A Mobile Cloud-Based Biofeedback Platform for Evaluating Medication Response: 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017

Y.-Z. Lai, C.-H. Tai, Y.-S. Chang, K.-H. Chung

研究成果: 會議貢獻類型論文

摘要

In recent years, biofeedback has been widely applied into diagnosis and treatment of various diseases. There are also increasingly research exploiting various ICT (Information & Communication Technology) technologies, such as cloud technology, to achieve diagnosis and treatment. Therefore, how to use mobile cloud technology to assist the disease's diagnosis, to record treatment status, and to infer the result will be an important issue. In this paper, we will propose a mobile cloud platform and framework for the patient of mental illness for evaluating medication response through a variety of biofeedback information collection, integration, and fusion, so that physician can know the patient's situation. The physiological data including Heart Rate Variability and Brain Wave are collected through wearable sensors. And the psychological data is collected through monthly mood chart. The biofeedback physiological and psychological data can be fused into together to show the medication response after patient taking some medicines. An APP for the framework has been developed to show the effectiveness. © 2017 IEEE.
原文英語
頁面183-188
頁數6
DOIs
出版狀態已發佈 - 2018

指紋

Biofeedback
Medicine
Brain
Fusion reactions
Communication

引用此文

@conference{3479e0cb51ac487988fa777741cf8836,
title = "A Mobile Cloud-Based Biofeedback Platform for Evaluating Medication Response: 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017",
abstract = "In recent years, biofeedback has been widely applied into diagnosis and treatment of various diseases. There are also increasingly research exploiting various ICT (Information & Communication Technology) technologies, such as cloud technology, to achieve diagnosis and treatment. Therefore, how to use mobile cloud technology to assist the disease's diagnosis, to record treatment status, and to infer the result will be an important issue. In this paper, we will propose a mobile cloud platform and framework for the patient of mental illness for evaluating medication response through a variety of biofeedback information collection, integration, and fusion, so that physician can know the patient's situation. The physiological data including Heart Rate Variability and Brain Wave are collected through wearable sensors. And the psychological data is collected through monthly mood chart. The biofeedback physiological and psychological data can be fused into together to show the medication response after patient taking some medicines. An APP for the framework has been developed to show the effectiveness. {\circledC} 2017 IEEE.",
keywords = "Biofeedback, Cloud computing, Mobile Cloud, Diagnosis, Physiological models, Physiology, Brain wave, Cloud technologies, Communication technologies, Heart rate variability, Information collections, Mental illness, Mobile clouds, Physiological data, Diseases",
author = "Y.-Z. Lai and C.-H. Tai and Y.-S. Chang and K.-H. Chung",
note = "Conference code: 135287 Export Date: 20 October 2018 Funding details: 105-2634-F-305 -001 Funding details: 105-2221-E-305 -010 Funding details: 106-2221-E-305-014 Funding details: MOST, Endocrine Society of the Republic of China Funding details: MOST, Ministry of Science and Technology, Taiwan Funding text: ACKNOWLEDGMENT This work was partially supported by Ministry of Science and Technology of Taiwan, Republic of China under Grant No. MOST 105-2634-F-305 -001, 105-2221-E-305 -010, and 106-2221-E-305-014. References: Rush, A.J., Trivedi, M.H., Stewart, J.W., Nierenberg, A.A., Fava, M., Kurian, B.T., Wisniewski, S.R., Combining medications to enhance depression outcomes (CO-MED): Acute and long-term outcomes of a single-blind randomized study (2011) The American Journal of Psychiatry, 168 (7), pp. 689-701; Rush, A.J., Trivedi, M.H., Ibrahim, H.M., Carmody, T.J., Arnow, B., Klein, D.N., The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDSSR): A psychometric evaluation in patients with chronic major depression (2003) Biological Psychiatry, 54, pp. 573-583; Niv, S., Clinical efficacy and potential mechanisms of neurofeedback (2013) Personality and Individual Differences, 54 (6), pp. 676-686; Brody, A.L., Barsom, M.W., Bota, R.G., Saxena, S., Prefrontal-subcortical and limbic circuit mediation of major depressive disorder (2001) Seminars in Clinical Neuropsychiatry, 6 (2), pp. 102-112; Hammond, D.C., Neurofeedback treatment of depression with the Roshi (2000) Journal of Neurotherapy, 4 (2), pp. 45-56; Blase, K.L., Van Dijke, A., Cluitmans, P.J., Vermetten, E., Efficacy of HRV biofeedback as additional treatment of depression and PTSD (2016) Tijdschr Psychiatr, 58 (4), pp. 292-300; Peng, H., Hu, B., Liu, Q., Dong, Q., Zhao, Q., Moore, P., User-centered depression prevention: An EEG approach to pervasive healthcare (2011) IEEE, Pervasive-Health, pp. 325-330; Begić, D., Popović-Knapić, V., Grubišin, J., Kosanović-Rajačić, B., Filipčić, I., Telarović, I., Jakovljević, M., (2011) Quantitative Electroencephalography in Schizophrenia and Depression, 23 (4), pp. 355-362; Thompson, T., Steffert, T., Ros, T., Leach, J., Gruzelier, J., (2008) EEG Applications for Sport and Performance. Methods, 45 (4), pp. 279-288; Coutin-Churchman, P., A{\~n}ez, Y., Uzc{\'a}tegui, M., Quantitative spectral analysis of EEG in psychiatry revisited: Drawing signs out of numbers in a clinical setting (2003) Clin Neurophysiol, 114, pp. 2294-2306; Piper, D., Schiecke, K., Leistritz, L., Pester, B., Benninger, F., Ungureanu, M., Strungaru, R., Witte, H., Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure (2014) Biomed. Eng. Tech., 59 (4), pp. 343-355; Gil, R., Virgili-Gom{\'a}, J., Garc{\'i}, R., Mason, C., Emotion ontology for collaborative modelling and learning of emotional responses (2015) Computers in Human Behavior, 51, pp. 610-617; Huo, Z.Y., Owu, S.F., Heart rate variability (2009) Taiwan Medical Journal, 52 (6), pp. 290-293; Lin, C.W., Wang, J.S., Chung, P.C., Mining physiological conditions from heart rate variability analysis (2010) IEEE Computational Intelligence Magazine, 5 (1), pp. 50-58; Holm, H., Gudbjartsson, D.F., Arnar, D.O., Several common variants modulate heart rate, PR interval and QRS duration (2010) Nature Genetics, 42 (2), pp. 117-122; Auer, R., Association of major and minor ECG abnormalities with coronary heart disease events (2012) Journal of the American Medical Association, 307 (14), pp. 1497-1505; Brown, L., Grundlehner, B., Van De-Molengraft, J., Penders, J., Gyselinckx, B., Body area network for monitoring autonomic nervous system responses (2009) Proceedings of 3rd International Conference on Pervasive Computing Technologies for Healthcare-Pervasive Health 2009 (PCTHealth '09), pp. 1-3. , April; Borromeo, S., Rodriguez-Sanchez, C., Machado, F., Hernandez-Tamames, J.A., De La-Prieta, R., A reconfigurable, wearable, wireless ECG system (2007) Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society (EMBC '07), pp. 1659-1662. , August; Hinrikus, H., Suhhova, A., Bachmann, M., Aadamsoo, K., Vohma, U., Pehlak, H., Lass, J., Spectral features of EEG in depression (2010) Biomedical Engineering, 55, pp. 155-161; Chang, Y.-S., Fan, C.-T., Lo, W.-T., Hung, W.-C., Yuan, S.-M., Mobile cloud based depression diagnosis using ontology and Bayesian network (2015) Future Generation Computer Systems, 43-44, pp. 87-98; Badawi, H.F., Dong, H., El Saddik, A., Mobile cloud-based physical activity advisory system using biofeedback sensors (2017) Future Generation Computer Systems, 66, pp. 59-70. , https://doi.org/10.1016/j.future.2015.11.005; Haux, R., Health information systems - Past, present, future (2006) International Journal of Medical Informatics, 75 (3-4), pp. 268-281; McKee, M.G., Biofeedback: An overview in the context of heart-brain medicine (2008) Cleveland Clinic Journal of Medicine, 75, pp. S31-S34; Rottenberg, J., Frank, H., Wilhelm, J.J., Gross, H., Ian, G., Respiratory sinus arrhythmia as a predictor of outcome in major depressive disorder (2002) Journal of Affective Disorders, 71, pp. 265-272",
year = "2018",
doi = "10.1109/SC2.2017.35",
language = "English",
pages = "183--188",

}

TY - CONF

T1 - A Mobile Cloud-Based Biofeedback Platform for Evaluating Medication Response

T2 - 7th IEEE International Symposium on Cloud and Service Computing, SC2 2017

AU - Lai, Y.-Z.

AU - Tai, C.-H.

AU - Chang, Y.-S.

AU - Chung, K.-H.

N1 - Conference code: 135287 Export Date: 20 October 2018 Funding details: 105-2634-F-305 -001 Funding details: 105-2221-E-305 -010 Funding details: 106-2221-E-305-014 Funding details: MOST, Endocrine Society of the Republic of China Funding details: MOST, Ministry of Science and Technology, Taiwan Funding text: ACKNOWLEDGMENT This work was partially supported by Ministry of Science and Technology of Taiwan, Republic of China under Grant No. MOST 105-2634-F-305 -001, 105-2221-E-305 -010, and 106-2221-E-305-014. References: Rush, A.J., Trivedi, M.H., Stewart, J.W., Nierenberg, A.A., Fava, M., Kurian, B.T., Wisniewski, S.R., Combining medications to enhance depression outcomes (CO-MED): Acute and long-term outcomes of a single-blind randomized study (2011) The American Journal of Psychiatry, 168 (7), pp. 689-701; Rush, A.J., Trivedi, M.H., Ibrahim, H.M., Carmody, T.J., Arnow, B., Klein, D.N., The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDSSR): A psychometric evaluation in patients with chronic major depression (2003) Biological Psychiatry, 54, pp. 573-583; Niv, S., Clinical efficacy and potential mechanisms of neurofeedback (2013) Personality and Individual Differences, 54 (6), pp. 676-686; Brody, A.L., Barsom, M.W., Bota, R.G., Saxena, S., Prefrontal-subcortical and limbic circuit mediation of major depressive disorder (2001) Seminars in Clinical Neuropsychiatry, 6 (2), pp. 102-112; Hammond, D.C., Neurofeedback treatment of depression with the Roshi (2000) Journal of Neurotherapy, 4 (2), pp. 45-56; Blase, K.L., Van Dijke, A., Cluitmans, P.J., Vermetten, E., Efficacy of HRV biofeedback as additional treatment of depression and PTSD (2016) Tijdschr Psychiatr, 58 (4), pp. 292-300; Peng, H., Hu, B., Liu, Q., Dong, Q., Zhao, Q., Moore, P., User-centered depression prevention: An EEG approach to pervasive healthcare (2011) IEEE, Pervasive-Health, pp. 325-330; Begić, D., Popović-Knapić, V., Grubišin, J., Kosanović-Rajačić, B., Filipčić, I., Telarović, I., Jakovljević, M., (2011) Quantitative Electroencephalography in Schizophrenia and Depression, 23 (4), pp. 355-362; Thompson, T., Steffert, T., Ros, T., Leach, J., Gruzelier, J., (2008) EEG Applications for Sport and Performance. Methods, 45 (4), pp. 279-288; Coutin-Churchman, P., Añez, Y., Uzcátegui, M., Quantitative spectral analysis of EEG in psychiatry revisited: Drawing signs out of numbers in a clinical setting (2003) Clin Neurophysiol, 114, pp. 2294-2306; Piper, D., Schiecke, K., Leistritz, L., Pester, B., Benninger, F., Ungureanu, M., Strungaru, R., Witte, H., Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure (2014) Biomed. Eng. Tech., 59 (4), pp. 343-355; Gil, R., Virgili-Gomá, J., Garcí, R., Mason, C., Emotion ontology for collaborative modelling and learning of emotional responses (2015) Computers in Human Behavior, 51, pp. 610-617; Huo, Z.Y., Owu, S.F., Heart rate variability (2009) Taiwan Medical Journal, 52 (6), pp. 290-293; Lin, C.W., Wang, J.S., Chung, P.C., Mining physiological conditions from heart rate variability analysis (2010) IEEE Computational Intelligence Magazine, 5 (1), pp. 50-58; Holm, H., Gudbjartsson, D.F., Arnar, D.O., Several common variants modulate heart rate, PR interval and QRS duration (2010) Nature Genetics, 42 (2), pp. 117-122; Auer, R., Association of major and minor ECG abnormalities with coronary heart disease events (2012) Journal of the American Medical Association, 307 (14), pp. 1497-1505; Brown, L., Grundlehner, B., Van De-Molengraft, J., Penders, J., Gyselinckx, B., Body area network for monitoring autonomic nervous system responses (2009) Proceedings of 3rd International Conference on Pervasive Computing Technologies for Healthcare-Pervasive Health 2009 (PCTHealth '09), pp. 1-3. , April; Borromeo, S., Rodriguez-Sanchez, C., Machado, F., Hernandez-Tamames, J.A., De La-Prieta, R., A reconfigurable, wearable, wireless ECG system (2007) Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society (EMBC '07), pp. 1659-1662. , August; Hinrikus, H., Suhhova, A., Bachmann, M., Aadamsoo, K., Vohma, U., Pehlak, H., Lass, J., Spectral features of EEG in depression (2010) Biomedical Engineering, 55, pp. 155-161; Chang, Y.-S., Fan, C.-T., Lo, W.-T., Hung, W.-C., Yuan, S.-M., Mobile cloud based depression diagnosis using ontology and Bayesian network (2015) Future Generation Computer Systems, 43-44, pp. 87-98; Badawi, H.F., Dong, H., El Saddik, A., Mobile cloud-based physical activity advisory system using biofeedback sensors (2017) Future Generation Computer Systems, 66, pp. 59-70. , https://doi.org/10.1016/j.future.2015.11.005; Haux, R., Health information systems - Past, present, future (2006) International Journal of Medical Informatics, 75 (3-4), pp. 268-281; McKee, M.G., Biofeedback: An overview in the context of heart-brain medicine (2008) Cleveland Clinic Journal of Medicine, 75, pp. S31-S34; Rottenberg, J., Frank, H., Wilhelm, J.J., Gross, H., Ian, G., Respiratory sinus arrhythmia as a predictor of outcome in major depressive disorder (2002) Journal of Affective Disorders, 71, pp. 265-272

PY - 2018

Y1 - 2018

N2 - In recent years, biofeedback has been widely applied into diagnosis and treatment of various diseases. There are also increasingly research exploiting various ICT (Information & Communication Technology) technologies, such as cloud technology, to achieve diagnosis and treatment. Therefore, how to use mobile cloud technology to assist the disease's diagnosis, to record treatment status, and to infer the result will be an important issue. In this paper, we will propose a mobile cloud platform and framework for the patient of mental illness for evaluating medication response through a variety of biofeedback information collection, integration, and fusion, so that physician can know the patient's situation. The physiological data including Heart Rate Variability and Brain Wave are collected through wearable sensors. And the psychological data is collected through monthly mood chart. The biofeedback physiological and psychological data can be fused into together to show the medication response after patient taking some medicines. An APP for the framework has been developed to show the effectiveness. © 2017 IEEE.

AB - In recent years, biofeedback has been widely applied into diagnosis and treatment of various diseases. There are also increasingly research exploiting various ICT (Information & Communication Technology) technologies, such as cloud technology, to achieve diagnosis and treatment. Therefore, how to use mobile cloud technology to assist the disease's diagnosis, to record treatment status, and to infer the result will be an important issue. In this paper, we will propose a mobile cloud platform and framework for the patient of mental illness for evaluating medication response through a variety of biofeedback information collection, integration, and fusion, so that physician can know the patient's situation. The physiological data including Heart Rate Variability and Brain Wave are collected through wearable sensors. And the psychological data is collected through monthly mood chart. The biofeedback physiological and psychological data can be fused into together to show the medication response after patient taking some medicines. An APP for the framework has been developed to show the effectiveness. © 2017 IEEE.

KW - Biofeedback

KW - Cloud computing

KW - Mobile Cloud

KW - Diagnosis

KW - Physiological models

KW - Physiology

KW - Brain wave

KW - Cloud technologies

KW - Communication technologies

KW - Heart rate variability

KW - Information collections

KW - Mental illness

KW - Mobile clouds

KW - Physiological data

KW - Diseases

U2 - 10.1109/SC2.2017.35

DO - 10.1109/SC2.2017.35

M3 - Paper

SP - 183

EP - 188

ER -