Data Mining based Methodologies for Cardiac Risk Patterns Identification

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2007 ◽  
Vol 40 (15) ◽  
pp. 8-9
Author(s):  
ROGER S. BLUMENTHAL
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2011 ◽  
Vol 44 (1) ◽  
pp. 46-47
Author(s):  
HOWARD P. LEVY

2010 ◽  
Vol 43 (20) ◽  
pp. 72
Author(s):  
M. ALEXANDER OTTO
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2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
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2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.


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