Abstract
In recent decades, the health monitoring system has been an attractive research topic. In order to diagnose Cardiovascular Disease (CVD), Electrocardiogram (ECG) is one of the popular instruments. In recent times, ECG monitoring systems are exponentially increasing. As a result, it is very difficult for the scientists and health experts to choose, compare and evaluate the systems, and identify which will meet their needs and necessary monitoring. In this research article, we offer a global taxonomy and review from an expert of ECG surveillance systems. It provides support, which is an evidence to monitor components, contexts, functions and challenges. For monitoring the ECG systems, an architectural model is proposed in this paper and provides in-depth review. In this research paper, an adaptive medium filter is used for signal preprocessing and the segmentation is accomplished using watershed transform. Finally, feature extraction is performed using several texture features and signal classification is accomplished using probabilistic neural networks (PNN).
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Rajakumar, G., Nagaraju, V., Bapu, B.R.T., Malar, P.S.R., Krishnan, R.S., Narayanan, K.L. (2022). Myocardial Infarction Analysis Using Deep Learning Neural Network Based on Image Processing Approach. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_59
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DOI: https://doi.org/10.1007/978-981-19-2281-7_59
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