Application of chaos theory for arrhythmia detection in pathological databases. (9th November 2022) - Gupta, Varun
To handle the current pathological situation of heart-related diseases, various techniques belonging to automatic electro-cardio-gram (ECG) signal analysis are already available but have not succeeded. In this paper, Savitzky-Golay filtering (SGF) and support vector machine (SVM) techniques are used for pre-processing and classification purposes. Feature extraction algorithms play a vital role in biomedical signal processing (BSP). For that purpose, the chaos analysis theory is used as a feature extraction tool on different pathological datasets obtained from different cardiology labs to classify different arrhythmia types. The effectiveness of the proposed methodology is evaluated on different performance evaluating parameters, viz., sensitivity (Se), accuracy (Acc), and duplicity (D). The proposed methodology presented Se of 99.87%, Acc of 99.72%, and D of 0.066%.