Aligning Time Series with Genetically Tuned Dynamic Time Warping Algorithm

Kumar, Pankaj; Gupta, Ankur; Jayaraman, Vaidyanathan; Kulkarni, Bhaskar D.

doi:10.1007/978-3-540-72960-0_12

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…For computing the individual TDP for every cycle, we develop a simple and robust method that allows an elastic shifting of the time axis, based on dynamic time warping (DTW) [10], [11]. Put simply, DTW enables a nonlinear mapping of one signal to another, even if they are out of phase in the time axis.…”

Section: Our Pswt-dtw Approachmentioning

confidence: 99%

A novel PSWT-DTW approach for analyzing pseudo-periodic signals

Joshi

Chandran

Jayaraman

et al. 2013

2013 IEEE Point-of-Care Healthcare Technologies (PHT)

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Pseudo-periodic signals are rampant in biomedical applications but are difficult to analyze. One approach is to compute time domain parameters of each individual cycle in the pseudo-periodic signal. The classic approach requires repeated computation in each cycle, which tends to be either error prone, computationally burdensome, or requires manual effort.We provide a novel combination of the pitch synchronous wavelet transform which when combined with dynamic time warping results in effective quantification of cycles in the pseudo-periodic signal. We demonstrate our application of this method in studying the arterial pulse. The results show that our approach is feasible and effective, and confirms further scope in other applications.

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Section: Our Pswt-dtw Approachmentioning

confidence: 99%

A novel PSWT-DTW approach for analyzing pseudo-periodic signals

Joshi

Chandran

Jayaraman

et al. 2013

2013 IEEE Point-of-Care Healthcare Technologies (PHT)

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“…In engineering, genetic algorithm has also been used with good results in problems such as cyclicsteam oil production optimization problem [21], speed control of brushless DC motor [22], airport scheduling [23], mobile robot motion control [24], modeling adaptive agents in stock markets [25], [26], portfolio management [27] and traveling salesman problem [28]. GA has also been applied to optimize the parameters for DTW [29].…”

Section: Introductionmentioning

confidence: 99%

Time warping of apneic ECG signals using genetic algorithm

Lui

Cheung

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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We construct a method of time warping in quasiperiodic time series analysis using genetic algorithm in order to extract the instantaneous phase difference between a template signal and a testing signal. Contrast to previous studies, which involves correlation estimations to determine the shape similarity of two signals taken from the quasiperiodic time series, time warping perform the comparison of the two signals by first constructing a discrete set of M points formed from uniformly sampled values of the template signal f(t). The discrete set of sample values of the testing signal, g(t), which contains N points, will be interpolated to form a continuous function so that the difference between the template signal at those M points and the corresponding testing signals are minimize to best preserve the mapping of the two signals. The result of this optimization procedure produces a phase shift function that relates the time t in the testing signal to the time t in the template signal. Due to the numerous choices in the partitioning of the time domain of the two signals, genetic algorithm is found to be effective in extracting this phase shift function. We apply this theoretical tool of time warping using genetic algorithm to analyze the electrocardiographic (ECG) signals, with the aim to investigate if central apneic and obstructive apneic episodes can be differentiated from non-apneic episodes. Detailed statistical analysis of the phase shift from real ECG data of sleep apnea patient indicates that the difference of both magnitude and phase of the signals can be used to differentiate apneic events from nonapneic events.

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