Hrv analysis software

Manufactured by Kubios

Sourced in Finland

Kubios HRV analysis software is designed to process and analyze heart rate variability (HRV) data. The software provides tools for calculating various HRV parameters from recorded R-R interval or heart rate time series data.

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Lab products found in correlation

T31 transmitter, by Polar Electro (1 mentions) Polar 800 watch, by Polar Electro (1 mentions)

Close spelling variants of this product

HRV software (36 citations) Kubios HRV analysis software (Premium version 3.2.0., (2 citations) Kubios HRV analysis software, version 3.3 (2 citations)

7 protocols using hrv analysis software

Heart Rate Variability During 6MWT

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HR and RR intervals (iR-R) were recorded using a telemetric cardiac monitor (Polar¯ S810i, Finland). An elastic band (Polar T31 transmitter) was placed around the patient's thorax at the level of the lower third of the sternum, while the patient was in a sitting position; signals were continuously transmitted to the receiving unit by electromagnetic field. Recorded data were then transferred to Kubios HRV¯ analysis software (version 2.2, Finland) for subsequent analysis.
The signal processing was carried out as follows: at rest (5 min), during the 6MWT (we discarded the initial 60 s of data and selected the most stable signal, corresponding to the latter portion of the test), and post-exercise recovery (5 min) with and without EPAP. The HRV signal collected in the time domain provided mean RR, STD RR, mean HR, STD HR, RMSSD, and RR tri index, in the frequency domain were LF, HF, and LF/HF ratio, and nonlinear analysis provided the ApEn and Shannon entropy indices.

da L. Goulart C., San Martin E.A., Mansour K.M., Schneiders P.B, & da Silva A.L. (2018). Influence of expiratory positive airway pressure on cardiac autonomic modulation at rest and in submaximal exercise in COPD patients. Brazilian Journal of Medical and Biological Research, 51(6), e7180.

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Heart Rate Variability Analysis During Sleep

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Electrocardiographic data were sampled at 256 Hz and a 5 min segment of ECG recordings from seizure-free epoch during NREM sleep was extracted. Signal processing was performed using the Kubios HRV analysis software, version 3.3. A built-in QRS detection algorithm based on the Pan–Tompkin's algorithm was applied to detect R-peaks and RRi was computed. Cubic spline interpolation at a rate of 4 Hz generated an equidistantly sampled RR time series. An automatic artifact correction algorithm was used to detect technical and physiological artifacts and remove ectopic/misplaced beats from the normal sinus rhythm. A detrending method based on smoothness prior to regularization with a cutoff frequency of <0.04 Hz was used to remove non-stationarities. Subsequently, fast-Fourier transformation separated the RR time series into its component frequencies, quantified as power spectral density (ms²), i.e., the area under the curve (AUC) in a given segment of the spectrum was estimated by Welch's periodogram method using a window width of 300 s with 50% overlap and a smoothing window of 0.02 Hz.

Goswami I., Maguire B., Chau V., Tam E.W., Pinchefsky E., Whitney R., Wilson D., Miller S.P, & Cortez M.A. (2022). Early transient dysautonomia predicts the risk of infantile epileptic spasm syndrome onset: A prospective cohort study. Frontiers in Neurology, 13, 1090155.

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Analyzing Heart Rate Variability Metrics

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According to the guidelines: Heart rate variability, Standards of measurement, physiological interpretation, and clinical use¹⁴, the HRV parameters were computed using same set of 24 h ambulatory electrocardiograms while calculating AC and DC. The quality control of the RR interval series was performed as AC/DC computation, that is, abnormal RR intervals, defined as RR intervals that change by more than 20% from the previous RR interval, were removed automatically; sinus arrhythmia and ventricular rhythm were further excluded manually according to the P waves morphology and QRS waves shape respectively. Indices of time-domain methods include the full-course standard deviation of NN (NN is used in place of RR to emphasize that the processed beats are normal beats) intervals (SDNN); root mean square of successive differences (RMSSD, which refers to the square root of the mean of the squares of the successive differences between adjacent NNs); indices of frequency-domain methods, which assign bands of frequency and then count the number of NN intervals that match each band, these include high frequency (HF), low frequency (LF) and the LF/HF; and average heart rate; were computed using Kubios HRV analysis software (http://kubios.uef.fi).

Chen Z., Yang Y., Zou C., Zhang Y., Huang X., Li X, & Yang X. (2018). Low heart deceleration capacity imply higher atrial fibrillation-free rate after ablation. Scientific Reports, 8, 5537.

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Measuring Autonomic Modulation During Musical Performance

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Performer electrocardiogram data was measured with a Polar 800 watch (PolarElectro Oy, Kempele, Finland) for 5 min before performances, during performance and for 5 min immediately post-performance. Its lack of interference with performance made it well-suited to explore MPA. The Polar 800 watch is a non-invasive monitoring device that provides continuous R-R interval data from which autonomic modulation of the heart was assessed. Heart rate variability (HRV) was assessed using Kubios HRV Analysis Software (version 3.0.1, Kuopio, Finland) for 5-min intervals at pre-performance, performance, and post-performance. These intervals were analyzed in 2.5-min segments, in order to detect short-term changes in cardiac-autonomic modulation.
Spectral analysis of the R-R interval segments produced power measures which were interpreted as follows: high frequency peak (HF; 0.15–0.4 Hz) representing primarily parasympathetic modulation, low frequency peak (LF; 0.04–0.15 Hz) reflecting both sympathetic and parasympathetic modulation (23 (link)). LF and HF power were each calculated in milliseconds² (ms²) and normalized units (nu) (24 (link)). The LF/HF ratio was used as a marker of the relative degree of sympathetic-cardiac modulation (24 (link)). Total power (milliseconds², ms²) was used as an indicator of the overall variability of the RR intervals.

Jha S., Stogios N., de Oliveira A.S., Thomas S, & Nolan R.P. (2022). Getting Into the Zone: A Pilot Study of Autonomic-Cardiac Modulation and Flow State During Piano Performance. Frontiers in Psychiatry, 13, 853733.

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Heart Rate Variability Protocol

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Heart rate data (OH1, Polar, Kempele, Finland) was collected in R–R interval mode for 4 min while the participant was lying quietly in the dedicated Rebalance© Impulse room. Data from the last 3 min of each sampling period were used for analysis, to allow the heart rate to stabilize. No particular breathing frequency was imposed (Saboul et al., 2013 (link)). For all HRV samples, it was subsequently verified that the respiration rate was always in the high-frequency range (0.15–0.50 Hz).
HRV data were analyzed using specialized HRV analysis Software (Kubios HRV analysis Software, Finland; Tarvainen et al., 2014 (link)). Data were processed by the same individual and visually inspected to identify artifacts and occasional ectopic beats, which were removed manually. The time-varying HRV indices kept for analysis were the root mean square difference of successive normal R–R intervals (RMSSD). The mean heart rate (HRmean) of the 3 min period was also recorded.

Hausswirth C., Nesi X., Dubois A., Duforez F., Rougier Y, & Slattery K. (2022). Four Weeks of a Neuro-Meditation Program Improves Sleep Quality and Reduces Hypertension in Nursing Staff During the COVID-19 Pandemic: A Parallel Randomized Controlled Trial. Frontiers in Psychology, 13, 854474.

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Automated HRV Data Processing Protocol

Cited 1 time

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HRV RR data, the length of time between normal successive heart beats, is transferred via Bluetooth® technology to the ActiLife software. It is then downloaded as a text file. The text file is saved as a CSV file and uploaded to Kubios HRV analysis software. Data is manually cleaned in the CSV file before uploading to Kubios. Participant information including age, height, weight, and sex is input in Kubios for accurate analysis. The final 5 minutes of the recording are used for analysis. Kubios uses a threshold-based artifact correction algorithm that compares every RR interval against a local average interval.³⁶ The correction level is adjusted for each participant because there are inter-individual differences in HRV, so using a fixed threshold does not work optimally for all participants.³⁶ The correction level chosen for each participant is the lowest level that correctly identifies artifact without over-correcting normal beats as artifact. Recordings with greater than 2% artifact are excluded from analysis because even a small number of edited RR intervals (<5%) can have a great influence on HRV results.^{37 (link)} Kubios utilizes Fast Fourier Transform to run frequency domain analyses.^{29 (link)} Research assistants spend an average of 15 to 20 minutes to score HRV data from each participant for the ongoing study.

Luong A., Goodyke M., Dunn S.L., Baynard T, & Bronas U. (2021). ActiGraph and Short-Term Heart Rate Variability Study Protocol: Amended for the COVID-19 Pandemic. The Journal of cardiovascular nursing, 36(6), 599-608.

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Cardiac Electrophysiology in Myocardial Ischemia-Reperfusion Injury

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The electrocardiographic signals were acquired by PowerLab standard leads II. Then using LabChart 8 to analyse the ST-segment displacement values and the low-frequency (LF,0.04–0.15 Hz) /high-frequency (HF,0.15–0.4 Hz) ratios (LF/HF), respectively, for the first 5 min of the preparation of the MIRI model in the rats, for the 30 min of myocardial ischemia, and for the 120 min of reperfusion. Heart rate variability (HRV) at 120 min of reperfusion was analyzed using the Kubios HRV analysis software.

Zhou J., Zhang B., Zhou X., Zhang F., Shu Q., Wu Y., Chang H.M., Hu L., Cai R.L, & Yu Q. (2024). Electroacupuncture pretreatment mediates sympathetic nerves to alleviate myocardial ischemia–reperfusion injury via CRH neurons in the paraventricular nucleus of the hypothalamus. Chinese Medicine, 19, 43.

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