The use of biomarkers in the elderly: current and future challenges.
ABSTRACT Biomarkers are hypothesized but not frequently used in research with the elderly because of a general paucity of supportive scientific data. However, there is an obvious need for greater diagnostic specificity and sensitivity across many diagnoses in the elderly, as well as good targets for therapeutic trials. The authors reviewed the available information in this field as part of a general review of geriatric research for the . Potential biomarkers with pathophysiologic significance have been studied in the field of Alzheimer disease research with some success, especially in the area of genetic markers (apolipoprotein E [APOE] epsilon4 allele), neuroimaging, and cerebrospinal fluid markers (beta-amyloid and tau). While some progress has been made in the search for adequate biomarkers in the elderly, in particular with Alzheimer disease, much more work is needed before these potential biomarkers can be reliably used in clinical practice.
SourceAvailable from: Michael S. Ritsner
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ABSTRACT: Data-driven machine-learning techniques enable the modeling and interpretation of complex physiological signals. The energy consumption of these techniques, however, can be excessive, due to the complexity of the models required. In this paper, we study the tradeoffs and limitations imposed by the energy consumption of high-order detection models implemented in devices designed for intelligent biomedical sensing. Based on the flexibility and efficiency needs at various processing stages in data-driven biomedical algorithms, we explore options for hardware specialization through architectures based on custom instruction and coprocessor computations. We identify the limitations in the former, and propose a coprocessor-based platform that exploits parallelism in computation as well as voltage scaling to operate at a subthreshold minimum-energy point. We present results from post-layout simulation of cardiac arrhythmia detection with patient data from the MIT-BIH database. After wavelet-based feature extraction, which consumes 12.28 μJ, we demonstrate classification computations in the 12.00-120.05 μJ range using 10000-100000 support vectors. This represents 1170× lower energy than that of a low-power processor with custom instructions alone. After morphological feature extraction, which consumes 8.65 μJ of energy, the corresponding energy numbers are 10.24-24.51 μJ, which is 1548× smaller than one based on a custom-instruction design. Results correspond to Vdd=0.4 V and a data precision of 8 b.IEEE Transactions on Very Large Scale Integration (VLSI) Systems 10/2013; 21(10):1849-1862. DOI:10.1109/TVLSI.2012.2220161 · 1.14 Impact Factor