Hrv 2

Manufactured by Kubios

Sourced in Finland

Kubios HRV 2.2 software is a comprehensive tool for heart rate variability (HRV) analysis. The software provides advanced algorithms and tools for processing and analyzing HRV data from various sources, including ECG, photoplethysmography (PPG), and other biosignal recordings.

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Lab products found in correlation

Vevo 2100, by Fujifilm (1 mentions) Vevo software, by Fujifilm (1 mentions) Ms 400, by Fujifilm (1 mentions) Vevolab software, by Fujifilm (1 mentions) Polar v800 heart rate monitor, by Polar Electro (1 mentions) Polar v800, by Polar Electro (1 mentions)

7 protocols using hrv 2

Multimodal Recording of Cardiac and Respiratory Responses

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We trimmed the first/last 30 s to remove setting-related artifacts (e.g., posture adjustments by participants at the beginning or end of recording), and pre-processed the middle 5 min ECG recording sections using Kubios HRV 2.1 (Tarvainen et al., 2014 (link)). About 5% of participants showed significant posture adjustment-related artifacts in the beginning or end of recordings. For another 5%, the research assistant made an error in starting the recording late. So we chose to trim all recordings at the ends to extract the same length of 5 min recording. After identifying RR-time series via Kubios QRS-detection algorithm, a trained research assistant manually inspected recordings and corrected for missed artifacts within Kubios when necessary. Overall, the rate of false beats in the raw 5 min ECG recordings was low and varied from recording to recording (0–5% range).
Respiration was measured using a respiration belt and Biopac wireless BioNomadix module for concurrent ECG and respiration recordings. Respiration analyses were conducted using the Acqknowlege 4.2 Software. Each recording was also examined by a trained research assistant (who manually calculated a number of cycles per second) to double-check the software-aided respiration frequency calculation.

Grossmann I., Sahdra B.K, & Ciarrochi J. (2016). A Heart and A Mind: Self-distancing Facilitates the Association Between Heart Rate Variability, and Wise Reasoning. Frontiers in Behavioral Neuroscience, 10, 68.

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Cardiac Measures and Physiological Complexity

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Four cardiac measures were considered (see Camm et al., 1996) : the mean heart rate, the square root of the mean of the squares of the successive differences between adjacent NN peaks (rMSSD), the high-frequency band (.15-.40 Hz) spectral power, in log-linear scale (lnHF); and sample entropy (SampEn; Richman and Moorman, 2000) .
Heart rate has been extensively associated with parasympathetic activity (Beauchaine and Thayer, 2015) (link). SampEn is a measure of chaotic irregularity or complexity of a biological system over time (the higher the SampEn the more complex the behaviour of this system). Cardiac measures were calculated by means of Kubios HRV 2.1 (Tarvainen et al., 2014) (link).

de la Torre-Luque A., Fiol-Veny A., Essau C.A., Balle M, & Bornas X. (2020). Effects of a transdiagnostic cognitive behaviour therapy-based programme on the natural course of anxiety symptoms in adolescence. Journal of affective disorders, 264.

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Echocardiography and ECG in Mice

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Transthoracic echocardiography was performed on healthy mice and before the sacrification (day 7 or day 35). Mice were anesthetized with isoflurane and imaged using a high-frequency, high-resolution imaging system for small animals (Vevo 2100, VisualSonics, Toronto, Canada) equipped with a transducer probe operating at 18–38 MHz (MS-400, VisualSonics). In addition, surface electrocardiography (ECG) signal was acquired during echocardiography. The paws of the mice were attached to the electrode pads of the heated platform (36–37 °C). ECG data were exported from Vevo software (VisualSonics) and analyzed with rodent ECG imaging software (Kubios HRV 2.0, Kuopio, Finland) as previously^{18 (link)}.

Vuorio T., Ylä-Herttuala E., Laakkonen J.P., Laidinen S., Liimatainen T, & Ylä-Herttuala S. (2018). Downregulation of VEGFR3 signaling alters cardiac lymphatic vessel organization and leads to a higher mortality after acute myocardial infarction. Scientific Reports, 8, 16709.

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Echocardiographic Assessment of Cardiac Function

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High-resolution transthoracic echocardiographic measurements were performed using VEVO2100 Ultrasound System for small animals (Fujifilm VisualSonics Inc., Toronto, ON, Canada) using a high-frequency ultrasound probe (MS400) operating at 18–38 MHz. Mice were anesthetised with isoflurane induction: 4.5% isoflurane, 450 mL air and maintenance; 2.0% isoflurane, 200 mL air (Baxter 28 International Inc., Deerfield, IL, USA). LV dimensions and wall thickness were measured from the parasternal short axis M-Mode images using papillary muscles as an indication of mid-ventricular level. LV systolic function was measured by using Teicholz method and diastolic function by measuring the LV relaxation time starting from the peak systole and ending when the maximum relaxation of LV posterior wall was achieved. The analysis was conducted from three consecutive cardiac cycles using VEVO Lab software (Fujifilm VisualSonics Inc., ON, Canada). In addition, ECG signal was acquired via limb electrodes during echocardiography and data were analysed with rodent ECG imaging software (Kubios HRV 2.0, Kuopio, Finland) as previously described [15 (link)].

Tirronen A., Downes N.L., Huusko J., Laakkonen J.P., Tuomainen T., Tavi P., Hedman M, & Ylä-Herttuala S. (2021). The Ablation of VEGFR-1 Signaling Promotes Pressure Overload-Induced Cardiac Dysfunction and Sudden Death. Biomolecules, 11(3), 452.

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Analyzing Heart Rate Variability in Animals

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Stable ECG recordings taken at 3 minutes every 2 hours for each animal were exported from Dataquest A.R.T. 4.3. We analyzed HR variability with Kubios HRV 2.2 software.16 (link) Powers (in square milliseconds) for low frequency and high frequency were used for 0.15- to 1.5-Hz bands and 1.5- to 5.0-Hz bands, respectively.17 (link) The low- and high-frequency power and the ratio of low/high-frequency power were calculated for the 12-hour light/dark period for each animal.

Batchu S.N., Smolock E.M., Dyachenko I.A., Murashev A.N, & Korshunov V.A. (2015). Autonomic Dysfunction Determines Stress-Induced Cardiovascular and Immune Complications in Mice. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 4(5), e001952.

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Heart Rate Variability in Emotion Regulation

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Emotion dysregulation was measured by comparing HRV prior to (resting baseline) and during (exposure) a stress challenge task (i.e., Dysregulation index=Baseline – Exposure). Prior to monitoring, participants chose 12 of 60 words that were rated as most relevant to their trauma. A BioHarness strap and sensor (Zephyr Performance Systems) was positioned tightly across the chest to monitor the electrocardiogram using OmniSense 5.1 software. Monitoring commenced at baseline, with participants sitting at rest wearing a headset without audio recording and watching a slideshow of generic nature pictures for three minutes. During the 3-minute exposure task, participants closed their eyes to focus on each of the selected words, which were randomly repeated three times. Kubios HRV 2.2 software was used to analyze the electrocardiogram data using the time-domain method. The root mean square of successive difference (RMSSD) was used to measure short-term variability (Schaffer & Ginsberg, 2017 (link)). RMSSD is an appropriate HRV index related to emotion regulation (Godfrey et al., 2019 (link)) and psychophysiology (Bigger et al., 1988 (link); Owen & Steptoe, 2003 (link)), is preferred for its statistical characteristics (Buccelletti, et al., 2009 (link)), and accurately represents parasympathetic activity (Chalmers et al., 2014 (link)).

Mathew A.S., Lotfi S., Bennett K.P., Larsen S.E., Dean C., Larson C.L, & Lee H.J. (2021). Association between Spatial Working Memory and Re-experiencing Symptoms in PTSD. Journal of behavior therapy and experimental psychiatry, 75, 101714.

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Heart Rate Variability Following Maximal Exercise

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HRV parameters were collected at baseline, immediately after the maximal graded test, 24 h and 48 h after the end of maximal effort test. Heart rate recovery was measured at 2 min (HRR2) after the end of the test. These measurements were performed with the subjects in a supine position on an examination table for 10 min, during which RR interval recordings were acquired using a portable heart rate monitor (Polar V800, Polar, Finland). The last 5 min of the RR recording were analysed by means of Kubios HRV 2.2 software [24 (link)]. HRV time and frequency domains were retrieved [25 (link)]. The mean squared differences of successive RR intervals (rMSSD) were retained for the time domain, while the very low frequency (VLF < 0.04 Hz), low frequency (LF from 0.04 to 0.15 Hz) and high frequency (HF from 0.15 to 0.40 Hz) components, in absolute (ms2) and in normalized units (nu) [LFnu: 100 x LF / (total power – VLF), and HFnu: 100 x HF / (total power - VLF)] were retained for the frequency domain. From the values of LF and HF, the LF/HF ratio was determined [26 ]. The validity of the HRV procedure derived from V800 Polar heart rate monitor is reported elsewhere [27 (link)].

Bellafiore M., Pintaudi A.M., Thomas E., Tesoriere L., Bianco A., Cataldo A., Cerasola D., Traina M., Livrea M.A, & Palma A. (2021). Redox and autonomic responses to acute exercise-post recovery following Opuntia ficus-indica juice intake in physically active women. Journal of the International Society of Sports Nutrition, 18, 43.

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