workbench to inductively derive data-defining symbolic rules","authors":"S. Abidi, K. Hoe","doi":"10.1109/CBMS.2002.1011365","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011365","url":null,"abstract":"The application of data mining techniques to medical data is certainly beneficial for researchers interested in discerning the complexity of healthcare processes in real-life operational situations. We present a methodology, together with its computational implementation, for the automated extraction of data-defining CNF symbolic rules from medical data-sets comprising both annotated and un-annotated attributes. We propose a hybrid approach for symbolic rule extraction which features a sequence of methods including data clustering, data discretization and eventually symbolic rule discovery via rough set approximation. We present a generic data mining workbench that can generate cluster/class-defining symbolic rules from medical data, such that the resultant symbolic rules are directly applicable to medical rule-based expert systems.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129915973","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011393
Myrian R. B. Araujo, C. Traina, A. Traina, J. M. Bueno, H. Razente
This paper shows how to support images in a relational database, so it can fulfill the requirements to be used as the storage mechanism of a PACS. This support includes the ability to answer similarity queries based on the image content, providing fast image retrieval based on indexing structures. The main concept allowing this support is the definition of distance functions based on features, which are extracted from the images as they are stored in the database. An extension to SQL enables the construction of an interpreter that intercepts the extended commands and translates them into standard SQL, allowing one to take advantage of any relational database server. We describe experiments made with a prototype implemented using these concepts, which allowed answering queries up to 20 times faster than using existing relational servers alone.
{"title":"Extending relational databases to support content-based retrieval of medical images","authors":"Myrian R. B. Araujo, C. Traina, A. Traina, J. M. Bueno, H. Razente","doi":"10.1109/CBMS.2002.1011393","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011393","url":null,"abstract":"This paper shows how to support images in a relational database, so it can fulfill the requirements to be used as the storage mechanism of a PACS. This support includes the ability to answer similarity queries based on the image content, providing fast image retrieval based on indexing structures. The main concept allowing this support is the definition of distance functions based on features, which are extracted from the images as they are stored in the database. An extension to SQL enables the construction of an interpreter that intercepts the extended commands and translates them into standard SQL, allowing one to take advantage of any relational database server. We describe experiments made with a prototype implemented using these concepts, which allowed answering queries up to 20 times faster than using existing relational servers alone.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124262604","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011410
Ciril Groselj
The principle of any retrospective on patient data-based investigation is searching the patients by problem or sign, but not by name. With a proper problem-encoded archival database, the data mining process would be easy. One would only need to input the request and obtain the proper data in a short time. Medical archives are frequently based on paper records only, with the patient name as the entry key. To find the proper record in such an archive, a detection strategy is needed. The process continues with collecting the usually enormous amount of papers, finding the appropriate records within them, and finally encoding and arranging them in a table. The whole process can be separated into patients, paper and data mining. Because of their slowness, these phases can be the most time-consuming part of a medical data-based investigation. The author describes his data mining experience.
{"title":"Data mining problems in medicine","authors":"Ciril Groselj","doi":"10.1109/CBMS.2002.1011410","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011410","url":null,"abstract":"The principle of any retrospective on patient data-based investigation is searching the patients by problem or sign, but not by name. With a proper problem-encoded archival database, the data mining process would be easy. One would only need to input the request and obtain the proper data in a short time. Medical archives are frequently based on paper records only, with the patient name as the entry key. To find the proper record in such an archive, a detection strategy is needed. The process continues with collecting the usually enormous amount of papers, finding the appropriate records within them, and finally encoding and arranging them in a table. The whole process can be separated into patients, paper and data mining. Because of their slowness, these phases can be the most time-consuming part of a medical data-based investigation. The author describes his data mining experience.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125259831","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011356
Tsutomu Matsumoto, Yuki Ueda, S. Kawaji
The importance of developing a support system for medical decision-making in the clinical field has been pointed out for improving the accuracy and objectivity of the judgment of clinicians, and for early detection of disease. In this paper, we notice that signs, symptoms and clinical laboratory data are essential information which show the functional depression and failure of the ecosystem, and a practical software system is presented that provides physicians with clues to clinical diagnosis. First, by analysing the medical task and the structuralization of patient information, a medical modelling method is provided, where diagnosis can be considered as the identification of a patient who corresponds to the controlled object. Secondly, by using the concept of a patient model and a disease model, a system identification algorithm is proposed, and an actual system constructed for medical diagnosis is described in order to confirm the usefulness of the proposed method.
{"title":"A software system for giving clues of medical diagnosis to clinician","authors":"Tsutomu Matsumoto, Yuki Ueda, S. Kawaji","doi":"10.1109/CBMS.2002.1011356","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011356","url":null,"abstract":"The importance of developing a support system for medical decision-making in the clinical field has been pointed out for improving the accuracy and objectivity of the judgment of clinicians, and for early detection of disease. In this paper, we notice that signs, symptoms and clinical laboratory data are essential information which show the functional depression and failure of the ecosystem, and a practical software system is presented that provides physicians with clues to clinical diagnosis. First, by analysing the medical task and the structuralization of patient information, a medical modelling method is provided, where diagnosis can be considered as the identification of a patient who corresponds to the controlled object. Secondly, by using the concept of a patient model and a disease model, a system identification algorithm is proposed, and an actual system constructed for medical diagnosis is described in order to confirm the usefulness of the proposed method.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114591146","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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