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Pham, Tuan, ProfessorORCID iD iconorcid.org/0000-0002-4255-5130
Alternative names
Biography [eng]

I am a Professor of Biomedical Engineering.  My current research focuses on image processing, time-series analysis and pattern recognition applied to medicine, biology, and mental health.  

Publications (10 of 112) Show all publications
Liu, J., Pham, T., Yan, H. & Liang, Z. (2018). Fuzzy mixed-prototype clustering algorithm for microarray data analysis. Neurocomputing, 276, 42-54
Open this publication in new window or tab >>Fuzzy mixed-prototype clustering algorithm for microarray data analysis
2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 276, p. 42-54Article in journal (Refereed) Published
Abstract [en]

Being motivated by combining the advantages of hyperplane-based pattern analysis and fuzzy clustering techniques, we present in this paper a fuzzy mix-prototype (FMP) clustering for microarray data analysis. By integrating spherical and hyper-planar cluster prototypes, the FMP is capable of capturing latent data models with both spherical and non-spherical geometric structures. Our contributions of the paper can be summarized into three folds: first, the objective function of the FMP is formulated. Second, an iterative solution which minimizes the objective function under given constraints is derived. Third, the effectiveness of the proposed FMP is demonstrated through experiments on yeast and leukemia data sets.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
FMP, Microarray data analysis, Fuzzy clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-141163 (URN)10.1016/j.neucom.2017.06.083 (DOI)000419222000005 ()
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2018-01-22Bibliographically approved
Pham, T. (2018). Fuzzy Weighted Recurrence Networks of Time Series. Physica A: Statistical Mechanics and its Applications
Open this publication in new window or tab >>Fuzzy Weighted Recurrence Networks of Time Series
2018 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371Article in journal (Refereed) Accepted
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-150832 (URN)
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2018-09-05Bibliographically approved
Pham, T. (2018). Nonlinear dynamics analysis of short-time photoplethysmogram in Parkinson's disease. In: : . Paper presented at 2018 IEEE International Conference on Fuzzy Systems (pp. 1749-1754). IEEE
Open this publication in new window or tab >>Nonlinear dynamics analysis of short-time photoplethysmogram in Parkinson's disease
2018 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-149772 (URN)978-1-5090-6020-7 (ISBN)
Conference
2018 IEEE International Conference on Fuzzy Systems
Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2018-08-15
Pham, T. (2018). Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features. In: : . Paper presented at 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (pp. 112-115). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Pattern analysis of blood oxygen saturation is important for gaining insights into the cardiorespiratory control system, real-time monitoring during operations, identifying potential predictors for the diagnosis of disease severity, and improving the hospitalization of patients with critical chronic diseases. This paper investigates the use of nonlinear dynamics features for machine learning and classification of blood oxygen saturation signals in healthy young and healthy old subjects. The validation of the feature reliability for the signal variability analysis has a clinical implication for differentiating blood oxygen saturation in patients with respect to the particular influence of aging, when patient's data become available.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-147558 (URN)10.1109/BHI.2018.8333382 (DOI)978-1-5386-2405-0 (ISBN)978-1-5386-2406-7 (ISBN)
Conference
2018 IEEE EMBS International Conference on Biomedical & Health Informatics
Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2018-05-18
Pham, T. (2018). Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features. Journal of Neuroscience Methods, 307, 128-130
Open this publication in new window or tab >>Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features
2018 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 307, p. 128-130Article in journal (Refereed) Published
Abstract [en]

A noncommutative algebra corresponding to the classical catenoid is introduced together with a differential calculus of derivations. We prove that there exists a unique metric and torsion-free connection that is compatible with the complex structure, and the curvature is explicitly calculated. A noncommutative analogue of the fact that the catenoid is a minimal surface is studied by constructing a Laplace operator from the connection and showing that the embedding coordinates are harmonic. Furthermore, an integral is defined and the total curvature is computed. Finally, classes of left and right modules are introduced together with constant curvature connections, and bimodule compatibility conditions are discussed in detail.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Noncommutative catenoid, Noncommutative Riemannian geometry, Noncommutative minimal surface, Noncommutative curvature
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-148129 (URN)10.1016/j.jneumeth.2018.05.019 (DOI)000442055800020 ()29859213 (PubMedID)
Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2018-10-04Bibliographically approved
Pham, T. (2017). Complementary features for radiomic analysis of malignant and benign mediastinal lymph nodes. In: : . Paper presented at 2017 IEEE International Conference on Image Processing, Beijing, China, September 17-20, 2017.
Open this publication in new window or tab >>Complementary features for radiomic analysis of malignant and benign mediastinal lymph nodes
2017 (English)Conference paper, Published paper (Refereed)
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-141164 (URN)
Conference
2017 IEEE International Conference on Image Processing, Beijing, China, September 17-20, 2017
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2017-10-13Bibliographically approved
Pham, T. (2017). Scaling of texture in training autoencoders for classification of histological images of colorectal cancer. In: F. Cong et al. (Ed.), Advances in Neural Networks: 14th International Symposium on Neural Networks (ISNN 2017 (pp. 524-532). Springer
Open this publication in new window or tab >>Scaling of texture in training autoencoders for classification of histological images of colorectal cancer
2017 (English)In: Advances in Neural Networks: 14th International Symposium on Neural Networks (ISNN 2017 / [ed] F. Cong et al., Springer, 2017, p. 524-532Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10261
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-138052 (URN)10.1007/978-3-319-59081-3 (DOI)978-3-319-59080-6 (ISBN)978-3-319-59081-3 (ISBN)
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-06-16Bibliographically approved
Pham, T. (2017). Time-shift multiscale entropy analysis of physiological signals. Entropy, 19(6), Article ID 257.
Open this publication in new window or tab >>Time-shift multiscale entropy analysis of physiological signals
2017 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 19, no 6, article id 257Article in journal (Refereed) Published
Abstract [en]

Measures of predictability in physiological signals using entropy measures have been widely applied in many areas of research. Multiscale entropy expresses different levels of either approximate entropy or sample entropy by means of multiple factors for generating multiple time series, enabling the capture of more useful information than using a scalar value produced by the two entropy methods. This paper presents the use of different time shifts on various intervals of time series to discover different entropy patterns of the time series. Examples and experimental results using white noise, 1/ f noise, photoplethysmography, and electromyography signals suggest the validity and better performance of the proposed time-shift multiscale entropy analysis of physiological signals than the multiscale entropy.

Keywords
approximate entropy, sample entropy, multiscale entropy, higuchi’s fractal dimension, time shift, physiological signals
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-138027 (URN)10.3390/e19060257 (DOI)000404454500020 ()
Note

Funding agencies: LiU Faculty of Science and Engineering

Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-11-29Bibliographically approved
Pham, T. D. (2017). Validation of computer models for evaluating the efficacy of cognitive stimulation therapy. Wireless personal communications, 94(3), 301-314
Open this publication in new window or tab >>Validation of computer models for evaluating the efficacy of cognitive stimulation therapy
2017 (English)In: Wireless personal communications, ISSN 0929-6212, E-ISSN 1572-834X, Vol. 94, no 3, p. 301-314Article in journal (Refereed) Published
Abstract [en]

The notion of using computational methods for evaluating cognitive stimulation therapy (CST) based on the synchronized recording of photoplethysmographic (PPG) signals of care-givers and participants offers an objective and cost-effective analysis in health care to improve the patient’s quality of life. While computer models are promising as a useful tool for such a purpose, a question of interest is how the model reliability, which is the degree to which an assessment tool produces stable and consistent results, can be established. This paper addresses this issue with the application of dynamic-time warping and resampling to measure the performance of two PPG features known as the largest Lyapunov exponent and linear predictive coding, which have been applied for studying the efficacy of CST. The potential success of this computerized evaluation can be a precursor to the development of a personalized e-therapy system that operates on mobile devices.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Cognitive stimulation therapy, Cognitive decline, Model performance assessment, Therapeutic communication, Dynamic-time warping, Photoplethysmograph, Largest Lyapunov exponent, Linear predictive coding
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-128597 (URN)10.1007/s11277-015-3017-7 (DOI)000401758400001 ()2-s2.0-84938817213 (Scopus ID)
Available from: 2016-05-25 Created: 2016-05-25 Last updated: 2018-02-09Bibliographically approved
Pham, T. & Wada, I. (2016). Chaos analysis of ER-network dynamics in microscopy imaging. In: Christos H. Skiadas, Charilaos Skiadas (Ed.), Handbook of applications of chaos theory: (pp. 253-270). Boca Raton, FL, USA: CRC Press
Open this publication in new window or tab >>Chaos analysis of ER-network dynamics in microscopy imaging
2016 (English)In: Handbook of applications of chaos theory / [ed] Christos H. Skiadas, Charilaos Skiadas, Boca Raton, FL, USA: CRC Press, 2016, p. 253-270Chapter in book (Refereed)
Place, publisher, year, edition, pages
Boca Raton, FL, USA: CRC Press, 2016
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-129070 (URN)9781466590434 (ISBN)9781466590441 (ISBN)
Available from: 2016-06-10 Created: 2016-06-10 Last updated: 2016-11-29Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4255-5130

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