Human State Classification and Predication for Critical Care Monitoring by Real-Time Bio-signal Analysis
Abstract
To address the challenges in critical care monitoring, we present a multi-modality bio-signal modeling and analysis modeling framework for real-time human state classification and predication. The novel bioinformatic framework is developed to solve the human state classification and predication issues from two aspects: a) achieve 1:1 mapping between the bio-signal and the human state via discriminant feature analysis and selection by using probabilistic principle component analysis (PPCA); b) avoid time-consuming data analysis and extensive integration resources by using Dynamic Bayesian Network (DBN). In addition, intelligent and automatic selection of the most suitable sensors from the bio-sensor array is also integrated in the proposed DBN.
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Three most popular approaches for dimension reduction in medical domain are PCA, ICA, and LDA, which statistically select the subset of most significant features.
“Three most popular approaches for dimension reduction in medical domain are PCA, ICA, and LDA [66] which are statistically selecting the subset of the most significant features [45,67].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [67]
Most research has used several sensors to have global reasoning in health monitoring.
“Besides the works focusing on physiological data mining gathered from a single sensor, most of the researches have used several sensors [30,40,44,67] to have global reasoning in health monitoring.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [67]
Cites
Probabilistic Principal Component Analysis
Tipping, M. E., & Bishop, C. M. (1999) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00196 · Cited by 3,679 · see citing works
A Tutorial on Learning with Bayesian Networks
Heckerman, D. E. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-85066-3_3 · Cited by 1,712 · see citing works
An ultra-wearable, wireless, low power ECG monitoring system
Park, C., Chou, P. H., Bai, Y., Matthews, R., & Hibbs, A. H. (2006) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2006.4600353 · Cited by 265 · see citing works
Recursive Noisy OR—A Rule for Estimating Complex Probabilistic Interactions
Lemmer, J. F., & Gossink, D. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2004.834424 · Cited by 89 · see citing works
Giving advice in negotiation using physiological information
Yuasa, M., Yasumura, Y., & Nitta, K. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2000.884997 · Cited by 3 · see citing works
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
Machine and Deep Learning Algorithms for Wearable Health Monitoring
Fei, C., Liu, R., Li, Z., Wang, T., & Baig, F. N. (2021) · Health information science · doi:10.1007/978-3-030-68723-6_6 · Cited by 27 · see citing works
Marseet, A. A., Kumar, S., & Sahin, F. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217985
- Authors
- Li, X., & Porikli, F.
- Affiliations
- Northrop Grumman (United States) (US)
- Mitsubishi Electric Research Laboratories (United States) (US)
- Venue
- 2010 20th International Conference on Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-08
- Volume
- 34
- Pages
- 2460-2463
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2069257788
- Cited by
- 3
- References
- 5
- Citations by year
- 2021: 1 · 2017: 1 · 2013: 1
- FWCI
- 0.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition (publishedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- intensive care monitoring, probabilistic principal component analysis, dynamic Bayesian networks, sensor selection, biosensor arrays
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