Introduction: The objective definition of the normal ECG waveform is difficult, even 121 years after Einthoven’s invention of the ECG. Consequently, ECG evaluation is primarily focused on interval measurements. For the waveforms, many rules and exceptions apply, making the interpretation of the ECG waveforms an art. In this study, a novel method is proposed that compares ECG waveforms with a distribution of normal ECG waveforms (WaveECG) and vectorcardiographic-derived positions of the electrical activity (PathECG) in order to determine the pathological status of the ECG. Methods: Human-annotated ECGs from the PhysioNet PTB-XL Diagnostic ECG database were used. ECGs classified as normal were used to create gender-specific distributions of the normal WaveECG and PathECG. The similarity between the evaluated ECG and the distributions was then quantified. Logistic regression was used to examine the potential of PathECG and WaveECG in the prediction of an abnormal ECG waveform. The independent China Physiological Signal Challenge database was used for validation. Results: A total of 14,381 ECGs were used to construct the distribution of the normal WaveECG and PathECG and to fit the model, and 6,320 independent ECGs were used for validation. The QRS model performance had an area under the receiver operating characteristic curve of 82.1% (95% CI [80.3–84%]) for male patients and 86.1% (95% CI [84.7–87.6%]) for female patients. The area under the receiver operating characteristic curve of the P wave model was 65.4% (95% CI [62.8–68.0%]) for female patients and 66.8% (95% CI [63.9–69.6%]) for male patients. Conclusion: An abnormal ECG can be detected in an explainable and visually understandable way by comparing the ECG to the distribution of normal ECGs. This method might significantly support the less experienced ECG reader in identifying patients requiring cardiac care.

Deep learning has become central to semantic segmentation of three-dimensional medical images. However—despite many published models—their adoption in practice remains limited, as deployment often requires advanced programming skills and familiarity with specific machine learning frameworks. Thus, technical barriers restrict its use to specialized users. To address this, we present SegMed (version 1.0), an open-source, standalone desktop application that provides an end-to-end workflow for deep learning-based medical image segmentation. SegMed supports the loading and inspection of common medical image formats, as well as array-based formats. The application integrates standard preprocessing operations often used in the field and directly supports loading of pretrained segmentation models implemented in both PyTorch (version 2.X) and Keras (version 2.X) and those created using the Medical Open Network for AI framework (version 1.X). Models are automatically inspected to infer required configurations, such as input size and post-processing steps, enabling segmentation with minimal user intervention. Results can be exported as volumetric images or 3D surface meshes for downstream analysis, visualization, or special applications such as virtual reality. SegMed was tested using multiple publicly available pretrained models, demonstrating robustness and flexibility across diverse segmentation tasks. By abstracting low-level implementation details, SegMed lowers technical barriers, promotes reproducibility, and facilitates the integration of AI-assisted segmentation into medical imaging workflows.

Electrocardiography (ECG) is a vital tool for monitoring equine health, particularly in racehorses, where it can be used to assist in evaluating recovery capacity and overall fitness as well as capturing arrhythmia. With the rapid advancement of commercial biosensor modules, the development of low-cost, portable ECG systems has become increasingly feasible. This paper presents the design and evaluation of an equine Holter ECG prototype built using an Arduino-based system for recording and storing cardiac signals. The prototype was tested on a single healthy horse at rest, using varying sampling frequencies $(250 \text{Hz}, 500 \text{Hz}$, and 1000 Hz) and digital filters with different cutoff frequencies. The recorded signals were analyzed to assess both technical metrics, using signal-to-noise ratio (SNR), and clinical features. Results showed a consistently high SNR (greater than 16 dB) across all technical configurations. Clinical parameters did not differ significantly from reference values in the literature $(\mathrm{p} {>} 0.05)$, indicating good alignment. These findings demonstrate the feasibility of affordable, Arduino-based systems for equine ECG monitoring, and while the study was limited to a single horse, it provides a useful reference for selecting optimal sampling and filter parameters in future research.

BACKGROUND:The equivalent dipole layer (EDL) relates local endocardial and epicardial transmembrane potentials to body surface potentials and can therefore be used to gain insight into cardiac activation and recovery. To use the EDL-source model for the inverse problem of electrocardiography, initial estimates for local activation times (LAT) and recovery times (LRT) are required because of its non-linear relation with body surface potentials. OBJECTIVE:To develop an AT-independent initial RT estimate in the EDL-source model. METHODS:Body surface mapping (BSM) and cardiac imaging were performed in 15 subjects. LAT and LRT were estimated using the EDL-source model. Various ventricular recovery patterns were tested to investigate the relation between recovery patterns and normal T-waves, including LAT-dependent-recovery and RT differences along transmural, interventricular, anterior-posterior and apico-basal axes. A new algorithm was developed based on the backwards modeling of the T-wave (BackRep) to identify the latest area of recovery. Correlation coefficient (CC) and relative difference (RD) between the recorded and computed T-waves were reported. RESULTS:BackRep (CC = 0.89 [IQR:0.83-0.90]; RD = 0.63 [IQR:0.49-0.69]), outperformed the anatomical axes based recovery patterns (CC = 0.29 [IQR:0.21-0.46] - 0.79 [IQR:0.78-0.83]; RD = 1.02 [IQR:0.98-1.18] - 0.61 [IQR:0.57-0.68]) and LAT-based recovery pattern (CC = 0.63 [IQR:0.60-0.73]; RD = 4.35 [IQR:2.74-9.05]). Of the RT differences along the anatomical axes, the apico-basal recovery pattern showed the best match between recorded and computed T-waves. A significant apex-to-base RT difference was also found in the BackRep recovery maps. CONCLUSION:BackRep provides a reliable AT-independent initial RT estimate and supports the presence of an apex-to-base RT difference in normal T-wave morphology.

BACKGROUND Body surface potential mapping (BSPM) can provide a detailed assessment of cardiac electrical activity and might be of potential added benefit in multiple cardiac diseases. Normal intra-and interpersonal variation in BSPM is not clearly described and could be of use in the distinction between normal variation and cardiac disease development. OBJECTIVE The purpose of this study was to describe the effects of normal respiration, changes in body position, repeated electrode placement, and heart rate differences on BSPM signals in a healthy population. METHODS Sixty-seven-lead BSPM was performed in healthy individuals during the resting supine position, a reclined position of 45 degrees, an exercise-increased heart rate, and a follow-up measurement in the resting supine position after 1 week to determine the effect of repeated electrode placement. R-, S-and T-wave amplitudes in all leads were compared between the baseline supine position and the aforementioned conditions. RESULTS Ten subjects were included {5 (50%) male; median age 28 years (interquartile range [IQR] 26-30 years)}. The R-wave showed the greatest amplitude variation across all conditions, with the largest changes caused by repeated electrode placement (maximum decrease-0.63 mV [IQR-0.69 to-0.22 mV]) and normal respiration (maximum increase 0.32 mV [IQR 0.08-0.55 mV]) and the smallest changes due to reclined position (maximum decrease-0.23 mV [IQR-0.28 to-0.15 mV]). Electrodes near standard precordial positions were most affected. The exercise-increased heart rate reduced the R-wave amplitude in left-sided electrodes and increased the S-wave amplitude in middle superior electrodes. T-wave amplitude generally increased after exercise. CONCLUSION Normal intrapersonal variation in BSPM signals was analyzed. Repeated electrode placement and normal respiration caused the largest amplitude changes. These findings may help differentiate normal variation from pathological changes in BSPM.

Background:Carriers of (likely) Plakophilin-2 pathogenic variants (PKP2-(L)PV) are at risk of developing arrhythmogenic cardiomyopathy. Early disease detection is crucial because life-threatening arrhythmias may occur early. CineECG is a novel electrocardiogram (ECG) analysis tool that reconstructs the average trajectory of ventricular electrical activity. Objective:The study aimed to describe the electrical depolarization and repolarization CineECG trajectories in PKP2-(L)PV carriers with a normal ECG as per evaluation of 2 cardiologists, who meet no Task Force Criteria other than their PV. Methods:PKP2-(L)PV carriers were 2:1-matched to control subjects, who had atrioventricular nodal reentry tachycardia but no other cardiac abnormalities. Sinus rhythm ECGs of controls were used to create a normal distribution of trajectories. PKP2-(L)PV carriers' trajectories were compared with the normal distribution. A trajectory was considered abnormal if it fell less than 95% within the normal distribution. Results:Overall, 104 subjects were included (age 24 years [19-36], 43% men): 37 PKP2-(L)PV carriers and 67 controls. Depolarization and repolarization trajectories were abnormal in 51% and 24% of carriers, respectively. In carriers with abnormal depolarization trajectories, significant differences were observed in the direction of the initial depolarization trajectory when compared with controls in the inferior-superior axis (P = .005) and posterior-anterior axis (P = .020). In the left-right axis, the direction significantly differed from carriers with a normal trajectory (P = .020). Conclusion:Abnormal electrical activity was identified in over half of preclinical PKP2-(L)PV carriers with a normal ECG. CineECG could be a sensitive tool to unveil early, subtle abnormalities in ventricular electrical activity that would otherwise not be detected.

IntroductionHigh-resolution digitized cardiac anatomical data sets are in huge demand in clinical, basic research and computational settings. They can be leveraged to evaluate intricate anatomical and structural changes in disease pathology, such as myocardial infarction (MI), which is one of the most common causes of heart failure and death. Advancements in high-resolution imaging and anatomical techniques in this field and our laboratory have led to vast improvements in understanding cardiovascular anatomy, especially the cardiac conduction system (CCS) responsible for the electricity of the heart, in healthy/aged/obese post-mortem human hearts. However, the digitized anatomy of the electrical system of the heart within MI hearts remains unexplored.MethodsFive post-mortem non-MI and MI human hearts were obtained by the Visible Heart® Laboratories via LifeSource, Minneapolis, MN, United States (with appropriate ethics and consent): specimens were then transported to Manchester University with an material transfer agreement in place and stored under the HTA 2004, UK. After performing contrast-enhanced micro-CT, a visualization tool (namely Amira) was used for 3D high-resolution anatomical visualizations and reconstruction. Various cardiovascular structures were segmented based on the attenuation difference of micro-CT scans and tissue traceability. The relationship between the CCS and surrounding tissues in MI and non-MI human hearts was obtained. 3D anatomical models were further explored for their use in computational simulations, 3D printing and mix/virtual reality visualization.Results3D segmented cardiovascular structures in the MI hearts elicited diverse macro-/micro- anatomical changes. The key findings are thickened valve leaflets, formation of new coronary arteries, increased or reduced thicknesses of pectinate and papillary muscles and Purkinje fibers, thinner left bundle branches, sinoatrial nodal atrophy, atrioventricular conduction axis fragmentation, and increased epicardial fat in some hearts. The propagation of the excitation impulses can be simulated, and 3D printing can be utilized from the reconstructed and segmented structures.DiscussionHigh-resolution digitized cardiac anatomical datasets offer exciting new tools for medical education, clinical applications, and computational simulation.

Background:Pathogenic variants in plakophilin-2 (PKP2) and phospholamban (PLN) are associated with arrhythmogenic cardiomyopathy. Early disease detection is important to prevent adverse events. Body surface potential mapping (BSPM) may detect local electrical abnormalities earlier than the 12-lead electrocardiogram. Objective:This study aimed to determine abnormalities in R-, S-, and T-wave amplitudes in PKP2- and PLN-pathogenic variant carriers using BSPM. Methods:67 lead BSPM was performed in controls and PKP2 and PLN carriers. R-, S-, and T-wave amplitudes across all leads in controls were used as reference. Amplitudes of carriers exceeding these ranges were considered abnormal and assessed across disease stages (presymptomatic, electrical, and structural, as done previously). Follow-up BSPM (≥2 years) was performed in a subset of carriers. Results:152 subjects (40 [27;54] years; 51% women) (40 controls and 112 carriers [53 PKP2 and 59 PLN]) were included. Amplitude abnormalities were most frequent in structural disease, predominantly in T waves (PKP2 20 [10;29]; PLN 25 [22;30] leads). Abnormalities in electrical disease were more prevalent in PLN carriers than PKP2 carriers (R wave 4 [1;7] vs 13 [8;16] leads, P = .002; S wave 2 [1;3] vs 4 [3;12] leads, P < .001; T wave 1 [0;3] vs 20 [16;28] leads, P < .001). Presymptomatic carriers typically had abnormalities outside the 12-lead configuration. As the disease progressed, abnormalities became more frequent and extended toward V1-V6. Follow-up BSPM (23 PKP2 and 16 PLN) showed consistency in locations of abnormalities with increased frequency (maximal increase 31%). Conclusion:BSPM detected abnormal amplitudes within and beyond the 12-lead electrocardiogram, even in presymptomatic carriers. Follow-up BSPM suggests that these abnormalities are associated with disease progression, highlighting the potential benefit of BSPM in early disease detection.

Background: CineECG offers a visual representation of the location and direction of the average ventricular electrical activity throughout a single cardiac cycle, based on the 12-lead ECG. Currently, CineECG has not been used to visualize ventricular activation patterns during ischemia. Purpose: To determine the changes in ventricular activity during acute ischemia with the use of CineECG, and relating this to changes in the ECG. Methods: Continuous ECG's during percutaneous coronary intervention with prolonged balloon inflation from the STAFF III database were analyzed with CineECG at baseline and every 10 s throughout the first 150 s of balloon inflation. The CineECG direction was determined for the initial QRS-complex, terminal QRS-complex, STsegment and T-wave. Changes in the CineECG were quantified by calculating the Delta angle between the direction at baseline and the direction at every 10 s of inflation. Additionally, the root mean square amplitude (rmsA) of the ST-segment was computed. Results: 94 patients were included. At start inflation, the median Delta angle was 14.7 degrees [7.5-33.4], 21.8 degrees [11.4-34.2], 20.6 degrees [8.0-43.9], and 23.5 degrees [11.8-48.0] for the initial QRS-complex, terminal QRS-complex, ST-segment and Twave, respectively. Meanwhile, the median rmsA increased from 0.039 mV [0.027-0.058] at baseline to 0.045 mV [0.033-0.075] at start of inflation. Conclusions: CineECG was able to detect immediate changes in ventricular electrical activity during induced ischemia, while changes in the ST-segment of the ECG were still subtle. Therefore, CineECG might support the early detection of acute ischemia, even before distinct ECG changes become visible.

The most commonly applied way of teaching students to convey the foundations of human anatomy and physiology involves textbooks and lectures. This way of transmitting knowledge causes difficulties for students, especially in the context of three-dimensional imaging of organ structures, and as a consequence translates into difficulties with imagining them. Even despite the rapid uptake of knowledge dissemination provided by online materials, including courses and webinars, there is a clear need for learning programs featuring first-hand immersive experiences tailored to suit individual study paces. In this paper, we present an approach to enhance a classical study program by combining multi-modality data and representing them in a Mixed Reality (MR)-based environment. The advantages of the proposed approach have been proven by the conducted investigation of the relationship between atrial anatomy, its electrophysiological characteristics, and resulting P wave morphology on the electrocardiogram (ECG). Another part of the paper focuses on the role of the sinoatrial node in ECG formation, while the MR-based visualization of combined micro-computed tomography (micro-CT) data with non-invasive CineECG imaging demonstrates the educational application of these advanced technologies for teaching cardiac anatomy and ECG correlations.