IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 19, NO. 1, JANUARY 2015
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Guest-Editorial Biomedical Informatics in Clinical Environments HE aim of this special section is to provide an overview of the emerging biomedical informatics technologies and their application in research and clinical environments. Recent developments in biomedical informatics have created methods, techniques and tools, which are based on the analysis of heterogeneous data, data mining, decision support systems, multiscale modeling, etc. The distance from the development of such systems and the real clinical environments is still long enough, and only some of them have been used in a clinical scale. Physiological signals, such as Electrocardiograms and Electroencephalograms, are used for the diagnosis of the patient status and detection of events. In [1], an unsupervised, robust, and computationally fast algorithm that uses Modified Multiscale Sample Entropy and Kurtosis to automatically identify the independent eye blink components is presented. Such signal processing approaches can be used in diagnostic equipment to assist the medical doctors and nurses to detect events and save time, since the process can be made fully automated. A method for smart auscultation employing a novel blind recovery of the original cardiac and respiratory sounds from a single observation mixture, which has been tested in noisy multiple clinical environments is presented in [2]. The outcome of such processing systems can be used in decision making systems. Decision support systems are employed to process physiological signals, imaging, and genetic data, as well as environmental information to create new knowledge and make the doctor’s decisions easier, more accurate, and faster. Research has been performed in the diagnosis of breast cancer from mammographic images. In [3], the diagnostic information that the relative location of the cluster inside the breast may provide is investigated and the obtained results indicate that an enhancement in the diagnostic process improves both the sensitivity and the specificity of the diagnostic process. Decision support systems are also employed for the continuous assessment of health indicators for elderly people living on their own, to assist in the prolongation of their independence and improvement of their quality of life. In that case such systems are used outside the clinical environment. In [4], this approach is demonstrated in geriatric depression cases. In the framework of integration of the existing information, feedback of consumers, especially for the pharmaceutical industries and the medical personnel on a treatment or adverse events, can be extracted from social media using data mining techniques [5]. Research and clinical practice can be greatly benefited from the identification of prominent biomarkers and identification of novel aspects of a disease. This now can be done in a safe way employing protein interaction networks as it has been done for hepatitis [6]. This is an alternative way to diagnose a disease
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Digital Object Identifier 10.1109/JBHI.2014.2382771
due to the initial lack of symptoms. In the case of cutaneous melanoma, both gene expression profiling and imaging data have been utilized for the identification of genetic biomarkers and the selection of certain imaging features, which trigger the evolution of the disease [7]. Biomarkers are crucial not only for the early diagnosis of a disease but also for the surgical procedures in active clinical environments. A good example is described in [8], where the weighting of beta-band frequency peaks from simultaneous microelectrode recordings can serve as a biomarker and lead to the best stimulation contacts during and after surgery in deep brain stimulation for patients suffering from Parkinson’s disease. It is well understood that biomedical informatics tools have been developed to translate the abundance of information into useful knowledge, which can be integrated with biomedical knowledge and provide tools and systems useful for diagnosis, prognosis, and treatment in clinical and research environments. This will result in more efficient, accurate, and cost effective clinical practices. M. AKAY Department of Biomedical Engineering University of Houston Houston, TX 77004 USA [email protected] D. I. FOTIADIS Unit of Medical Technology and Intelligent Information Systems University of Ioannina and IMBB-FORTH Ioannina 45110, Greece [email protected] K. S. NIKITA School of Electrical and Computer Engineering National Technical University of Athens Athens 106 82, Greece [email protected] R. W. WILLIAMS Department of Anatomy and Neurobiology University of Tennessee Science Center TN 38163, USA [email protected] REFERENCES [1] R. Mahajan and B. I. Morshed, “Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis and waveletICA,” J. Biomed. Health Inform., to be published. [2] G. Shah, P. Koch and C. B. Papadias, “On the blind recovery of cardiac and respiratory sounds,” J. Biomed. Health Inform., to be published.
2168-2194 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 19, NO. 1, JANUARY 2015
[3] I. I. Andreadis, G. M. Spyrou, and K. S. Nikita, “A CADx scheme for mammography empowered with topological information from clustered microcalcifications’ atlases,” J. Biomed. Health Inform., to be published. [4] A. S. Billis, E. I. Papageorgiou, C. A. Frantzidis, M. S. Tsatali, A. C. Tsolaki and P. D. Bamidis, “A decision-support framework for promoting independent living and ageing well,” J. Biomed. Health Inform., to be published. [5] A. Akay, A. Dragomir, and B.-E. Erlandsson, “Network-based modeling and intelligent data mining of social media for improving care,” J. Biomed. Health Inform., to be published.
[6] T. Simos, U. Georgopoulou, G. Thyphronitis, J. Koskinas, and C. Papaloukas, “Analysis of protein interaction networks for the detection of candidate Hepatitis B and C biomarkers,” J. Biomed. Health Inform., to be published. [7] I. Valavanis, I. Maglogiannis, and A. A. Chatziioannou, “Exploring robust diagnostic signatures for cutaneous melanoma utilizing genetic and imaging data,” J. Biomed. Health Inform., to be published. [8] K. P. Michmizos, P. Frangou, P. Stathis, D. Sakas, and K. S. Nikita, “Beta band frequency peaks inside the subthalamic nucleus as a biomarker for motor improvement after deep brain stimulation in Parkinson’s disease,” J. Biomed. Health Inform., to be published.
149
Guest-Editorial Biomedical Informatics in Clinical Environments HE aim of this special section is to provide an overview of the emerging biomedical informatics technologies and their application in research and clinical environments. Recent developments in biomedical informatics have created methods, techniques and tools, which are based on the analysis of heterogeneous data, data mining, decision support systems, multiscale modeling, etc. The distance from the development of such systems and the real clinical environments is still long enough, and only some of them have been used in a clinical scale. Physiological signals, such as Electrocardiograms and Electroencephalograms, are used for the diagnosis of the patient status and detection of events. In [1], an unsupervised, robust, and computationally fast algorithm that uses Modified Multiscale Sample Entropy and Kurtosis to automatically identify the independent eye blink components is presented. Such signal processing approaches can be used in diagnostic equipment to assist the medical doctors and nurses to detect events and save time, since the process can be made fully automated. A method for smart auscultation employing a novel blind recovery of the original cardiac and respiratory sounds from a single observation mixture, which has been tested in noisy multiple clinical environments is presented in [2]. The outcome of such processing systems can be used in decision making systems. Decision support systems are employed to process physiological signals, imaging, and genetic data, as well as environmental information to create new knowledge and make the doctor’s decisions easier, more accurate, and faster. Research has been performed in the diagnosis of breast cancer from mammographic images. In [3], the diagnostic information that the relative location of the cluster inside the breast may provide is investigated and the obtained results indicate that an enhancement in the diagnostic process improves both the sensitivity and the specificity of the diagnostic process. Decision support systems are also employed for the continuous assessment of health indicators for elderly people living on their own, to assist in the prolongation of their independence and improvement of their quality of life. In that case such systems are used outside the clinical environment. In [4], this approach is demonstrated in geriatric depression cases. In the framework of integration of the existing information, feedback of consumers, especially for the pharmaceutical industries and the medical personnel on a treatment or adverse events, can be extracted from social media using data mining techniques [5]. Research and clinical practice can be greatly benefited from the identification of prominent biomarkers and identification of novel aspects of a disease. This now can be done in a safe way employing protein interaction networks as it has been done for hepatitis [6]. This is an alternative way to diagnose a disease
T
Digital Object Identifier 10.1109/JBHI.2014.2382771
due to the initial lack of symptoms. In the case of cutaneous melanoma, both gene expression profiling and imaging data have been utilized for the identification of genetic biomarkers and the selection of certain imaging features, which trigger the evolution of the disease [7]. Biomarkers are crucial not only for the early diagnosis of a disease but also for the surgical procedures in active clinical environments. A good example is described in [8], where the weighting of beta-band frequency peaks from simultaneous microelectrode recordings can serve as a biomarker and lead to the best stimulation contacts during and after surgery in deep brain stimulation for patients suffering from Parkinson’s disease. It is well understood that biomedical informatics tools have been developed to translate the abundance of information into useful knowledge, which can be integrated with biomedical knowledge and provide tools and systems useful for diagnosis, prognosis, and treatment in clinical and research environments. This will result in more efficient, accurate, and cost effective clinical practices. M. AKAY Department of Biomedical Engineering University of Houston Houston, TX 77004 USA [email protected] D. I. FOTIADIS Unit of Medical Technology and Intelligent Information Systems University of Ioannina and IMBB-FORTH Ioannina 45110, Greece [email protected] K. S. NIKITA School of Electrical and Computer Engineering National Technical University of Athens Athens 106 82, Greece [email protected] R. W. WILLIAMS Department of Anatomy and Neurobiology University of Tennessee Science Center TN 38163, USA [email protected] REFERENCES [1] R. Mahajan and B. I. Morshed, “Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis and waveletICA,” J. Biomed. Health Inform., to be published. [2] G. Shah, P. Koch and C. B. Papadias, “On the blind recovery of cardiac and respiratory sounds,” J. Biomed. Health Inform., to be published.
2168-2194 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
150
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 19, NO. 1, JANUARY 2015
[3] I. I. Andreadis, G. M. Spyrou, and K. S. Nikita, “A CADx scheme for mammography empowered with topological information from clustered microcalcifications’ atlases,” J. Biomed. Health Inform., to be published. [4] A. S. Billis, E. I. Papageorgiou, C. A. Frantzidis, M. S. Tsatali, A. C. Tsolaki and P. D. Bamidis, “A decision-support framework for promoting independent living and ageing well,” J. Biomed. Health Inform., to be published. [5] A. Akay, A. Dragomir, and B.-E. Erlandsson, “Network-based modeling and intelligent data mining of social media for improving care,” J. Biomed. Health Inform., to be published.
[6] T. Simos, U. Georgopoulou, G. Thyphronitis, J. Koskinas, and C. Papaloukas, “Analysis of protein interaction networks for the detection of candidate Hepatitis B and C biomarkers,” J. Biomed. Health Inform., to be published. [7] I. Valavanis, I. Maglogiannis, and A. A. Chatziioannou, “Exploring robust diagnostic signatures for cutaneous melanoma utilizing genetic and imaging data,” J. Biomed. Health Inform., to be published. [8] K. P. Michmizos, P. Frangou, P. Stathis, D. Sakas, and K. S. Nikita, “Beta band frequency peaks inside the subthalamic nucleus as a biomarker for motor improvement after deep brain stimulation in Parkinson’s disease,” J. Biomed. Health Inform., to be published.
