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      Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures

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          Most cited references 24

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          Micromachined inertial sensors

           N. Yazdi,  F. Ayazi,  K Najafi (1998)
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            Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability?

            Heart rate variability (HRV) is concerned with the analysis of the intervals between heartbeats. An emerging analysis technique is the Poincaré plot, which takes a sequence of intervals and plots each interval against the following interval. The geometry of this plot has been shown to distinguish between healthy and unhealthy subjects in clinical settings. The Poincaré plot is a valuable HRV analysis technique due to its ability to display nonlinear aspects of the interval sequence. The problem is, how do we quantitatively characterize the plot to capture useful summary descriptors that are independent of existing HRV measures? Researchers have investigated a number of techniques: converting the two-dimensional plot into various one-dimensional views; the fitting of an ellipse to the plot shape; and measuring the correlation coefficient of the plot. We investigate each of these methods in detail and show that they are all measuring linear aspects of the intervals which existing HRV indexes already specify. The fact that these methods appear insensitive to the nonlinear characteristics of the intervals is an important finding because the Poincaré plot is primarily a nonlinear technique. Therefore, further work is needed to determine if better methods of characterizing Poincaré plot geometry can be found.
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              Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy.

              Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.
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                Author and article information

                Journal
                Epilepsia
                Epilepsia
                Wiley
                0013-9580
                1528-1167
                December 09 2018
                December 09 2018
                Affiliations
                [1 ]Department of Electrical and Electronic Engineering The University of Melbourne Melbourne Victoria Australia
                [2 ]School of Information Technology Deakin University Geelong Victoria Australia
                [3 ]Melbourne Brain Centre Department of Medicine The Royal Melbourne Hospital University of Melbourne Melbourne Victoria Australia
                [4 ]Department of Engineering Design Indian Institute of Technology Madras Chennai India
                [5 ]Department of Neurosciences and Neurology The Central Clinical School Alfred Hospital Monash University Melbourne Victoria Australia
                [6 ]Department of Medicine and Neurology The Royal Melbourne Hospital The University of Melbourne Melbourne Victoria Australia
                Article
                10.1111/epi.14619
                © 2018

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