scholarly journals Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

2018 ◽  
Vol 119 (4) ◽  
pp. 1394-1410 ◽  
Author(s):  
Sile Hu ◽  
Qiaosheng Zhang ◽  
Jing Wang ◽  
Zhe Chen
Keyword(s):  
Real Time ◽  
Spike Activity ◽  
Change Point ◽  
Particle Filtering ◽  
Jump Process ◽  
Change Point Detection ◽  
Stochastic Jump ◽  
Abrupt Changes ◽  
Non Gaussian ◽  
Point Detection

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

scholarly journals Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1720
Author(s):  
Pieter Van den Berghe ◽  
Maxim Gosseries ◽  
Joeri Gerlo ◽  
Matthieu Lenoir ◽  
Marc Leman ◽  
...  
Keyword(s):  
Real Time ◽  
Change Point ◽  
Major Change ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Biofeedback System ◽  
Individualized Approach ◽  
Tibial Acceleration ◽  
Point Detection

A method is presented for detecting changes in the axial peak tibial acceleration while adapting to self-discovered lower-impact running. Ten runners with high peak tibial acceleration were equipped with a wearable auditory biofeedback system. They ran on an athletic track without and with real-time auditory biofeedback at the instructed speed of 3.2 m·s−1. Because inter-subject variation may underline the importance of individualized retraining, a change-point analysis was used for each subject. The tuned change-point application detected major and subtle changes in the time series. No changes were found in the no-biofeedback condition. In the biofeedback condition, a first change in the axial peak tibial acceleration occurred on average after 309 running gait cycles (3′40″). The major change was a mean reduction of 2.45 g which occurred after 699 running gait cycles (8′04″) in this group. The time needed to achieve the major reduction varied considerably between subjects. Because of the individualized approach to gait retraining and its relatively quick response due to a strong sensorimotor coupling, we want to highlight the potential of a stand-alone biofeedback system that provides real-time, continuous, and auditory feedback in response to the axial peak tibial acceleration for lower-impact running.

scholarly journals Change-point detection: application of Cusum method to real life data

2021 ◽  
Vol 8 (1) ◽  
pp. 1041-1047
Author(s):  
Edoh Katchekpele ◽  
Tchilabalo Abozou Kpanzou ◽  
Jean-Etienne Ouindllassida Ouédraogo
Keyword(s):  
Change Point ◽  
Real Life ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Test Statistic ◽  
Life Data ◽  
Real Life Data ◽  
Abrupt Changes ◽  
Cusum Method ◽  
Point Detection

Several procedures have been developed for the detection of abrupt changes in time series. Among these procedures, it can be mentioned the Cumulative Sum (Cusum) type method. It is in such a perspective that Katchekpele et al. (2017) proposed a method using a Cusum type test to detect a change-point in the unconditional variance of the generalised autoregressive conditional heteroskedasticity(GARCH) models. The aim of this paper is to present an application of their technique. After briefly recalling how the test statistic was constructed, the change-point detection algorithm is given and it is shown how it is applied to some real life data.

scholarly journals Change point detection for clustered expression data

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Michaela Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Data Structure ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Research Question ◽  
Change Point Detection ◽  
Expression Data ◽  
Abrupt Changes ◽  
Point Detection

Abstract Background: To detect changes in biological processes samples are oftenmeasured at several time points. We observe expression data measured atdifferent developmental stages, or more broadly, historical data. Hence, the mainassumption of our proposed methodology is the independence between theobserved samples over time. In addition, the observations are clustered at eachpoint in time. The clustering is caused by measuring litter mates from relativelyfew mother mice at each development stage. The examination is lethal.Therefore, we have an independent data structure over the entire history, but adependent data structure at a particular point in time. Over the course of thehistorical data, we want to identify abrupt changes in the outcome - a changepoint. Results: In this paper, we demonstrate the application of generalized hypothesistesting using a linear mixed effects model as one possible method for detectingchange points. The coefficients from the linear mixed model are used in multiplecontrast tests. The effect estimates are then visualized with simultaneousconfidence intervals. The figure of the confidence intervals can be used for thedetermination of the change point. Multiple contrast tests depend on the choiceof the used contrast. A variety of possible usable contrasts exists. In smallsimulation studies, we model different courses with abrupt changes and illustratedifferent contrasts. We found two contrasts, both capable of answering differentresearch questions in change point detection. Sequen contrast to detectindividual points of change or McDermott contrast to illustrate overallprogression. In addition, we show the application on a clinical pilot study. Conclusion: Simultaneous confidence intervals estimated by multiple contrasttests using the model fit from a linear mixed model are usable to determinepossible change points in clustered expression data. The confidence intervalsdeliver direct interpretable effect estimates on the scale of the outcome for thestrength of the potential change point. Hence, scientists can define biologicallyrelevant limits of change depending on the research question. We found tworarely used contrast with the best properties to detect a possible change: theSequen and McDermott contrast. We provide R code for the direct applicationwith examples

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