scholarly journals Improved Multiscale Entropy Technique with Nearest-Neighbor Moving-Average Kernel for Nonlinear and Nonstationary Short-Time Biomedical Signal Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
S. P. Arunachalam ◽  
S. Kapa ◽  
S. K. Mulpuru ◽  
P. A. Friedman ◽  
E. G. Tolkacheva
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Ex Vivo ◽  
Moving Average ◽  
Multiscale Entropy ◽  
Series Data ◽  
Physiological Data ◽  
Short Time Series ◽  
Biomedical Signals ◽  
Short Time

Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE) and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE) technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR) and atrial fibrillation (AF) was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

scholarly journals Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants

2021 ◽  
Author(s):  
Airton Monte Serrat Borin ◽  
Anne Humeau-Heurtier ◽  
Luiz Otavio Murta ◽  
Luiz Eduardo Virgilio Silva
Keyword(s):  
Time Series ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Short Term ◽  
Series Length ◽  
Short Time Series ◽  
Fuzzy Algorithms ◽  
Computing Performance ◽  
Similar Accuracy ◽  
Short Time

Abstract Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions-as a function of time series length-present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.

scholarly journals AN USER INTENTION MINING MODEL BASED ON FRACTAL TIME SERIES PATTERN

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040017
Author(s):  
SHAOFEI WU
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fractal Theory ◽  
Effective Means ◽  
Series Data ◽  
Range Analysis ◽  
Short Time Series ◽  
User Intent ◽  
User Intentions ◽  
Short Time

Users use the network more and more frequently, and more and more data is published on the network. Therefore, how to find, organize, and use the useful information behind these massive data through effective means, and analyze user intentions is a huge challenge. There are many time series problems in user intentions. Time series have complex characteristics such as randomness and multi-scale variability. Effectively identifying the inherent laws and objective phenomena contained in time series is the purpose of analyzing and processing time series data. Fractal theory provides a new way to analyze time series, and obtains the characteristics and rules of time series from a new perspective. Therefore, this paper introduces the fractal theory to analyze the time series problem, and proposes an improved G-P algorithm to realize the prediction and mining of user intentions. First, the method of array storage instead of repeated calculations is used to improve the method of saturated correlation dimension. Second, the Hurst exponent of the time series is obtained by the variable scale range analysis method. Finally, a fractal model for predicting user intent in short time series is established using the accumulation and transformation method. The experimental results show that the use of fractal theory can effectively describe the relevant characteristics of time series, the development trend of user intentions can be mined from big data, and the prediction model for short time series can be established to achieve information mining of user intentions.

scholarly journals Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

2020 ◽  
Vol 34 (04) ◽  
pp. 5758-5766 ◽  
Author(s):  
Qiquan Shi ◽  
Jiaming Yin ◽  
Jiajun Cai ◽  
Andrzej Cichocki ◽  
Tatsuya Yokota ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Computational Cost ◽  
Time Series Forecasting ◽  
Low Rank ◽  
Multiple Time ◽  
Tucker Decomposition ◽  
Unified Framework ◽  
Short Time Series ◽  
Short Time

This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors are projected to compressed core tensors by applying Tucker decomposition. At the same time, the generalized tensor Autoregressive Integrated Moving Average (ARIMA) is explicitly used on consecutive core tensors to predict future samples. In this manner, the proposed approach tactically incorporates the unique advantages of MDT tensorization (to exploit mutual correlations) and tensor ARIMA coupled with low-rank Tucker decomposition into a unified framework. This framework exploits the low-rank structure of block Hankel tensors in the embedded space and captures the intrinsic correlations among multiple TS, which thus can improve the forecasting results, especially for multiple short time series. Experiments conducted on three public datasets and two industrial datasets verify that the proposed BHT-ARIMA effectively improves forecasting accuracy and reduces computational cost compared with the state-of-the-art methods.

scholarly journals Permutation test for periodicity in short time series data

2006 ◽  
Vol 7 (S2) ◽  
Author(s):  
Andrey A Ptitsyn ◽  
Sanjin Zvonic ◽  
Jeffrey M Gimble
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Permutation Test ◽  
Series Data ◽  
Short Time Series ◽  
Short Time
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