The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit.

The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device.

Timing errors and temporal uncertainty in clinical databases-A narrative review.

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.

An integration engineering framework for machine learning in healthcare.

Background And Objectives: Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented.

A practical approach to storage and retrieval of high-frequency physiological signals.

Objective: Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms.

Temporal Variability in the Sampling of Vital Sign Data Limits the Accuracy of Patient State Estimation.

Objectives: Physiologic signals are typically measured continuously in the critical care unit, but only recorded at intermittent time intervals in the patient health record. Low frequency data collection may not accurately reflect the variability and complexity of these signals or the patient's clinical state. We aimed to characterize how increasing the temporal window size of observation from seconds to hours modifies the measured variability and complexity of basic vital signs.

Prevalence of the Brugada electrocardiographic pattern at the Medical Center of Louisiana in New Orleans.

Since 1992 the Brugada syndrome has gained recognition as a cause of ventricular fibrillation. The syndrome was originally described in patients with the diagnostic triad of (1) right bundle branch block, (2) an electrocardiogram (ECG) with persistent ST-segment elevation in leads V1, V2, and V3, and (3) sudden cardiac death. Two different types of ST-segment elevation, coved and saddleback, have been described.