Detecting pattern transitions in psychological time series - A validation study on the Pattern Transition Detection Algorithm (PTDA).

Andreas Kaiser,Helmut Schöller,Kathrin Viol,Günter Schiepek,Wolfgang Aichhorn,Clemens Fartacek,Alexander N. Pisarchik

doi:10.1371/journal.pone.0265335

Andreas Kaiser, Helmut Schöller + Show 5 more

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https://doi.org/10.1371/journal.pone.0265335

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Abstract

With the increasing use of real-time monitoring procedures in clinical practice, psychological time series become available to researchers and practitioners. An important interest concerns the identification of pattern transitions which are characteristic features of psychotherapeutic change. Change Point Analysis (CPA) is an established method to identify the point where the mean and/or variance of a time series change, but changes of other and more complex features cannot be detected by this method. In this study, an extension of the CPA, the Pattern Transition Detection Algorithm (PTDA), is optimized and validated for psychological time series with complex pattern transitions. The algorithm uses the convergent information of the CPA and other methods like Recurrence Plots, Time Frequency Distributions, and Dynamic Complexity. These second level approaches capture different aspects of the primary time series. The data set for testing the PTDA (300 time series) is created by an instantaneous control parameter shift of a simulation model of psychotherapeutic change during the simulation runs. By comparing the dispersion of random change points with the real change points, the PTDA determines if the transition point is significant. The PTDA reduces the rate of false negative and false positive results of the CPA below 5% and generalizes its application to different types of pattern transitions. RQA quantifiers also can be used for the identification of nonstationary transitions in time series which was illustrated by using Determinism and Entropy. The PTDA can be easily used with Matlab and is freely available at Matlab File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/80380-pattern-transition-detection-algorithm-ptda).

Highlights

With the increasing use of real-time monitoring devices like the Synergetic Navigation System [1], high frequency sampled longitudinal data of psychological processes become available
The aim of this study is (1) to assess the performance of the Change Point Analysis (CPA) on time series with changes of complex patterns, (2) to extend the CPA to identify changes of the second order features like distribution, fluctuation, frequency, and autocorrelation, and (3) to validate the new Pattern Transition Detection Algorithm (PTDA) in terms of precision, rate of false negative results, and rate of false positive results
We will refer to a change point (CP) as a point of the time series that was applied to a single method and is the result of the CPA, and to a transition point (TP) as a point that was identified by the Pattern Transition Detection Algorithm (PTDA), which incorporates the results of all change points found for the different methods

Summary

Introduction

With the increasing use of real-time monitoring devices like the Synergetic Navigation System [1], high frequency sampled longitudinal data of psychological processes become available. Real-time monitoring produces time series with daily or even within-day measurements of psychological variables relevant to psychotherapeutic changes like emotions, motivation, insights, and symptom severity. The availability of such time series open the possibility to go beyond the assessment of prepost changes of psychotherapy and to shed light on the mechanisms of change that are still largely unknown [19, 20]. One crucial prerequisite to derive mechanisms of change from empirical time series is to identify the point of change in a patient’s individual process in a reliable and valid way This is non-trivial due to the noisy, discontinuous, and complex patterns of the time series [22, 23]

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