Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study.

Background: Clinical decision support systems (CDSS) have the potential to lower the patient mortality and morbidity rates. However, signal artifacts present in physiological data affect the reliability and accuracy of the CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing physiological data, further leading to alarm fatigue because of increased noise levels, staff disruption, and staff desensitization in busy critical care environments.

Video-Based Neonatal Motion Detection.

Newborns admitted to the neonatal intensive care unit (NICU) require a high level of care due to their precarious condition. Nurses typically monitor their vital signs continuously using wearable sensors such as electrocardiogram (ECG) electrodes placed on their chest and a pulse oximeter on a limb. When the patient moves, this can cause motion artifacts on one or more physiologic signals, potentially resulting in a false alarm on the patient monitor.

CEA: Clinical Event Annotator mHealth Application for Real-time Patient Monitoring.

This research develops a novel dynamic mobile health (mHealth) application (app), called the Clinical Event Annotator (CEA). The CEA comprises of a native Android tablet app and an administrative web app. The native app is used at the patient bedside to manually annotate clinical events in real-time.

Performance Evaluation of New-Generation Pulse Oximeters in the NICU: Observational Study.

This crossover observational study compares the data characteristics and performance of new-generation Nellcor OXIMAX and Masimo SET SmartPod pulse oximeter technologies. The study was conducted independent of either original equipment manufacturer (OEM) across eleven preterm infants in a Neonatal Intensive Care Unit (NICU). The SmartPods were integrated with Dräger Infinity Delta monitors.

Implementation of artifact detection in critical care: a methodological review.

Artifact detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice.

Service oriented architecture to support real-time implementation of artifact detection in critical care monitoring.

The quality of automated real-time critical care monitoring is impacted by the degree of signal artifact present in clinical data. This is further complicated when different clinical rules applied for disease detection require source data at different frequencies and different signal quality. This paper proposes a novel multidimensional framework based on service oriented architecture to support real-time implementation of clinical artifact detection in critical care settings.

Integrative learning through the design of an electrocardiogram acquisition system.

This paper presents an electrocardiogram acquisition system course design project for an upper year bioinstrumentation course. The objective of this design project is to provide students an opportunity for an integrative learning, enhancing their educational experience. Unlike similar electrocardiogram instrumentation projects, this project is based on a commercially available instrumentation amplifier enabling better systems-level thinking.