CODESNA_HRV, a new tool to assess the activity of the autonomic nervous system from heart rate variability

Mourot, Laurent

doi:10.15761/pmrr.1000165

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…The DFA method shows typically two ranges of scale invariance, which are quantified by two separate scaling exponents, α 1 and α 2 , reflecting the short-term and long-term correlation. The short-term fluctuation is characterized by the slope α 1 obtained from the (log 10 (s), log 10 (F s )) graph within range [4,16] and long-range fluctuation is characterized by the slope α 2 obtained from the range [17].…”

Section: Fractal Methodsmentioning

confidence: 99%

“…Some of these software systems offer a graphical user interface, allowing them to be used by a wider range of users. Most systems are desktop applications and are created in MATLAB (Kubios [16], CODESNA_HRV [17], KARDIA [18], SinusCor [19], POLYAN [20] etc. ), and gHRV [21] was created in the Python programming language, rHRV [22] was developed in the "R" programming language.…”

Section: Introductionmentioning

confidence: 99%

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Cardio-Diagnostic Assisting Computer System

et al. 2020

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The mathematical analysis and the assessment of heart rate variability (HRV) based on computer systems can assist the diagnostic process with determining the cardiac status of patients. The new cardio-diagnostic assisting computer system created uses the classic Time-Domain, Frequency-Domain, and Time-Frequency analysis indices, as well as the nonlinear methods (Poincaré plot, Recurrence plot, Hurst R/S method, Detrended Fluctuation Analysis (DFA), Multi-Fractal DFA, Approximate Entropy and Sample Entropy). To test the feasibility of the software developed, 24-hour Holter recordings of four groups of people were analysed: healthy subjects and patients with arrhythmia, heart failure and syncope. Time-Domain (SDNN < 50 ms, SDANN < 100 ms, RMSSD < 17 ms) and Frequency-Domain (the spectrum of HRV in the LF < 550 ms2, and HF < 540 ms2) parameter values decreased in the cardiovascular disease groups compared to the control group as a result of lower HRV due to decreased parasympathetic and increased sympathetic activity. The results of the nonlinear analysis showed low values of (SD1 < 56 ms, SD2 < 110 ms) at Poincaré plot (Alpha < 90 ms) at DFA in patients with diseases. Significantly reducing these parameters are markers of cardiac dysfunction. The examined groups of patients showed an increase in the parameters (DET% > 95, REC% > 38, ENTR > 3.2) at the Recurrence plot. This is evidence of a pathological change in the regulation of heart rhythm. The system created can be useful in making the diagnosis by the cardiologist and in bringing greater accuracy and objectivity to the treatment.

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Section: Fractal Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cardio-Diagnostic Assisting Computer System

et al. 2020

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“…This hardware dependency might be a disadvantage for some users. The most popular desktop applications for Heart Rate Variability analysis are: Kubios HRV software [30], CODESNA [31], SinusCor [32], gHRV [33], and others.…”

Section: Research Backgroundmentioning

confidence: 99%

Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals

et al. 2020

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This article introduces an integrated photoplethysmographic (PPG) based cardiovascular monitoring system that consists of an individually portable PPG device for recording photoplethysmographic signals and a software system with a serverless architecture for processing, storing, and analyzing the obtained signals. The portable device uses the optical plethysmography technique for measuring blood volume in blood vessels. The device was tested and validated by a comparative analysis of three photoplethysmographic signals and one Electrocardiographic signal registered simultaneously in the target subject. The comparative analysis of these signals shows insignificant deviations in the obtained results, with the mean squared error between the studied signals being less than 21 ms. This deviation cannot affect the results that were obtained from the analysis of the interval series tested. Based on this result, we assume that the detected signals with the proposed device are realistic. The designed software system processes the registered data, performs preprocessing, determines the pulse rate variability, and performs mathematical analysis of PP intervals. Two groups of subjects were studied: 42 patients with arrhythmia and 40 healthy controls. Mathematical methods for data analysis in time and frequency domain and nonlinear methods (Poincaré plots, Rescaled Range Plot, Detrended Fluctuation Analysis, and MultiFractal Detrended Fluctuation Analysis) are applied. The obtained results are presented in tabular form and some of them in graphical form. The parameters studied in the time and frequency domain, as well as with the nonlinear methods, have statistical significance (p < 0.05) and they can distinguish between the two studied groups. Visual analysis of PP intervals, based on Poincare’s nonlinear method, provides important information on the physiological status of patients, allowing for one to see at a glance the entire PP interval series and quickly detect cardiovascular disorders, if any. The photoplethysmographic data of healthy individuals and patients diagnosed with arrhythmia were recorded, processed, and examined through the system under the guidance of a cardiologist. The results were analyzed and it was concluded that this system could serve to monitor patients with cardiovascular diseases and, when the condition worsens, a signal could be generated and sent to the hospital for undertaking immediate measures to stabilize patient’s health.

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CODESNA_HRV, a new tool to assess the activity of the autonomic nervous system from heart rate variability

Abstract: EA 3920 Prognostic markers and regulatory factors of cardiovascular diseases and Exercise Performance, Health, Innovation platform,

Cited by 2 publications

References 39 publications

Cardio-Diagnostic Assisting Computer System

Cardio-Diagnostic Assisting Computer System

Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals

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