RR data .txt files were exported from the Elite HRV app then processed by Kubios HRV Premium Software version 3.5 (Biosignal Analysis and Medical Imaging Group, Department of Physics, University of Kuopio, Kuopio, Finland). Movesense Medical sensor ECG tracings were recorded by the Movesense showcase app via an iPhone, converted into .csv files and also processed by Kubios HRV Premium. Preprocessing settings were set to the default values including the RR detrending method which was kept at “smoothness priors” (Lambda = 500). The RR series was then corrected by the Kubios HRV Premium “automatic method” [19 (link)]. For RF calculation, the window width was set to 30 s with a recalculation done every 1 s (grid interval = 1 s). Data sets with artefacts >3% were excluded from analysis. A 30 s window was based on recommendations from Kubios HRV [16 ]. A particular RF value was therefore based on the time 15 s before and 15 s after each given time stamp. The reference RF measured by the Quark CPET (breath by breath) was exported to Microsoft Excel 365 and time aligned with both the Polar H10 and Movesense Medical ECG sensor-derived RF. Since both the Polar H10 and Movesense Medical sensor ECG RF were recalculated every 1 s for both devices, only those values that time matched the gas exchange RF values were included for analysis.
Hrv premium
Manufactured by Kubios
Sourced in Finland
Kubios HRV Premium is a software application designed for heart rate variability (HRV) analysis. It provides advanced tools for processing and analyzing HRV data from various sources, including wearable devices and research-grade equipment.
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Lab products found in correlation
13 protocols using hrv premium
1
Validating Respiratory Rate Sensors
2
Heart Rate Variability Analysis in Stroop Test
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Recorded R–R intervals were analyzed offline using Kubios HRV Premium software (Kubios Oy, Kuopio, Finland) using 2-min intervals based on recommendations from published standards (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996 (link)). Frequency domain measures analyze the power distribution of HRV as a function of high frequency (HF) and low frequency (LF). HF and LF components are reflective of parasympathetic and sympathetic activation, respectively. LF/HF can be regarded as the overall sympathovagal balance and degree of autonomic arousal (Pagani et al., 1986 (link); Shaffer and Ginsberg, 2017 (link)).
Heart rate variability recordings during Stroop test were processed by Kubios HRV Premium. Automated artifact correction was performed for all recordings prior to analysis. One-hundred twenty second sampling periods were utilized to derive LF, HF, and LF/HF using Fast Fourier transformation spectrum method (Figure 1 ). LF and HF bands were standardly defined as 0.04–0.15 and 0.15–0.4 Hz, respectively, and absolute power for each band was analyzed in normalized units, LF or HF divided by total power (Malliani et al., 1994 (link)).
Heart rate variability recordings during Stroop test were processed by Kubios HRV Premium. Automated artifact correction was performed for all recordings prior to analysis. One-hundred twenty second sampling periods were utilized to derive LF, HF, and LF/HF using Fast Fourier transformation spectrum method (
3
Equine Heart Rate Variability Analysis
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In total, 14 ECG recordings were obtained, with one recording per device per horse. Raw data were exported from both devices and imported into an HRV software (Kubios HRV Premium, version 3.5.0) for analysis. Data were not filtered during analysis. Automatically identified R-peaks and data from the same channel were manually assessed by one operator to correct any misidentified or missed peaks. The data were segmented to obtain HR and HRV metrics for rest, walk, trot, canter, and post-exercise walk individually.
Time-domain metrics were determined for each device and activity, including mean heart rate (HR), the standard deviation of R-R intervals (SDRR), the HRV triangular index (the integral of the density of the R-R interval histogram divided by its height—HRV TI), the triangular interpolation of R-R interval histogram (TIRR), and the root mean square of successive R-R interval differences (RMSSDs). Additionally, nonlinear measurements were obtained from the Poincaré plot by fitting an ellipse to the plotted points. The standard deviation perpendicular to the line of identity (SD1) and the standard deviation along the line of identity (SD2) of the Poincaré plot were determined, where SD1 correlates with short-term HRV and SD2 correlates with long-term HRV [9 (link),12 (link),21 (link)].
Time-domain metrics were determined for each device and activity, including mean heart rate (HR), the standard deviation of R-R intervals (SDRR), the HRV triangular index (the integral of the density of the R-R interval histogram divided by its height—HRV TI), the triangular interpolation of R-R interval histogram (TIRR), and the root mean square of successive R-R interval differences (RMSSDs). Additionally, nonlinear measurements were obtained from the Poincaré plot by fitting an ellipse to the plotted points. The standard deviation perpendicular to the line of identity (SD1) and the standard deviation along the line of identity (SD2) of the Poincaré plot were determined, where SD1 correlates with short-term HRV and SD2 correlates with long-term HRV [9 (link),12 (link),21 (link)].
4
Comparing Wearable Devices for Heart Rate Variability
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Raw mECG waveform data (from the Shimmer) were analyzed via Kubios HRV Premium (V3.2.0, Kuopio, Finland) to enable time segmenting, such that comparisons were directly aligned (using the manually recorded timestamps mentioned above) with data exports obtained from each of the COTS devices (Tarvainen et al., 2014 (link)). For the measurement of rMSSD from the raw Shimmer signal, data processing was executed within Kubios to enable direct (synchronized with COTS devices) comparisons. Since the HRV analyses primarily focused on rMSSD (i.e., autonomic activity), very low frequency trend components were removed by using the Smoothing priors method. The smoothing parameter was set to λ = 400, which corresponds to a cut-off frequency of 0039 Hz (below the low-frequency band). Finally, inter-beat intervals (IBIs) were extracted from the R-R temporal differences throughout each recording session. For each device, rMSSD was calculated as the square root of the mean squared differences between successive RR intervals for the specified recording period. In summary, HR and rMSSD values from COTS devices were obtained from their companion applications whereas the same metrics derived from the Shimmer signal were analyzed in and extracted from Kubios.
5
ECG Data Preprocessing for HRV Analysis
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According to the database information20 (link)–22 , the ECG recordings were obtained from either MP150 (ECG100C, BIOPAC systems inc., Golata, CA, USA) or Task Force Monitor system (CN Systems Medizintechnik GmbH, Graz AUT). The ECG recordings were visually inspected by two of our trained observers and the correct identification of R waves was visually supervised in the Kubios HRV premium software27 (link). After correction of misidentifications, artifacts, and identification of arrhythmias, an automatic beat correction algorithm3 (link) was applied to correct any remaining ectopic heartbeats that were replaced by interpolated RR values. Most of the RR time series did not require any ectopy correction, heartbeat interpolation was applied only in 15 recordings (1.5%), and the percentage of heartbeats corrected by the automatic beat correction algorithm was within 0.55% and 1.24%.
6
Normality Tests and Statistical Analyses of Autonomic and Gastrointestinal Parameters
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Normality tests provided by Prism (9.4.1 GraphPad Software, LLC) were performed for all data (D'Agostino-Pearson omnibus, Anderson-Darling, Shapiro-Wilk, and the Kolmogorov-Smirnov normality test with Dallal-Wilkinson-Lillie). HRV parameters were extracted using Kubios HRV Premium, Version 3.2.0. Parameters that passed normality tests including SI and water intake were arranged for a repeated-measures one-way ANOVA with a four-bythree design (four-condition: 0-Hz nMNS (placebo) 40-Hz nMNS, 80-Hz nMNS, 120-Hz nMNS; three-stage: resting baseline, stimulation, and poststimulation). Because EGG data had only 17 values owing to server communication error, a repeated-measures mixed-effects ANOVA was used instead. All previously mentioned data underwent a post hoc Tukey multiple comparison test with the Geisser-Greenhouse correction for nonsphericity of data. Parameters that failed normality tests included RMSSD and underwent a repeatedmeasures Freidman test with a post hoc Dunn multiple comparison test. Delta (Δ) represents the "baseline to post-stim" difference of a parameter for each stimulation (eg, Δfrequency represents the baseline to poststimulation difference of the GSW frequency, and this is calculated for each stimulation modality), and Δ comparisons in between groups are defined as delta-delta difference and represented as ΔΔ (eg, ΔΔ frequency).
7
Perioperative Anxiety and Pain Assessment
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Clinical data and operation type will be recorded (Supplementary data). Participants will complete a pain score assessment (VAS) 10 min before and after the intervention period. Anxiety, an established modifier of pain severity23 (link) and common after surgery,24 (link) will be assessed using the General Anxiety Disorder-7 (GAD-7) questionnaire at the time of intervention before and after surgery. In addition, the Amsterdam Preoperative Anxiety and Information Scale25 (link) will also be presented for each intervention group but not analysed. HR data will be analysed using Kubios HRV Premium (version 3.5.0, Kuopio, Finland).26 (link) Plasma will be stored at –80°C having been obtained after both intervention periods for exploratory analyses of biomarkers related to vagal activation. Postoperative morbidity data, as defined by POMS,21 (link) will be collected by a member of the admitting surgical team and analysed by an independent member of the research team; both will be masked to the treatment allocation. Data will be entered electronically on a secure database (Research Electronic Data Capture).27 (link)
8
Wearable Sensor Accuracy: ECG Comparison
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Raw ECG was processed using the clinical standard, Kubios HRV Premium (version 3.3) to extract RR intervals and HR. Differences between the ECG and each wearable sensor were calculated at each matched timestamp for each wearable sensor for each participant. Both relative and absolute differences were calculated as shown in Eqs. (6 ) and (7 ).
9
HRV Analysis of ECG Recordings
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Raw ECG data were processed utilizing the Kubios HRV Premium software (Version 3.1.0, Kubios Oy, Finland) to generate HRV parameters. Frequency domain activity was calculated Welch's periodogram method (Welch, 1967 (link)) for the following HRV frequency bands: low frequency (LF) power (0.04–0.15 Hz), high frequency (HF) power (0.15–0.4 Hz), and total power (TP), as well as the LF:HF ratio. Additionally, time domain activity was calculated for the standard deviation of NN intervals (SDNN). It should be noted that in the process of deriving these variables, both visual and automatic artifact correction process was undertaken (Tarvainen et al., 2014 (link)), as well as the Smoothn Priors method (Tarvainen et al., 2002 (link)) of trend component rejection were utilized, and HRV data were log‐transformed prior to analysis where relevant.
10
Heart Rate Variability Analysis Protocol
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Heart rate variability (HRV) was recorded before testing the thermal detection and pain thresholds with eMotion Faros 180° (Mega Electronics Ltd., Kuopio, Finland). HRV data analysis was performed with Kubios HRV Premium (ver. 3.3.1, Kubios Ltd., Kuopio, Finland). HRV was assessed during a five-minute sitting resting period in which participants were verbally instructed to relax and not talk or move (as described in [49 (link)]). The data were collected with a sampling rate of 1000 Hz and saved on the device for offline analysis. For artifact correction, the automatic correction algorithm of Kubios software was used, which was shown to reliably correct for artifacts in recorded HRV data with high sensitivity and specificity [50 (link)]. The parameters of interest were the root mean square of successive differences (RMSSD) as a primary time-domain index for measuring overall short-term HRV as well as frequency-domain indices, i.e., the low-frequency (LF) band (0.04–0.15 Hz) and high-frequency (HF) band (0.15–0.40), reflecting SNS and PNS influences via baroreceptor activity and parasympathetic activity, respectively [51 (link),52 (link)].
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