Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Ramesh Kumar SUNKARIA"'
Autor:
Kanchan Sharma, Ramesh Kumar Sunkaria
Publikováno v:
Journal of Arrhythmia, Vol 39, Iss 3, Pp 412-421 (2023)
Abstract Background Accurate arrhythmia (atrial fibrillation (AF) and congestive heart failure (CHF)) detection is still a challenge in the biomedical signal‐processing field. Different linear and nonlinear measures of the electrocardiogram (ECG) s
Externí odkaz:
https://doaj.org/article/e87437ff32fc4365917ac1b2e1158013
Publikováno v:
Wireless Personal Communications. 130:1161-1190
Publikováno v:
Applied Medical Informatics, Vol 39, Iss 3-4, Pp 49-66 (2017)
Power spectral analysis of short-term heart rate variability (HRV) can provide instant valuable information to understand the functioning of autonomic control over the cardiovascular system. In this study, an adaptive continuous Morlet wavelet transf
Externí odkaz:
https://doaj.org/article/2314b59f33644188994e4f91ffa74db3
Publikováno v:
Silicon. 14:12075-12084
Autor:
Kanchan Sharma, Ramesh Kumar Sunkaria
Publikováno v:
Journal of Arrhythmia.
Autor:
Kanchan Sharma, Ramesh Kumar Sunkaria
Publikováno v:
Signal, Image and Video Processing.
Publikováno v:
Multimedia Tools and Applications. 81:7873-7893
Autor:
Himanshu Chhabra, Lakhan Dev Sharma, Ritesh Kumar Saraswat, Ramesh Kumar Sunkaria, Urvashi Chauhan
Publikováno v:
International Journal of Information Technology. 13:2363-2369
Cognitive load recognition during mental arithmetic activity facilitates to observe and identify the brain’s response towards stress stimulus. As a result, an efficient mental load characterization approach using electroencephalogram (EEG) signal a
Publikováno v:
Multidimensional Systems and Signal Processing. 33:275-300
Ultrasound is the most widely used biomedical imaging modality for the purpose of diagnosis. It often comes with speckle that results in reduced quality of images by hiding fine details like edges and boundaries, as well as texture information. In th
Publikováno v:
Signal, Image and Video Processing. 15:1821-1828
Detecting cognitive performance during mental arithmetic allows researchers to observe and identify the brain’s response to stimuli. Existing non-invasive methods for automated cognitive performance detection need improvements in terms of accuracy.