Real Time Premature Ventricular Contraction Detection Signals-Based Security and Steganography A Fuzzy Neural Network System

Abstract

This study investigates the integration of fuzzy logic and neural network systems for analyzing heart rate variability (HRV) and electrocardiogram (ECG) features to enhance the accuracy of cardiac monitoring. The primary purpose of this research is to develop a robust computational framework capable of synthesizing the dynamic variations in HRV and ECG data for improved decision-making. The challenge addressed lies in the need for accurate, real-time cardiac signal interpretation, as traditional methods often struggle to capture complex relationships between these signals. Using simulated HRV and ECG feature data, the study applies a fuzzy system and neural network framework to model the relationship between input signals and their respective outputs. The fuzzy system output is computed based on a linear relationship, while the neural network utilizes non-linear processing. A weighted combination of these outputs is then used to generate a final combined system output, providing a comprehensive interpretation of the signals. The results reveal significant insights into the system's performance. The fuzzy system output ranged from 0.46 to 0.94, with a final value of 0.88, indicating its responsiveness to input variability. The neural network output, exhibiting a non-linear relationship, ranged from 0.16 to 0.38, with a final value of 0.30. The combined fuzzy-neural system output demonstrated its ability to balance the two approaches, with values ranging from 0.38 to 0.83 and a final value of 0.74 at t = 10 seconds. These numerical results highlight the combined system's capacity to synthesize inputs effectively, providing meaningful and accurate outputs for potential clinical applications. Based on these findings, it is recommended that this combined fuzzy-neural framework be further validated with real-world cardiac data to assess its clinical utility. Additionally, incorporating adaptive learning mechanisms and real-time processing capabilities could further enhance the system's applicability in wearable health monitoring devices and diagnostic tools. This study underscores the potential of hybrid computational models in advancing the field of cardiac signal analysis and improving patient outcomes.