Validation of Computer Models for Evaluating the Efficacy of Cognitive Stimulation Therapy
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Grohol, J. M. (2004). What to look for in quality online support groups. http://psychcentral.com/archives/support_groups.htm . Accessed May 01, 2015.
Dinesen, B., & Toft, E. (2009). Telehomecare challenge collaboration among healthcare professionals. Wireless Personal Communications, 51, 711–724.
Delmastro, F. (2012). Pervasive communications in healthcare. Computer Communications, 35, 1284–1295.
Algaet, M. A., Noh, Z. A., Shibghatullah, A. S., Milad, A. A., & Mustapha, A. (2014). Provisioning quality of service of wireless telemedicine for e-health services: A review. Wireless Personal Communications, 78, 375–406.
Clark, L. W. (1995). Interventions for persons with Alzheimer’s disease: Strategies for maintaining and enhancing communicative success. Topics in Language Disorders, 15, 47–65.
Jootun, D., & McGhee, G. (2011). Effective communication with people who have dementia. Nursing Standard, 25, 40–46.
Orgeta, V., Qazi, A., Spector, A. E., & Orrell, M. (2014). Psychological treatments for depression and anxiety in dementia and mild cognitive impairment. Cochrane Database of Systematic Reviews, 1, CD009125. doi: 10.1002/14651858.CD009125.pub2 .
Kueider, A. M., Parisi, J. M., Gross, A. L., & Rebok, G. W. (2012). Computerized cognitive training with older adults: A systematic review. PLoS ONE, 7, e40588. doi: 10.1371/journal.pone.0040588 .
Shelley, K., & Shelley, S. (2001). Pulse oximeter waveform: Photoelectric plethysmography. In C. Lake (Ed.), Clinical monitoring: Practical applications for anesthesia and critical care (Chapter 23, pp. 420–428). Philadelphia: Saunders.
Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement, 28, R1–R39.
Asada, H. H., Shaltis, P., Reisner, A., Rhee, S., & Hutchinson, R. C. (2003). Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Engineering in Medicine and Biology Magazine, 22, 28–40.
Reisner, A. T., Shaltis, P. A., McCombie, D., & Asada, H. H. (2008). Utility of the photoplethysmogram in circulatory monitoring. Anesthesiology, 108, 950–958.
Frey, B., Waldvogel, K., & Balmer, C. (2008). Clinical applications of photoplethysmography in paediatric intensive care. Intensive Care Medicine, 34, 578–582.
Monte-Moreno, E. (2011). Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artificial Intelligence in Medicine, 53, 127–138.
Pham, T. D., Truong, C. T., Oyama-Higa, M., & Sugiyama, M. (2013). Mental-disorder detection using chaos and nonlinear dynamical analysis of photoplethysmographic signals. Chaos, Solitons & Fractals, 51, 64–74.
Sonoda, K., Kishida, Y., Tanaka, T., Kanda, K., Fujita, T., Higuchi, K., et al. (2013). Wearable photoplethysmographic sensor system with PSoC microcontroller. International Journal of Intelligent Computing in Medical Sciences & Image Processing, 5, 45–55.
Yousefi, R., Nourani, M., Ostadabbas, S., & Panahi, I. (2014). A motion-tolerant adaptive algorithm for wearable photoplethysmographic biosensors. IEEE J Biomedical and Health Informatics, 18, 670–681.
Wang, W., Stuijk, S., & de Haan, G. (2015). Exploiting spatial-redundancy of image sensor for motion robust rPPG. IEEE Transactions on Biomedical Engineering, 62, 415–425.
Pham, T. D., Oyama-Higa, M., Truong, C. T., Okamoto, K., Futaba, T., Kanemoto, S., et al. (2015). Computerized assessment of communication for cognitive stimulation for people with cognitive decline using spectral-distortion measures and phylogenetic inference. PLoS ONE, 10, e0118739. doi: 10.1371/journal.pone.0118739 .
Spigulis, J., Erts, R., Nikiforovs, V., Kviesis-Kipge, E. (2008). Wearable wireless photoplethysmography sensors. In Proceedings of SPIE biophotonics: Photonic solutions for better health care, 6691, 7. doi: 10.1117/12.801966 .
Takens, F. (1981). Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Lecture Notes in Mathematics, 898, 366–381.
Rosenstein, M. T., Collins, J. J., & DeLuca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65, 117–134.
Rabiner, L., & Juang, B. H. (1993). Fundamentals of speech recognition. New Jersey: Prentice Hall.
Ingle, V. K., & Proakis, J. G. (1997). Digital Signal Processing Using Matlab V.4. Boston: PWS Publishing.
Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1992). Numerical recipes in FORTRAN: The art of scientific computing (2nd ed.). Cambridge: Cambridge University Press.
Itakura, F., Saito, S. (1968). An analysis-synthesis telephony based on maximum likelihood method. In Reports of 6th international congress on acoustics, pp. C:5–5, C:17–20.
Itakura, F. (1975). Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech and Signal Processing, 23, 67–72.
Soong, F., Sondhi, M. M. (1987). A frequency-weighted Itakura spectral distortion measure and its application to speech recognition in noise. In Proceedings of IEEE international conference on acoustics, speech, and signal processing, pp. 625–628.
Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Proceedings of AAAI-94 workshop on knowledge discovery in databases, pp. 359–370.
Müller, M. (Ed.). (2007). Dynamic time warping. In Information retrieval for music and motion (pp. 69–84). Berlin: Springer.
Ecker, J. G., & Kupferschmid, M. (1988). Introduction to operations research. New York: Wiley.
Orfanidis, S. J. (1996). Introduction to signal processing. Englewood Cliffs: Prentice-Hall.
Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum.
Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition. London: Academic Press.
Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. Boca Raton: Chapman & Hall/CRC.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Mirhosseini, A. R., Yan, H., Lam, K. M., & Pham, T. (1998). Human face image recognition: An evidence aggregation approach. Computer Vision & Image Understanding, 71, 213–230.
Pham, T. D., & Yan, H. (1997). Fusion of handwritten numeral classifiers based on fuzzy and genetic algorithms. In Proceedings of North America fuzzy information processing society, pp. 257–262.