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Proceedings Paper

Noninvasive methodology for wellness baseline profiling
Author(s): Danny Wen-Yaw Chung; Yuh-Show Tsai; Shaou-Gang Miaou; Walter H. Chang; Yaw-Jen Chang; Shia-Chung Chen; Y. Y. Hong; C. S. Chyang; Quan-Shong Chang; Hon-Yen Hsu; James Hsu; Wei-Cheng Yao; Ming-Sin Hsu; Ming-Chung Chen; Shi-Chen Lee; Charles Hsu; Lidan Miao; Kenny Byrd; Mohamed F. Chouikha; Xin-Bin Gu; Paul C. Wang; Harold Szu
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Paper Abstract

We develop an accumulatively effective and affordable set of smart pair devices to save the exuberant expenditure for the healthcare of aging population, which will not be sustainable when all the post-war baby boomers retire (78 millions will cost 1/5~1/4 GDP in US alone). To design an accessible test-bed for distributed points of homecare, we choose two exemplars of the set to demonstrate the possibility of translation of modern military and clinical know-how, because two exemplars share identically the noninvasive algorithm adapted to the Smart Sensor-pairs for the real world persistent surveillance. Currently, the standard diagnoses for malignant tumors and diabetes disorders are blood serum tests, X-ray CAT scan, and biopsy used sometime in the physical checkup by physicians as cohort-average wellness baselines. The loss of the quality of life in making second careers productive may be caused by the missing of timeliness for correct diagnoses and easier treatments, which contributes to the one quarter of human errors generating the lawsuits against physicians and hospitals, which further escalates the insurance cost and wasteful healthcare expenditure. Such a vicious cycle should be entirely eliminated by building an "individual diagnostic aids (IDA)," similar to the trend of personalized drug, developed from daily noninvasive intelligent databases of the "wellness baseline profiling (WBP)". Since our physiology state undulates diurnally, the Nyquist anti-aliasing theory dictates a minimum twice-a-day sampling of the WBP for the IDA, which must be made affordable by means of noninvasive, unsupervised and unbiased methodology at the convenience of homes. Thus, a pair of military infrared (IR) spectral cameras has been demonstrated for the noninvasive spectrogram ratio test of the spontaneously emitted thermal radiation from a normal human body at 37°C temperature. This invisible self-emission spreads from 3 microns to 12 microns of the radiation wavelengths. This pair of cameras are used in the military satellite surveillance imaging operated at 3~5 mid IR band and 8~12 long IR band accompanied by several other UV and visible bands cameras. We can thereby measure accurately both the thermal emission bands at the mid IR and the long IR (X1 X2). The spectral ratio will be independent of the depth and imaging environment. Similarly, we will take six times per pair saliva samples (X1 X2) inside the upper jaw for three meals daily, of which the dynamics is shown as a delayed mirror image of "blood glucose level". And for which we must design a portable lab "system on chip (SOC)," and the micro-fluidity of pair channels per chemical reactions. According to the same biochemical principle of spontaneity, we apply the identical algorithm to determine both the ratio of hidden malignant and benign heat sources (s1, s2) and the blood glucose & other sources (s1, s2) leaking into the saliva. This is possible because of the Gibbs isothermal spontaneous process, in which the Helmholtz free energy must be minimized for the spontaneous thermal radiation from unknown mixing of malign and benign sources or the diffusion mixing of glucose s1 * and other sources s2 *. We have derived a general formula relating two equilibrium values, before and after, in order to design our devices. Daily tracking the spectrogram ratio and saliva glucose levels are, nevertheless, needed for a reliable prediction of individual malignant angiogenesis and blood glucose level in real time.

Paper Details

Date Published: 9 April 2007
PDF: 17 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760R (9 April 2007); doi: 10.1117/12.724306
Show Author Affiliations
Danny Wen-Yaw Chung, Chung Yuan Christian Univ. (Taiwan)
Yuh-Show Tsai, Chung Yuan Christian Univ. (Taiwan)
Shaou-Gang Miaou, Chung Yuan Christian Univ. (Taiwan)
Walter H. Chang, Chung Yuan Christian Univ. (Taiwan)
Yaw-Jen Chang, Chung Yuan Christian Univ. (Taiwan)
Shia-Chung Chen, Chung Yuan Christian Univ. (Taiwan)
Y. Y. Hong, Chung Yuan Christian Univ. (Taiwan)
C. S. Chyang, Chung Yuan Christian Univ. (Taiwan)
Quan-Shong Chang, Ming-Shen Hospitals at Long Tan (Taiwan)
Hon-Yen Hsu, Ming-Shen Hospitals at Long Tan (Taiwan)
James Hsu, Ming-Shen Hospitals at Long Tan (Taiwan)
Wei-Cheng Yao, Ming-Shen Hospitals at Long Tan (Taiwan)
Ming-Sin Hsu, Ming-Shen Hospitals at Long Tan (Taiwan)
Ming-Chung Chen, National Taiwan Univ. Hospital, NTU (Taiwan)
Shi-Chen Lee, National Taiwan Univ. Hospital, NTU (Taiwan)
Charles Hsu, Howard Univ. (United States)
Lidan Miao, Howard Univ. (United States)
Kenny Byrd, Howard Univ. (United States)
Mohamed F. Chouikha, Howard Univ. (United States)
Xin-Bin Gu, Howard Univ. (United States)
Paul C. Wang, Howard Univ. (United States)
Harold Szu, Howard Univ. (United States)
Office of Naval Research (United States)

Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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