Cardiac arrhythmia detection using artificial neural network

Abstract

This paper outlines the development of the ‘Cardiac Abnormality Monitoring’ wearable medical device, aimed at creating a compact safety monitor integrating advanced Artificial Neural Network (ANN) algorithms. Given power consumption constraints and cost-effectiveness, a strategy combining sophisticated instruments with neural network algorithms is proposed to enhance performance. This approach aims to compete with high-end wearable devices, utilizing innovative manufacturing techniques. The paper evaluates the feasibility of employing the Levenberg-Marquardt (LM) ANN algorithm in power-conscious wearable devices, considering its potential for offline embedded systems or IoT gadgets capable of cloud-based data uploading. The Levenberg-Marquardt ANN is chosen primarily for its practicality in prototype development, with other neural network algorithms also explored to identify potential alternatives. We have compared the six neural network models and determined the model that has the potential to replace the primary neural network model. We found that the ‘Kernelized SVC with PCA’ can test accuracy. To be specific, in this paper, we will evaluate the performance of the ANN model and also check its feasibility and practicality by integrating it with a constructed prototypical working model.

Keywords

• Wearable device
• LM-ANN
• Cardiac arrhythmia detection
• Training
• Regression