Affiliations: [a] Department of ECE, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India
| [b] Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education Krishnankoil, Tamil Nadu, India
| [c]
Data Science–Division School of Computing Science and Engineering VIT Bhopal University, Kothrikalan, Sehore Madhya Pradesh, India
| [d] Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. Saravanan Velusamy, Research Scholar, Department of ECE, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India. E-mail: [email protected].
Abstract: Due to the advantages of Electrocardiogram (ECG) signals, which are challenging to replicate yet easy to get, ECG-based identification has become a new path in biometric recognition research. These classic feature extraction techniques require Hand-crafted or feature-specific implications. The methods used for selection and integration of features, are time-consuming. The main objective of this study is develop deep learning approach to study the features of ECG data digital characteristics, thus saving a lot of signal pre-processing steps. This research proposed novel technique in X-wave recognition of ECG signal using max-min threshold technique and classification of ECG signal. This signal has been processed for noise removal and normalization. Then this processed signal has been used to recognize X-wave from ECG signal. From recognized X-wave, the ECG signal has been classified using Improved Support Vector Machine (ISVM). The QRS complex has been detected using Stacked Auto-Encoder with Neural Networks (SAENN). The study took raw ECG signals and entropy-based features evaluated from extracted QRS complexes. Exams are based on classifying heart disorders into two, five, and twenty classes. The experimental findings showed that our suggested model attained a high classification accuracy of 97%, precision of 89%, recall of 90%, F-1 score of 88%.
