Dynamic Time Division Scheduling Protocol for Medical Application Using Frog Synchronization Algorithm

Muhammad, Norhafizah; Lim, Tiong Hoo

doi:10.1007/978-3-030-69963-5_11

Cited by 1 publication

(1 citation statement)

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“…Artifacts or other information common to both signals can be identified and retrospectively aligned in situations where system constraints preclude real-time synchronization. This process may rely upon artifacts that are introduced into the data streams while they are being recorded ( 201 , 209 – 211 ), or may utilize features such as heart beats that are naturally present and accessible in a wide range of high frequency physiological signals ( 212 – 216 ). This method of synchronization requires artifacts or features from multiple time points throughout the signals to ensure that clock drift is properly accounted for ( 85 ).…”

Section: Improving Timing Accuracy and Precisionmentioning

confidence: 99%

Timing errors and temporal uncertainty in clinical databases—A narrative review

Goodwin

Eytan

Dixon

et al. 2022

Front. Digit. Health

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A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

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