Journal: Frontiers in Veterinary Science

Article Title: Measuring heart rate variability using a heart rate monitor in horses ( Equus caballus ) during groundwork

doi: 10.3389/fvets.2022.939534

Figure Lengend Snippet: Table representing the mean difference between the ECG and HRM and the standard deviation of the differences and the upper and lower limit of the 95% confidence interval, for the mean heart rate (HR) in beats per minute, and time-based parameters of heart rate variability (SDNN in ms and RMSSD in ms) during both the baseline conditions (B) and groundwork (GW) for the horses ( n = 28).

Article Snippet: Kruuse ECG Electrodes were connected to the Televet 100 (Engel Engineering, Heusenstamm, Germany) which was also attached to the girth to prevent movement of the equipment while the horse is walking, electrode location is shown in .

Techniques: Standard Deviation

Journal: Frontiers in Cardiovascular Medicine

Article Title: Alterations of sympathetic dynamics after atrial fibrillation ablation by analysis sympathetic nerve activity provide prognostic value for recurrence and mechanistic insights into ablation

doi: 10.3389/fcvm.2022.1024156

Figure Lengend Snippet: (A) The framework for skin nerve activity (SKNA) extraction: neuECG signal was originally recorded for 10 min sampling at 10,000 Hz and the neural activity signal was extracted by a 500-Hz high-pass filter and then a 1,000 Hz low-pass filter to remove the ECG signal. (B) Illustration of the extracted iSKNA (integrated SKNA) and aSKNA (averaged SKNA) signals: (1) the recorded neuECG signal; (2) the SKNA signal after high-pass filtering; (3) the iSKNA signal was derived by rectifying and summing up the SKNA signal with a non-overlapping 100 ms time window; (4) the iSKNA signal can be further averaged for different durations (e.g., 1, 5, or 10 s) to represent the dynamics of the sympathetic system interacting with the parasympathetic system for different time frames.

Article Snippet: The electrical signal on the skin was detected using standard ECG patch electrodes connected to a fully programmable amplifier (MP36, BIOPAC, USA).

Techniques: Activity Assay, Extraction, Sampling, Derivative Assay

Journal: Frontiers in Physiology

Article Title: ECG electrode localization using 3D visual reconstruction

doi: 10.3389/fphys.2025.1504319

Figure Lengend Snippet: ECG Electrode localization: (A) Example of YOLOv8 detection and classification of ECG electrodes in RGB image, where black is for class A, red for B, orange for C, and yellow for D; (B) Extraction of electrode locations as a 3D point cloud; (C) Alignment of all electrode point clouds; (D) Final 3D reconstruction of ECG electrodes; (E) Automated identification of the initial and secondary ECG electrodes in the RGB image; (F) Systematic Labeling and Arrangement of 3D ECG Electrode Positions.

Article Snippet: For example, in the case of CardioInsight, a deep learning model could be trained to detect regions corresponding to ECG electrodes, followed by an image processing algorithm or another deep learning model to identify and classify the numbers associated with each region.

Techniques: Extraction, Labeling

Journal: Frontiers in Physiology

Article Title: ECG electrode localization using 3D visual reconstruction

doi: 10.3389/fphys.2025.1504319

Figure Lengend Snippet: Flowchart outlining the algorithm to automatically label each ECG electrode channel.

Article Snippet: For example, in the case of CardioInsight, a deep learning model could be trained to detect regions corresponding to ECG electrodes, followed by an image processing algorithm or another deep learning model to identify and classify the numbers associated with each region.

Techniques:

Journal: Frontiers in Physiology

Article Title: ECG electrode localization using 3D visual reconstruction

doi: 10.3389/fphys.2025.1504319

Figure Lengend Snippet: Gold standard methods for ECG electrodes localization process using the CT-scan (top) or an Electromagnetic Tracking System (bottom): (A) 3D acquisition of the phantom via CT-scan; (B) Reconstruction of the phantom mesh using MusiCardio software; (C) Final 3D reconstruction of ECG electrodes from CT-scan; (D) Electromagnetic Tracking System and its electromagnetic field generation space; (E) Localization of electrodes on the phantom with the Electromagnetic Tracking System; (F) Final 3D reconstruction of ECG electrodes from the Electromagnetic Tracking System.

Article Snippet: For example, in the case of CardioInsight, a deep learning model could be trained to detect regions corresponding to ECG electrodes, followed by an image processing algorithm or another deep learning model to identify and classify the numbers associated with each region.

Techniques: Computed Tomography, Software