Nonlinear trend removal should be carefully performed in heart rate variability analysis

$\bullet$ Background : In Heart rate variability analysis, the rate-rate time series suffer often from aperiodic non-stationarity, presence of ectopic beats etc. It would be hard to extract helpful information from the original signals. 10 $\bullet$ Problem : Trend removal methods are commonly practiced to reduce the influence of the low frequency and aperiodic non-stationary in RR data. This can unfortunately affect the signal and make the analysis on detrended data less appropriate. $\bullet$ Objective : Investigate the detrending effect (linear \& nonlinear) in temporal / nonliear analysis of heart rate variability of long-term RR data (in normal sinus rhythm, atrial fibrillation, 15 congestive heart failure and ventricular premature arrhythmia conditions). $\bullet$ Methods : Temporal method : standard measure SDNN; Nonlinear methods : multi-scale Fractal Dimension (FD), Detrended Fluctuation Analysis (DFA) \& Sample Entropy (Sam-pEn) analysis. $\bullet$ Results : The linear detrending affects little the global characteristics of the RR data, either 20 in temporal analysis or in nonlinear complexity analysis. After linear detrending, the SDNNs are just slightly shifted and all distributions are well preserved. The cross-scale complexity remained almost the same as the ones for original RR data or correlated. Nonlinear detrending changed not only the SDNNs distribution, but also the order among different types of RR data. After this processing, the SDNN became indistinguishable be-25 tween SDNN for normal sinus rhythm and ventricular premature beats. Different RR data has different complexity signature. Nonlinear detrending made the all RR data to be similar , in terms of complexity. It is thus impossible to distinguish them. The FD showed that nonlinearly detrended RR data has a dimension close to 2, the exponent from DFA is close to zero and SampEn is larger than 1.5 -- these complexity values are very close to those for 30 random signal. $\bullet$ Conclusions : Pre-processing by linear detrending can be performed on RR data, which has little influence on the corresponding analysis. Nonlinear detrending could be harmful and it is not advisable to use this type of pre-processing. Exceptions do exist, but only combined with other appropriate techniques to avoid complete change of the signal's intrinsic dynamics. 35 Keywords $\bullet$ heart rate variability $\bullet$ linear / nonlinear detrending $\bullet$ complexity analysis $\bullet$ mul-tiscale analysis $\bullet$ detrended fluctuation analysis $\bullet$ fractal dimension $\bullet$ sample entropy

Design, Evaluation, and Application of Heart Rate Variability Analysis Software (HRVAS)

The analysis of heart rate variability (HRV) has become an increasingly popular and important tool for studying many disease pathologies in the past twenty years. HRV analyses are methods used to non-invasively quantify variability within heart rate. Purposes of this study were to design, evaluate, and apply an easy to use and open-source HRV analysis software package (HRVAS). HRVAS implements four major categories of HRV techniques: statistical and time-domain analysis, frequency-domain analysis, nonlinear analysis, and time-frequency analysis. Software evaluations were accomplished by performing HRV analysis on simulated and public congestive heart failure (CHF) data. Application of HRVAS included studying the effects of hyperaldosteronism on HRV in rats. Simulation and CHF results demonstrated that HRVAS was a dependable HRV analysis tool. Results from the rat hyperaldosteronism model showed that 5 of 26 HRV measures were statistically significant (p\u3c0.05). HRVAS provides a useful tool for HRV analysis to researchers

Feedback Control of Human Stress with Music Modulation

Mental stress has known detrimental effects on human health, however few algorithmic methods of reducing mental stress have been widely explored. While the act of listening to music has been shown to have beneficial effects for stress reduction, and furthermore, audio players have been designed to selectively choose music and other inputs with the intent of stress reduction, limited work has been conducted for real-time stress reduction with feedback control using physiological input signals such as heart rate or Heart Rate Variability (HRV). This thesis proposes a feedback controller that uses HRV signals from wearable sensors to perform real-time (< 1 second) modulations to music through tempo changes with the goal to regulate and reduce stress levels. A standardized, stress inducing test based on the popular Stroop test is also introduced, which has been shown to induce acute stress in subjects and can be used as a testing benchmark for controller design. Ultimately, a controller is presented that when used is not only able to maintain stress levels during stress-inducing inputs to a human but even provides de-stressing effects beyond baseline performance.No embargoAcademic Major: Electrical and Computer Engineerin

Evaluation of two detrending techniques for application in Heart Rate Variability

The performance of two different algorithms of detrending the RR-interval before Heart Rate Variability (HRV) analysis has been evaluated using both, simulated signals and real RR-interval time series. The first algorithm is based on the smoothness prior approach (SPA) and the second algorithm is implemented using wavelet packet (WP) analysis. The calculated time and frequency domain parameters obtained from real signals after detrending and the results obtained from simulated signals suggest that the WP method performed better than the SPA. The WP method provided more attenuation of the slow varying trend and was able to preserve the other signal components better than the SPA method. Also the SPA method was computationally slower and it might be not appropriate with long signals

Comparison of Linear Method and Non Linear Method for Analysis of Heart Rate Variability using MatlabÂ

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An Efficient Time-Varying Filter for Detrending and Bandwidth Limiting the Heart Rate Variability Tachogram without Resampling: MATLAB Open-Source Code and Internet Web-Based Implementation

The heart rate variability (HRV) signal derived from the ECG is a beat-to-beat record of RR intervals and is, as a time series, irregularly sampled. It is common engineering practice to resample this record, typically at 4 Hz, onto a regular time axis for analysis in advance of time domain filtering and spectral analysis based on the DFT. However, it is recognised that resampling introduces noise and frequency bias. The present work describes the implementation of a time-varying filter using a smoothing priors approach based on a Gaussian process model, which does not require data to be regular in time. Its output is directly compatible with the Lomb-Scargle algorithm for power density estimation. A web-based demonstration is available over the Internet for exemplar data. The MATLAB (MathWorks Inc.) code can be downloaded as open source

Detrending heart rate variability signal with empirical mode decomposition EMD

Analýza HRV je významným ukazatelem pro patofyziologická vyšetření. Při analýze se využívají detekce vlny R z průběhu EKG. Intervaly R-R mohou být následně analyzovány různými metodami. Při spektrální analýze je častým jevem rušivý nestacionární trend, který je potřeba odstranit. V této práci, která se zabývá odstraněním trendu, je hlavně představena technicky zajímavá a v posledních letech oblíbená metoda empirického rozkladu (EMD – Empirical mode decomposition). Následně je tato metoda porovnávána s metodou vlnkové transformace a předešlou hladkostí přiblížení (SPA – Smoothness prior approach).HRV analysis is an important indicator of pathophysiological examination. R-R waves are used for detection and analysis of ECG interval. R-R intervals can be analyzed by various methods. During spectral analysis is an often phenomenon disturbing non-stationary trend, which needs to be removed. In this paper, which deals about detrending, is mainly introduced Empirical mode decomposition (EMD) which is popular in recent years. Subsequently, this method is being compared to the method of wavelet transformation and Smoothness prior apprach (SPA).