P
Pranjali Gajbhiye
Researcher at Birla Institute of Technology and Science
Publications - 11
Citations - 227
Pranjali Gajbhiye is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Filter (signal processing) & Artifact (error). The author has an hindex of 5, co-authored 10 publications receiving 80 citations. Previous affiliations of Pranjali Gajbhiye include Visvesvaraya National Institute of Technology.
Papers
More filters
Journal ArticleDOI
Novel Approaches for the Removal of Motion Artifact From EEG Recordings
TL;DR: The experimental results demonstrate that the proposed MTV and MWTV approaches have better denoising performance with (average and average) values of (29.12 dB and 68.56%) and ( 29.29 dB and 67.51%), respectively, as compared to the existing techniques.
Journal ArticleDOI
EEG-Based Detection of Focal Seizure Area Using FBSE-EWT Rhythm and SAE-SVM Network
TL;DR: A hybrid approach based on the combination of the band or rhythm specific Fourier-Bessel series expansion domain empirical wavelet transform (FBSE-EWT) filter bank and sparse autoencoder based support vector machine (SAE-SVM) network is proposed for the categorization of FL and NFL types of EEG channels.
Journal ArticleDOI
Elimination of Ocular Artifacts From Single Channel EEG Signals Using FBSE-EWT Based Rhythms
TL;DR: This work has proposed a method for the removal of ocular artifacts from the EEG signal and it has a better performance with a minimum average MAE in PSD value of 0.029 for the proposed method as compared to other existing techniques.
Journal ArticleDOI
Wavelet Domain Optimized Savitzky–Golay Filter for the Removal of Motion Artifacts From EEG Recordings
Pranjali Gajbhiye,Nopparada Mingchinda,Wei Chen,Subhas Chandra Mukhopadhyay,Theerawit Wilaiprasitporn,Rajesh Kumar Tripathy +5 more
TL;DR: In this article, the wavelet domain optimized Savitzky-Golay (WOSG) filtering approach was proposed for the removal of motion artifacts from EEG signals, which is considered a preprocessing task for different neural information processing applications.
Journal ArticleDOI
Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time-frequency analysis.
TL;DR: A novel automated approach for sleep apnea detection using the bivariate CP signal, formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal, which has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively using the 10-fold cross-validation method.