WaveletBased Compression and Estimation of the N State Stochastic Microtubule Signal 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 waveletdomain runlength encoding for effective compression and estimation of the  N  state MT signal that could be extended to other biomedical signals atlarge. Peak detection is performed in both time and waveletdomain to effectively detect the  N  transition states in MTs and encoded during the waveletcompression process to alleviate the need for further processing at the receiverdecoder end. Experimental results demonstrate that our proposed waveletbased 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.

Generalization of a waveletbased algorithm to adaptively detect activation intervals in weak and noisy myoelectric signals Abstract   This study introduces an adaptive implementation of a Continuous Wavelet Transform (CWT) decomposition technique used to estimate the timing of muscular activation in weak and noisy myoelectric signals. The algorithm updates automatically the threshold based on the statistical properties of the EMG data, through an iterative estimation of the SignaltoNoise Ratio (SNR). Moreover, it includes a stopping criterion for the number of CWT decomposition levels, and this allows a relevant decrease of the computational burden. This algorithm was applied to both synthetic and semisynthetic signals, and compared against the original formulation of the CWTbased technique and a common thresholdbased technique for the detection of muscle activations. Performance of these techniques was assessed by using Bias, Relative Timing Error and Accuracy of the detection. Bias values resulted lower than 18xe2x80xafms, Relative Timing Error lower than 5% and Accuracy greater than 97% for all the tested SNR (ranging from xe2x88x922xe2x80xafdB to 10xe2x80xafdB), and with a substantial independence from SNR levels. The performance was shown to hold also if the hypothesis of absence of muscular activation in the reference window cannot be guaranteed. The results show that the proposed approach, which is adaptive, operatorindependent and iterative, performs properly when applied to weak and noisy myoelectric signals, and is thus a valid general solution when dealing with clinical conditions where muscular activity is low, and when recording conditions cannot be entirely controlled.