Advanced biomedical engineering by Gaetano D Gargiulo; Alistair McEwan

By Gaetano D Gargiulo; Alistair McEwan

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2010) and finite mixture model (Acharya & Zhu, 2009) for estimating the correlation structure from replicated measurements corresponding to a gene set with blind or informed replication mechanism. Throughout this section, we treat each replicated measurement individually as a random variable and assume that data are independently and identically distributed samples from a multivariate normal distribution. We discuss the parameter structures for each model and their estimation from replicated measurements corresponding to a pair of genes X and Y or a gene set with k genes X1 , .

Int. Conf. , Hong Kong, 1998, pp. 163–166. -M. Lau, 1994: Wavelets, period doubling, and time-frequency localization with application to organization of convection over the tropical western Pacific. J. Atmos. , 51, 2523–2541. ; Kong, X. & Liao, S. (2008). “Pulse wave analysis for cardiovascular information monitoring in patients with chronic heart failure: effects of COQ10 treatment” Montreal: Bio-engineering 2008 0 3 Multivariate Models and Algorithms for Learning Correlation Structures from Replicated Molecular Profiling Data Lipi R.

Big wide diastolic component may cause the local maximum value in frequency domain. 2. Slow heart rate 3. Unstable pulse 4. Incomplete waveforms caused by device The shape of diastolic component is important to arterial stiffness analysis. But it’s difficult to get the corresponding features at frequency domain because the diastolic part is relatively small and can be easily affected by systolic part in FFT. The features for arterial stiffness can not be derived directly from the Fourier transform.

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