Fusion-Based Deep Learning Framework Detection Using an Optimized for Cardiovascular Disease Squeeze Net Model
Subject Areas : Pattern Recognition
1 - Department of Computer Science and Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India
Keywords: Electrocardiogram (ECG), Data Mining, Pattern Mining, Optimization Search Algorithm (OSA), Squeeze Net (OSA-SN),
Abstract :
The electrocardiogram (ECG), which measures the electrical activity of the heart, is frequently used to identify heart conditions because it is simple and non-invasive. The primary focus of the analysis is on the fusion of action impulse patterns produced by the heart’s specialized cardiac tissues. It is possible to detect any irregularities in the heart by closely examining the electrical signal generated with each heartbeat. Since the application of data mining technology in healthcare has greatly increased knowledge, data mining is becoming a more and more popular option in the medical field. This study uses a hybridized algorithm for pattern mining in order to categorize heartbeats. First, the input data from the ECG signal is pre- processed using a median filter, and then the features are extracted. Both statistical and medical aspects are included in these extracted features. Activity pattern mining using the Optimization Search Algorithm (OSA), a hybridized optimization algorithm, is the next step. Lastly, the rule matrix produced by the mining of activity patterns is used to categorize heartbeats. This research proposes a deep learning-based framework for the classification of cardiovascular diseases using electrocardiogram (ECG) signals. A hybrid optimization technique, integrating the Taylor series and Ebola Optimization Search Algorithm (OSA), termed OSA-SN, is introduced to enhance the performance of a lightweight convolutional neural network model, SqueezeNet. The ECG signals undergo preprocessing and feature extraction, followed by sequential pattern mining using the proposed optimization strategy. The optimized model achieves superior performance metrics, with a precision of 0.93, sensitivity of 0.94, and specificity of 0.91. These results demonstrate the potential of the OSA-SN-enhanced SqueezeNet model in accurate, real-time heartbeat classification for clinical applications.
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