Paper Info
Title
Wavelet-Based Compression and Estimation of the $N$-State Stochastic Microtubule Signal.
Abstract
Recent studies reveal that the estimation of biological polymers such as Microtubules (MTs) is a challenging task given its stochastic nature and dynamic instability phenomenon of random transition between multiple states. As such, it is imperative to preserve, extract and estimate MT state information to gain deeper understanding of the influence of MT behavior on the onset of neurodegenerative disorders. Therefore, this paper introduces two novel compression frameworks with time-, frequency- and wavelet-domain run-length encoding for effective compression and estimation of the $N$ state MT signal that could be extended to other biomedical signals at-large. Peak detection is performed in both time- and wavelet-domain to effectively detect the $N$ transition states in MTs and encoded during the wavelet-compression process to alleviate the need for further processing at the receiver/decoder end. Experimental results demonstrate that our proposed wavelet-based compression technique is most suitable for compression of biomedical stochastic signals as it provided signal sparsity, better information preservation and estimation of MT system parameters. It also delivered competitive compression rates, better SNR and lower MT signal estimation errors with the additional benefit of reduction in storage, and computational needs.
Year
DOI
Venue
2019
10.1109/BIBM47256.2019.8982949
BIBM
Field
DocType
Citations 
Compression (physics),State information,Computer science,Instability,Algorithm,Artificial intelligence,Machine learning,Wavelet,Encoding (memory)
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Vineetha Menon100.34
Shantia Yarahmadian2154.37