Cardiac alterations induced by Heloderma horridum horridum venom in rats: An experimental study with ECG analysis using a linear regression algorithm.

Envenomation by reptile venom, particularly from lizards, poses significant health risks and can lead to physiological and cardiovascular changes. The venom of Heloderma horridum horridum, endemic to Colima, Mexico, was tested on Wistar rats. Electrocardiographic (ECG) data were collected pre-treatment and at 5-min intervals for 1 h post-envenomation. A specially designed computational linear regression algorithm (LRA) was used for the segmentation analysis of the ECG data to improve the detection of fiducial points (P, Q, R, S, and T) in ECG waves. Additionally, heart tissue was analyzed for macroscopic and microscopic changes. The results revealed significant electrocardiographic alterations, including pacemaker migration, junctional extrasystoles, and intraventricular conduction aberrations. By applying a linear regression algorithm, the study compensated for noise and anomalies in the isoelectric line in an ECG signal, improving the detection of P and T waves and the QRS complex with an efficiency of 97.5%. Cardiac enzyme evaluation indicated no statistically significant differences between the control and experimental groups. Macroscopic and microscopic examination revealed no apparent signs of damage or inflammatory responses in heart tissues. This study enhances our understanding of the cardiovascular impact of Heloderma venom, suggesting a greater influence on changes in conduction and arrhythmias than on direct cardiac damage to the myocardium.

Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices.

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart's electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm's performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal's isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.

Segmentation of the ECG Signal by Means of a Linear Regression Algorithm.

The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.

Reproducing chaos by variable structure recurrent neural networks.

In this paper, we present a new approach for chaos reproduction using variable structure recurrent neural networks (VSRNN). A neural network identifier is designed, with a variable structure that will change according to its output performance as compared to the given orbits of an unknown chaotic systems. A tradeoff between identification errors and computational complexity is discussed.