Spectral Analysis of the High Resolution ECG: Current Concepts and Future Directions

1994 ◽  
Vol 17 (3) ◽  
pp. 446-450 ◽  
Author(s):  
RALPH HABERL ◽  
PETER STEINBIGLER ◽  
GERHARD JILGE
1998 ◽  
Author(s):  
Eugen N. Scarlat ◽  
Liliana Preda ◽  
Constantin P. Cristescu ◽  
Alexandru M. Preda

2006 ◽  
Vol 65 (02) ◽  
pp. 264-274 ◽  
Author(s):  
Lora R. Stevens ◽  
Jeffery R. Stone ◽  
Josh Campbell ◽  
Sherilyn C. Fritz

AbstractA 2200-yr long, high-resolution (∼5 yr) record of drought variability in northwest Montana is inferred from diatoms and δ18O values of bio-induced carbonate preserved in a varved lacustrine core from Foy Lake. A previously developed model of the diatom response to lake-level fluctuations is used to constrain estimates of paleolake levels derived from the diatom data. High-frequency (decadal) fluctuations in the de-trended δ18O record mirror variations in wet/dry cycles inferred from Banff tree-rings, demonstrating the sensitivity of the oxygen-isotope values to changes in regional moisture balance. Low frequency (multi-centennial) isotopic changes may be associated with shifts in the seasonal distribution of precipitation. From 200 B.C. to A.D. 800, both diatom and isotope records indicate that climate was dry and lake level low, with poor diatom preservation and high organic carbon: nitrogen ratios. Subsequently, lake level rose slightly, although the climate was drier and more stable than modern conditions. At A.D. 1200, lake level increased to approximately 6 m below present elevation, after which the lake fluctuated between this elevation and full stage, with particularly cool and/or wetter conditions after 1700. The hydrologic balance of the lake shifted abruptly at 1894 because of the establishment of a lumber mill at the lake's outlet. Spectral analysis of the δ18O data indicates that severe droughts occurred with multi-decadal (50 to 70 yr) frequency.


1969 ◽  
Vol 8 (5) ◽  
pp. 1064 ◽  
Author(s):  
B. J. Pernick ◽  
C. Bartolotta ◽  
D. Yustein

2021 ◽  
pp. 20200944
Author(s):  
Lucio Calandriello ◽  
Simon LF Walsh

In patients with idiopathic pulmonary fibrosis (IPF), there is an urgent need of biomarkers which can predict disease behaviour or response to treatment. Most published studies report results based on continuous data which can be difficult to apply to individual patients in clinical practice. Having antifibrotic therapies makes it even more important that we can accurately diagnose and prognosticate in IPF patients. Advances in computer technology over the past decade have provided computer-based methods for objectively quantifying fibrotic lung disease on high-resolution CT of the chest with greater strength than visual CT analysis scores. These computer-based methods and, more recently, the arrival of deep learning-based image analysis might provide a response to these unsolved problems. The purpose of this commentary is to provide insights into the problems associated with visual interpretation of HRCT, describe of the current technologies used to provide quantification of disease on HRCT and prognostication in IPF patients, discuss challenges to the implementation of this technology and future directions.


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