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A novel method for nonstationary power spectral density estimation of cardiovascular pressure signals based on a Kalman filter with variable number of measurements
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhang, Z. G. Chan, Shing-Chow Aboy, M. |
| Copyright Year | 2008 |
| Abstract | We present a novel parametric power spectral density (PSD) estimation algorithm for nonstationary signals based on a Kalman filter with variable number of measurements (KFVNM). The nonstationary signals under consideration are modeled as time-varying autoregressive (AR) processes. The proposed algorithm uses a block of measurements to estimate the time-varying AR coefficients and obtains high-resolution PSD estimates. The intersection of confidence intervals (ICI) rule is incorporated into the algorithm to generate a PSD with adaptive window size from a series of PSDs with different number of measurements. We report the results of a quantitative assessment study and show an illustrative example involving the application of the algorithm to intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI). |
| Starting Page | 789 |
| Ending Page | 797 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/s11517-008-0351-x |
| Alternate Webpage(s) | http://www.mateoaboy.com/f5/page16/files/Zhang.08.pdf |
| Alternate Webpage(s) | https://www.wikidata.org/entity/Q51878267 |
| PubMed reference number | 18496723 |
| Alternate Webpage(s) | https://doi.org/10.1007/s11517-008-0351-x |
| Volume Number | 46 |
| Journal | Medical & Biological Engineering & Computing |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
