G
G. Swapna
Researcher at Amrita Vishwa Vidyapeetham
Publications - 18
Citations - 1420
G. Swapna is an academic researcher from Amrita Vishwa Vidyapeetham. The author has contributed to research in topics: Deep learning & Wavelet. The author has an hindex of 13, co-authored 18 publications receiving 994 citations. Previous affiliations of G. Swapna include Government Engineering College, Sreekrishnapuram.
Papers
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Automated EEG analysis of epilepsy: A review
TL;DR: This review discusses various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail, and briefly presents the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting.
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Diabetes detection using deep learning algorithms
TL;DR: A methodology for classification of diabetic and normal HRV signals using deep learning architectures and can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%.
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Linear and nonlinear analysis of normal and CAD-affected heart rate signals
U. Rajendra Acharya,Oliver Faust,Vinitha Sree,G. Swapna,Roshan Joy Martis,Nahrizul Adib Kadri,Jasjit S. Suri +6 more
TL;DR: This study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.
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Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals
TL;DR: This is the first paper in which deep learning techniques are employed in distinguishing diabetes and normal HRV and the accuracy obtained using cross-validation is the maximum value achieved so far for the the automated detection of diabetes using HRV.
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A Review on Ultrasound-Based Thyroid Cancer Tissue Characterization and Automated Classification:
U. R. Acharya,G. Swapna,S. V. Sree,Filippo Molinari,Savita Gupta,Rh Bardales,Agnieszka Witkowska,J. S. Suri +7 more
TL;DR: This paper discusses the different types of features that are used to study and analyze the differences between benign and malignant thyroid nodules, and presents a brief description of the commonly used classifiers in ultrasound based CAD systems.