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Akanksha Pathak
Researcher at Indian Institute of Technology Kharagpur
Publications - 10
Citations - 96
Akanksha Pathak is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Phonocardiogram & Computer science. The author has an hindex of 4, co-authored 6 publications receiving 45 citations. Previous affiliations of Akanksha Pathak include Indian Institute of Information Technology, Allahabad.
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Classification of coronary artery diseased and normal subjects using multi-channel phonocardiogram signal
TL;DR: A new multi-channel PCG-based system to classify CAD-affected and normal subjects is proposed, and it does not require any additional reference signal, such as an electrocardiogram (ECG) signal.
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A study on gait entropy image analysis for clothing invariant human identification
TL;DR: A robust feature selection technique has been addressed through Gait Entropy Image (GEnI) analysis and experimental results demonstrate the efficiency of proposed feature selection method using k-nearest neighbor (k-NN), minimum distance classifier (MDC), and support vector machine (SVM) algorithms.
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Detection of coronary artery atherosclerotic disease using novel features from synchrosqueezing transform of phonocardiogram
TL;DR: SST can capture useful time-frequency information from PCG to facilitate CAD detection and the proposed fusion framework using SST and spectral features in a multichannel PCG acquisition platform performs better than other PCG based approaches.
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An improved method to detect coronary artery disease using phonocardiogram signals in noisy environment
TL;DR: The proposed PCG-based multichannel CAD detection system robust against the environmental noise that does not require additional reference signals for noise acquisition and PCG segmentation is proposed and found to be superior in CAD classification when compared with existing noise removal based approach.
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Ensembled Transfer Learning and Multiple Kernel Learning for Phonocardiogram Based Atherosclerotic Coronary Artery Disease Detection
TL;DR: The proposed MKL, finds optimal kernel combination by maximizing the similarity with ideal kernel and minimizing the redundancy with other basis kernels, shows the potential of development of high accuracy CAD detection system by using easy to acquire, non-invasive PCG signal.