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.

Making machine learning matter to clinicians: model actionability in medical decision-making.

Machine learning (ML) has the potential to transform patient care and outcomes. However, there are important differences between measuring the performance of ML models in silico and usefulness at the point of care. One lens to use to evaluate models during early development is actionability, which is currently undervalued.

Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review.

Study Objectives: Polysomnograms (PSGs) collect a plethora of physiologic signals across the night. However, few of these PSG data are incorporated into standard reports, and hence, ultimately, under-utilized in clinical decision making. Recently, there has been substantial interest regarding novel alternative PSG metrics that may help to predict obstructive sleep apnea (OSA)-related outcomes better than standard PSG metrics such as the apnea-hypopnea index.

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.

Symptom subtypes and risk of incident cardiovascular and cerebrovascular disease in a clinic-based obstructive sleep apnea cohort.

Study Objectives: Patients with obstructive sleep apnea (OSA) are at increased risk of cardiovascular and cerebrovascular disease, but predicting those at greatest risk is challenging. Using latent class analysis, patients with OSA can be placed into discrete symptom subtypes. The aim of this study was to determine whether symptom subtypes are associated with future cerebrovascular disease in patients with OSA in a clinic-based cohort.

Heart rate variability is markedly abnormal following surgical repair of atrial and ventricular septal defects in pediatric patients.

Background: Abnormalities in heart rate variability (HRV) have been associated with increased morbidity and mortality. Adults with a history of successful correction of atrial septal defects (ASDs) and ventricular septal defects (VSDs) have clinically relevant abnormalities in HRV. The etiology of these abnormalities is unknown as is when they occur in the natural history of these lesions.

Symptom subtypes and cognitive function in a clinic-based OSA cohort: a multi-centre Canadian study.

Background: Distinct symptom subtypes are found in patients with OSA. The association between these subtypes and neurocognitive function is unclear.

Objective: The purposes of this study were to assess whether OSA symptom subtypes are present in a cohort of Canadian patients with suspected OSA and evaluate the relationship between subtypes and neurocognitive function.

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.