Cardiac Failure Detection Using Neural Network Model with Dual-Tree Complex Wavelet Transform

Cardiac failure in the current scenario is a major issue for which many advanced health care units are increasing. In a similar way, the technology innovation related to it requires to progress adequately. Early stage/post-stage treatment for survival is highly essential. Technocrats, scientists and physicians emphasize to develop better technology as compared to earlier methods. Noninvasive method of detection is the interest of this work. It is a nonintrusive diagnostic tool and can be used in computer-aided techniques that can speed up the diagnosis process. In this paper, the approach is observed in two parts: feature extraction and classification. For detection, features are extracted using Dual-Tree Complex Wavelet Transfer (DTCWT). The features are found the best spectral features. Classification model is based on Multilayer Preceptron (MLP) Neural Network. The data are collected from MIT-BIH NSR for experiment. Classification accuracy result has been presented in the result section.

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