Paroxysms are sudden, unpredictable, short-lived events that abound in physiological processes and pathological disorders, from cellular functions (e.g., hormone secretion and neuronal firing) to life-threatening attacks (e.g., cardiac arrhythmia, epileptic seizures, and diabetic ketoacidosis). With the increasing use of personal chronic monitoring (e.g., electrocardiography, electroencephalography, and glucose monitors), the discovery of cycles in health and disease, and the emerging possibility of forecasting paroxysms, the need for suitable methods to evaluate synchrony—or phase-clustering—between events and related underlying physiological fluctuations is pressing. Here, based on examples in epilepsy, where seizures occur preferentially in certain brain states, we characterize different methods that evaluate synchrony in a controlled timeseries simulation framework. First, we compare two methods for extracting the phase of event occurrence and deriving the phase-locking value, a measure of synchrony: (M1) fitting cycles of fixed period-length vs (M2) deriving continuous cycles from a biomarker. In our simulations, M2 provides stronger evidence for cycles. Second, by systematically testing the sensitivity of both methods to non-stationarity in the underlying cycle, we show that M2 is more robust. Third, we characterize errors in circular statistics applied to timeseries with different degrees of temporal clustering and tested with different strategies: Rayleigh test, Poisson simulations, and surrogate timeseries. Using epilepsy data from 21 human subjects, we show the superiority of testing against surrogate time-series to minimize false positives and false negatives, especially when used in combination with M1. In conclusion, we show that only time frequency analysis of continuous recordings of a related bio-marker reveals the full extent of cyclical behavior in events. Identifying and forecasting cycles in biomedical timeseries will benefit from recordings using emerging wearable and implantable devices, so long as conclusions are based on conservative statistical testing.
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January 2021
Research Article|
January 25 2021
Measuring synchrony in bio-medical timeseries
Special Collection:
Dynamical Disease: A Translational Perspective
Marc G. Leguia
;
Marc G. Leguia
a)
1
Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern
, 3010 Bern, Switzerland
a)Author to whom correspondence should be addressed: mgrauleg@gmail.com
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Vikram R. Rao
;
Vikram R. Rao
2
Department of Neurology and Weill Institute for Neurosciences, University of California
, San Francisco, California 94143, USA
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Jonathan K. Kleen
;
Jonathan K. Kleen
2
Department of Neurology and Weill Institute for Neurosciences, University of California
, San Francisco, California 94143, USA
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Maxime O. Baud
Maxime O. Baud
1
Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern
, 3010 Bern, Switzerland
3
Wyss Center for Bio- and Neuro-technology
, Geneva 1202, Switzerland
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a)Author to whom correspondence should be addressed: mgrauleg@gmail.com
Note: This paper is part of the Focus Issue on Dynamical Disease: A Translational Perspective.
Chaos 31, 013138 (2021)
Article history
Received:
August 27 2020
Accepted:
December 10 2020
Citation
Marc G. Leguia, Vikram R. Rao, Jonathan K. Kleen, Maxime O. Baud; Measuring synchrony in bio-medical timeseries. Chaos 1 January 2021; 31 (1): 013138. https://doi.org/10.1063/5.0026733
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