After extraction of the 12-channel ECG data (converted from exported.xml files) and RR data (exported from the Elite HRV app) as text files, import into Kubios HRV Premium Software version 3.5.0 (Biosignal Analysis and Medical Imaging Group, Department of Physics, University of Kuopio, Kuopio, Finland, [19 (link)]) was conducted. For the 12-channel ECG, lead 2 was used as the comparable lead to the chest strap device [21 ]. 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” [22 (link)]. For DFA a1 calculation window width was set to 4 ≤ n ≤ 16 beats [23 (link)]. During rest conditions a 2-min time window (00:30–02:30 min:s) was chosen for the analysis. During the incremental exercise time-varying analysis was adjusted to a 2-min window width and 20-s grid interval for the moving window, so that the exported.csv files from Kubios HRV Premium Software contained the HRV metrics of interest (RR, HR, DFA a1) recalculated every 20 s. Data sets with artefacts >5% were excluded from analysis based on [24 (link)].
Hrv premium software
Manufactured by Kubios
Sourced in Finland
HRV Premium software is a comprehensive heart rate variability (HRV) analysis tool. It provides advanced features for capturing, processing, and analyzing HRV data from various sources. The software offers a range of analytical tools and visualization options to facilitate in-depth exploration of HRV patterns and trends.
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15 protocols using hrv premium software
1
ECG Analysis using Kubios HRV Premium
2
ECG Signal Acquisition and Analysis
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Electrocardiogram (ECG) signals were measured using the ProComp Infiniti system (SA7500, Thought Technology, Canada). Three Ag/AgCl electrodes were attached on the wrists and an ankle in a lead II configuration. The measured signal was amplified, band-pass filtered, and sampled at 256 Hz. We used Kubios HRV Premium software (Kubios, www.kubios.com) to analyze the ECG signals. This system implements an in-house QRS detection algorithm based on the Pan-Tompkins method [53 , 54 ]. All detected R-peaks were inspected by the same operator to maintain consistency. A piecewise cubic spline interpolation method was used to correct artifacts. Finally, the entropy features were calculated separately from the individual phases using the RRI data.
3
Heart Rate Variability Analysis Protocol
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The Pan-Tompkins algorithm was used to extract the time series of the RRI. The artifacts caused by interference and ectopic heartbeat were corrected by a time-varying threshold algorithm, and then HRV time and frequency domain analysis was carried out.
The time-domain indices included the standard deviation of all normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD).
The frequency-domain indices included low-frequency power (LF, 0.04–0.15 Hz), high-frequency power (HF, 0.15–0.4 Hz), and the ratio of LF to HF (LF/HF).
The above analysis was performed with Kubios HRV Premium software (version 3.1.0,https://www.kubios.com , Kubios Oy, Kuopio, Finland).
The time-domain indices included the standard deviation of all normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD).
The frequency-domain indices included low-frequency power (LF, 0.04–0.15 Hz), high-frequency power (HF, 0.15–0.4 Hz), and the ratio of LF to HF (LF/HF).
The above analysis was performed with Kubios HRV Premium software (version 3.1.0,
4
Resting-state fMRI and Heart Rate Variability
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Photoplethysmography (PPG) recordings were performed simultaneous to the restingstate fMRI recording by means of a wireless Peripheral Pulse Unit MRI Sensor (Philips; sampling rate: 500 Hz). The PPG sensor was placed over the index finger of the non-dominant hand to monitor blood volume changes in the microvascular bed of the underlying tissue. The time intervals between blood volume pulse waves were assessed using Kubios HRV Premium software (version 3.5.0) [46] to derive continuous inter-beat-intervals for assessing heart rate variability (HRV). All inter-beat-interval time series were manually inspected prior to analysis and automatic artifact removal, as implemented in Kubios, was performed. PPG recordings were available for 43 participants (20 oxytocin/23 placebo) at T1 and for 40 participants (18 oxytocin/22 placebo) at T2. Upon artefact removal, 4 additional participants (3 oxytocin/1 placebo) were removed from the final analyses (availability of less than 5 min of noise-free data and/or >5% ectopic beats [47] ). For each of the remaining subjects, HRV frequency domain analyses were performed using Fast Fourier Transformation based on Welch's periodogram. Percentage power values were computed for the high-frequency component of the HRV signal, which is defined between 0.24 -1.04 Hz in children [48] .
5
Effects of Live Bedside Music on Physiological Outcomes
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Statistical analyses will be performed using IBM SPSS Statistics version 23 (IBM Corporation, Armonk, NY).
Outliers will be detected visually with parametric and nonparametric tests. Endpoints will be assessed on distribution using Q-Q plots and the Shapiro-Wilk test (p > 0.05). Data will be presented as the mean and standard deviation if normally distributed or as the median and range. Numbers and percentages will be used for categorical data. Analyses will be performed in both groups as well as between the live bedside music group and the control group. The chi-squared test will be used for categorical data. To determine an effect between the pre-test and post-test or follow-up test, a paired t-test will be used; the Wilcoxon signed-rank test will be used if the data is not normally distributed. To determine a difference between the control group and the live bedside music group, we will use an independent t-test or ANOVA if the data are normally distributed and the Mann-Whitney U test if not. Data on HRV, measured using the HeartMath emWave 2, will be analysed in both time and frequency domains using Kubios HRV Premium software [23] . Automatic correction to the measurements will be applied to remove artefacts, such as extra systoles.
Outliers will be detected visually with parametric and nonparametric tests. Endpoints will be assessed on distribution using Q-Q plots and the Shapiro-Wilk test (p > 0.05). Data will be presented as the mean and standard deviation if normally distributed or as the median and range. Numbers and percentages will be used for categorical data. Analyses will be performed in both groups as well as between the live bedside music group and the control group. The chi-squared test will be used for categorical data. To determine an effect between the pre-test and post-test or follow-up test, a paired t-test will be used; the Wilcoxon signed-rank test will be used if the data is not normally distributed. To determine a difference between the control group and the live bedside music group, we will use an independent t-test or ANOVA if the data are normally distributed and the Mann-Whitney U test if not. Data on HRV, measured using the HeartMath emWave 2, will be analysed in both time and frequency domains using Kubios HRV Premium software [23] . Automatic correction to the measurements will be applied to remove artefacts, such as extra systoles.
6
Preprocessing of One-channel ECG Recordings
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One-channel ECG recordings were obtained with BioHarness 3.0 (Zephyr technology) at a sampling frequency of 250 samples per second. We performed R wave identification and visual supervision using Kubios HRV premium software. The identification of arrhythmias and artifacts in ECG recordings was visually supervised by a trained physician. Sporadic ventricular extrasystoles were found in two recordings, a correction algorithm was used to substitute extrasystoles [8 (link)]. Less than 5% of heartbeats were replaced, as recommended in the HRV analysis guidelines [1 (link)]. We named the final HRV time series as the NN intervals [1 (link)] (distance between one R wave and the next R wave in ECG recording), referring to the time between "normal" heartbeats.
7
Resting ECG and Cold Stress
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After a 5-min resting period, subjects received the 3-leads ECG recording in the sitting position for 1 min. After the 1 min cold stimulation, the same ECG process was repeated. The R-R peak was extracted and analyzed by the Kubios HRV Premium software (version 2.2; University of Eastern Finland). The index of Low frequency divides high frequency (LF/HF index) was calculated.
All measurements were taken by two observers blinded. Cases of a discrepancy of >15% were resolved by consulting the senior-most author. All data were recorded and stored for later statistical analyses.
All measurements were taken by two observers blinded. Cases of a discrepancy of >15% were resolved by consulting the senior-most author. All data were recorded and stored for later statistical analyses.
8
Automated Heart Rate Variability Analysis
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All measurements were retrieved using Kubios HRV Premium software (Kubios Oy, Kuopio, Finland). The Kubios software derives the time domain measurements from the beat-to-beat RR intervals values. The frequency domain measurements are determined with the Fast Fourier Transformation (FFT) and the parametric autoregressive (AR) modeling [39 ]. The automatic beat correction application in the software was used to remove outliers. All measurements were analyzed in 5-minute windows according to the protocols set forth by the Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology [38 ]. The windows used for statistical analysis were the last 5 minutes of each recording period.
9
Automated Heart Rate Variability Analysis
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The recorded ECG data was processed utilising the Kubios HRV Premium software (Version 3.1.0, Kubios Oy, Kuopio, Finland) to derive the activity for the following HRV frequency bands: low frequency (LF; 0.04–0.15 Hz) and high frequency (HF; 0.15–0.4 Hz). The automatic artefact correction process built into the Kubios software [50 ], as well as the Smoothn Priors method [51 (link)] of trend component rejection were utilised to correct any artefacts and ectopic beats present in the raw ECG recordings.
All variables were derived from approximately 10 min of ECG [52 (link)], utilising Welch’s periodogram method [53 (link)] applied to a 300 s window with a 50% overlap. Further, it should be noted that for each of these variables raw power units (ms2) as well as natural log transformed (ln) and normalised units (n.u.) were utilised. Additionally, the ratio between LF and HF (LF/HF) as well as overall HRV power or total power (TPow) was also computed.
All variables were derived from approximately 10 min of ECG [52 (link)], utilising Welch’s periodogram method [53 (link)] applied to a 300 s window with a 50% overlap. Further, it should be noted that for each of these variables raw power units (ms2) as well as natural log transformed (ln) and normalised units (n.u.) were utilised. Additionally, the ratio between LF and HF (LF/HF) as well as overall HRV power or total power (TPow) was also computed.
10
Heart Rate Variability Analysis in REM Sleep
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HRV was calculated in continuous ECG segments of ≥5 minutes within REM using Kubios HRV premium software (Kubios Oy, Kuopio, Finland). Prior to analysis, ECG traces were visually inspected for physiological and technical artifacts (Laborde et al. 2017 (link)). Misplaced or ectopic beats were manually corrected if possible. Very low to low, or occasionally medium, filter was applied, if necessary, after correction (https://www.kubios.com/hrv-preprocessing/ ). Segments that did not provide reliable estimates due to excessive artifacts were removed. Twenty-three participants were excluded because of artifacts, not having ≥5 minute REM segments, or because their records were missing. High frequency (HF; .15-.4 Hz) absolute power (HF[ms2]) was used as a predictor variable in the primary analyses because it was shown to be associated with sleep-dependent memory processing (Whitehurst et al. 2016 (link)). We also carried out the same analyses with RMSSD, another measure of vagal activity (Shaffer and Ginsberg 2017 (link)). For frequency domain analyses, autoregressive method was used, with model order set at 16 (Laborde et al. 2017 (link)). Data was transformed by their natural logarithm (Laborde et al. 2017 (link)).
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