Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation.

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.

Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study.

Background: Predicting the onset of hemodynamic instability before it occurs remains a sought-after goal in acute and critical care medicine. Technologies that allow for this may assist clinicians in preventing episodes of hemodynamic instability (EHI). We tested a novel noninvasive technology, the Analytic for Hemodynamic Instability-Predictive Indicator (AHI-PI), which analyzes a single lead of electrocardiogram (ECG) and extracts heart rate variability and morphologic waveform features to predict an EHI prior to its occurrence.

Detection of Hemodynamic Status Using an Analytic Based on an Electrocardiogram Lead Waveform.

Objectives: Delayed identification of hemodynamic deterioration remains a persistent issue for in-hospital patient care. Clinicians continue to rely on vital signs associated with tachycardia and hypotension to identify hemodynamically unstable patients. A novel, noninvasive technology, the Analytic for Hemodynamic Instability (AHI), uses only the continuous electrocardiogram (ECG) signal from a typical hospital multiparameter telemetry monitor to monitor hemodynamics.

Novel Noninvasive Method of Cerebrovascular Blood Volume Assessment Using Brain Bioimpedance.

Cerebrovascular autoregulation (CAR) is the ability of vessels to modulate their tone in response to changes in pressure. As an auto-protective mechanism, CAR is critical in preventing secondary brain injury post-trauma. Monitoring of changes in cerebral blood volume might be valuable in evaluating CAR and response to various therapies.

Suppression of false arrhythmia alarms in the ICU: a machine learning approach.

This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of beat detection algorithms, some of which are developed by the authors.

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation.

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician's disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality.

Big Data Analytics in Healthcare.

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration.

An automated dental caries detection and scoring system for optical images of tooth occlusal surface.

Dental caries are one of the most prevalent chronic diseases. The management of dental caries demands detection of carious lesions at early stages. This study aims to design an automated system to detect and score caries lesions based on optical images of the occlusal tooth surface according to the International Caries Detection and Assessment System (ICDAS) guidelines.

A hierarchical method for removal of baseline drift from biomedical signals: application in ECG analysis.

Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised.

Automated midline shift and intracranial pressure estimation based on brain CT images.

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching.

An entropy-based automated cell nuclei segmentation and quantification: application in analysis of wound healing process.

The segmentation and quantification of cell nuclei are two very significant tasks in the analysis of histological images. Accurate results of cell nuclei segmentation are often adapted to a variety of applications such as the detection of cancerous cell nuclei and the observation of overlapping cellular events occurring during wound healing process in the human body. In this paper, an automated entropy-based thresholding system for segmentation and quantification of cell nuclei from histologically stained images has been presented.

Biomedical informatics for computer-aided decision support systems: a survey.

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades.

Heart rate variability analysis during central hypovolemia using wavelet transformation.

Detection of hypovolemia prior to overt hemodynamic decompensation remains an elusive goal in the treatment of critically injured patients in both civilian and combat settings. Monitoring of heart rate variability has been advocated as a potential means to monitor the rapid changes in the physiological state of hemorrhaging patients, with the most popular methods involving calculation of the R-R interval signal's power spectral density (PSD) or use of fractal dimensions (FD). However, the latter method poses technical challenges, while the former is best suited to stationary signals rather than the non-stationary R-R interval.

An automated optimal engagement and attention detection system using electrocardiogram.

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis.