Accurate segmentation of biomedical time-series, such as intracardiac electrograms, is vital for understanding physiological states and supporting clinical interventions. Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. This study evaluates five segmentation algorithms, from traditional rule-based methods to advanced deep learning models, using a unique clinical dataset of intracardiac signals from 100 patients. We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. Results indicated that deep learning models outperformed traditional methods, with UNet achieving the highest segmentation score of 88.9 % (root mean square errors for onset and offset of 8.43 ms and 7.49 ms), closely followed by DENS-ECG at 87.8 %. Faster R-CNN and SVM showed moderate performance, while the rule-based method had the lowest accuracy (77.7 %). UNet and DENS-ECG excelled in capturing detailed features and handling noise, highlighting their potential for clinical application. Despite greater computational demands, their superior performance and diagnostic potential support further exploration in biomedical time-series analysis.

• Keywords
• DENS-ECG, Electrophysiology Study, Faster R-CNN, Rule-based Delineation, Support Vector Machines, Time-series Segmentation, U-Net,
• MeSH
• Algorithms MeSH
• Bayes Theorem MeSH
• Deep Learning MeSH
• Electrocardiography * methods   MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Signal Processing, Computer-Assisted * MeSH
• Support Vector Machine MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Myocardial remodelling involves structural and functional changes in the heart, potentially leading to heart failure. The deoxycorticosterone acetate (DOCA)/salt model is a widely used experimental approach to study hypertension-induced cardiac remodelling. It allows to investigate the mechanisms underlying myocardial fibrosis and hypertrophy, which are key contributors to impaired cardiac function. In this study, myocardial remodelling in rat deoxycorticosterone acetate/salt model was examined over a three-week period. The experiment involved 11 male Sprague-Dawley rats, divided into two groups: fibrosis (n=6) and control (n=5). Myocardial remodelling was induced in the fibrosis group through unilateral nephrectomy, deoxyco-rticosterone acetate administration, and increased salt intake. The results revealed significant structural changes, including increased left ventricular wall thickness, myocardial fractional volume, and development of myocardial fibrosis. Despite these changes, left ventricular ejection fraction was preserved and even increased. ECG analysis showed significant prolongation of the PR interval and widening of the QRS complex in the fibrosis group, indicating disrupted atrioventricular and ventricular conduction, likely due to fibrosis and hypertrophy. Correlation analysis suggested a potential relationship between QRS duration and myocardial hypertrophy, although no significant correlations were found among other ECG parameters and structural changes detected by MRI. The study highlights the advantage of the DOCA/salt model in exploring the impact of myocardial remodelling on electrophysiological properties. Notably, this study is among the first to show that early myocardial remodelling in this model is accompanied by distinct electrophysiological changes, suggesting that advanced methods combined with established animal models can open new opportunities for research in this field. Key words Myocardial fibrosis, Remodelling, Animal model, DOCA-salt, Magnetic resonance imaging.

• MeSH
• Desoxycorticosterone Acetate * toxicity   MeSH
• Electrocardiography * methods   MeSH
• Fibrosis MeSH
• Hypertension * physiopathology   chemically induced   MeSH
• Rats MeSH
• Sodium Chloride, Dietary * MeSH
• Disease Models, Animal MeSH
• Rats, Sprague-Dawley MeSH
• Ventricular Remodeling * physiology   drug effects   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Desoxycorticosterone Acetate * MeSH
• Sodium Chloride, Dietary * MeSH