Automatic Diagnosis of Sinus Arrhythmia using HRV indices correlation during anesthesia state
Saleh, M. Naouar, M. Alahmed and R.B. . 2016
Abstract: This study establishes an efficient tool for investigation and automatic diagnosis of sinus arrhythmia
and autonomous nervous system (ANS) during anesthesia state. Application is designed to analyze heart rate
variability (HRV) based on 3D analysis: PWVT and installed to analyze the behavior of ANS state of anesthesia
in surgery service (n=11). PWVT demonstrate that the activity of ANS falls 8'43 after the injection of
anesthetic product, while STFT show this activity drops just at the injection, which is physiologically incorrect.
An automatic diagnosis method is introduced and it consists in preparing database with set of different cardiac
diseases parameters. The LF, HF, LF+HF and LF/HF powers are estimated to reflect the cardiac balance and
allow the establishment of parametric database. And estimated the correlation of the above parameters with
indices of HRV in time domain: Rri, RRSTD, rMSSD and pNN50. The correlations were significant with
p-value<0.001 for most pairs. Our study show, that variables strongly depend on vagal tone (pNN50, rMSSD
and LF and HF) were highly correlated (r > 0.89). According experimental results, LF/HF powers decrease during
anesthesia state and RRi increase.We conclude that some time and frequency domain indices correlate so
strongly with each other that they can surrogate each other
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Thisstudyestablishesanefficienttoolforinvestigationandautomaticdiagnosisofsinusarrhythmia and autonomous nervous system (ANS) during anesthesia state. Application is designed to analyze heart rate…
Abstract: This study establishes an efficient tool for investigation and automatic diagnosis of sinus arrhythmia
and autonomous nervous system (ANS) during anesthesia state. Application is…