G
G. Seshikala
Researcher at Reva Institute of Technology and Management
Publications - 19
Citations - 230
G. Seshikala is an academic researcher from Reva Institute of Technology and Management. The author has contributed to research in topics: Phase congruency & Mass screening. The author has an hindex of 7, co-authored 19 publications receiving 158 citations. Previous affiliations of G. Seshikala include Manipal University.
Papers
More filters
Journal ArticleDOI
Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers
Usha Desai,Usha Desai,Roshan Joy Martis,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,C. Gurudas Nayak,C. Gurudas Nayak,G. Seshikala,Ranjan Shetty K +9 more
TL;DR: Recurrence Quantification Analysis (RQA) features are applied for classifying four classes of ECG beats namely Normal Sinus Rhythm (NSR), A-F fib, AFL and V-Fib using ensemble classifiers.
Proceedings ArticleDOI
Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques
TL;DR: A machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features and is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.
Journal ArticleDOI
Decision support system for arrhythmia beats using ecg signals with dct, dwt and emd methods: a comparative study
Usha Desai,Usha Desai,Roshan Joy Martis,C. Gurudas Nayak,G. Seshikala,K. Sarika,K. Ranjan Shetty +6 more
TL;DR: This work has obtained a classification accuracy of 99.77% using ICs on DWT method and developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare.
Journal ArticleDOI
Application of ensemble classifiers in accurate diagnosis of myocardial ischemia conditions
TL;DR: This study presents an approach for automated diagnosis of MI condition using ECG beats, which is non-invasive and cost-efficient and helps in screening of MI, predominantly in rural and remote areas.
Proceedings ArticleDOI
An efficient technique for automated diagnosis of cardiac rhythms using electrocardiogram
TL;DR: Pattern recognition and machine learning-based approach for computer-aided detection of five classes of ECG arrhythmia beats using Discrete Cosine Transform (DCT) coefficients is described, which has intensive applications in early diagnosis, mass screening of cardiac health and in cardiac theoretic devices such as pacemaker systems.