Detecting pattern transitions in psychological time series - A validation study on the Pattern Transition Detection Algorithm (PTDA).
Kathrin Viol, Helmut Schöller, Andreas Kaiser, Clemens Fartacek, Wolfgang Aichhorn, Günter Schiepek
Author Information
Kathrin Viol: Institute of Synergetics and Psychotherapy Research, University Hospital of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria. ORCID
Helmut Schöller: Institute of Synergetics and Psychotherapy Research, University Hospital of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria.
Andreas Kaiser: Institute for Clinical Psychology, University Hospital of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria.
Clemens Fartacek: Institute of Synergetics and Psychotherapy Research, University Hospital of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria.
Wolfgang Aichhorn: Institute of Synergetics and Psychotherapy Research, University Hospital of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria.
Günter Schiepek: Institute of Synergetics and Psychotherapy Research, University Hospital of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria.
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).
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