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. METHODS: Retrospective cohort study at a quaternary care academic health system using data from hospitalized adult patients between August 2019 and April 2020 undergoing continuous ECG monitoring with intermittent noninvasive blood pressure (NIBP) or with continuous intraarterial pressure (IAP) monitoring. RESULTS: AHI-PI's low and high-risk indications were compared with the presence of EHI in the future as indicated by vital signs (heart rate > 100 beats/min with a systolic blood pressure < 90 mmHg or a mean arterial blood pressure of < 70 mmHg). 4,633 patients were analyzed (3,961 undergoing NIBP monitoring, 672 with continuous IAP monitoring). 692 patients had an EHI (380 undergoing NIBP, 312 undergoing IAP). For IAP patients, the sensitivity and specificity of AHI-PI to predict EHI was 89.7% and 78.3% with a positive and negative predictive value of 33.7% and 98.4% respectively. For NIBP patients, AHI-PI had a sensitivity and specificity of 86.3% and 80.5% with a positive and negative predictive value of 11.7% and 99.5% respectively. Both groups performed with an AUC of 0.87. AHI-PI predicted EHI in both groups with a median lead time of 1.1 h (average lead time of 3.7 h for IAP group, 2.9 h for NIBP group). CONCLUSIONS: AHI-PI predicted EHIs with high sensitivity and specificity and within clinically significant time windows that may allow for intervention. Performance was similar in patients undergoing NIBP and IAP monitoring.

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. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.

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. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.

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. Both approaches are also limited by high inter- and intra-individual variability, a serious issue when applying these indices to the clinical setting. We propose an approach which applies the discrete wavelet transform (DWT) to the R-R interval signal to extract information at both 500 and 125 Hz sampling rates. The utility of machine learning models based on these features were tested in assessing electrocardiogram signals from volunteers subjected to lower body negative pressure induced central hypovolemia as a surrogate of hemorrhage. These machine learning models based on DWT features were compared against those based on the traditional PSD and FD, at both sampling rates and their performance was evaluated based on leave-one-subject-out fold cross-validation. Results demonstrate that the proposed DWT-based model outperforms individual PSD and FD methods as well as the combination of these two traditional methods at both sample rates of 500 Hz (p value <0.0001) and 125 Hz (p value <0.0001) in detecting the degree of hypovolemia. These findings indicate the potential of the proposed DWT approach in monitoring the physiological changes caused by hemorrhage. The speed and relatively low computational costs in deriving these features may make it particularly suited for implementation in portable devices for remote monitoring.

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. The intent of this study was to determine if AHI is able to predict hemodynamic instability without the need for continuous direct measurement of blood pressure. DESIGN: Retrospective cohort study. SETTING: Single quaternary care academic health system in Michigan. PATIENTS: Hospitalized adult patients between November 2019 and February 2020 undergoing continuous ECG and intra-arterial blood pressure monitoring in an intensive care setting. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One million two hundred fifty-two thousand seven hundred forty-two 5-minute windows of the analytic output were analyzed from 597 consecutive adult patients. AHI outputs were compared with vital sign indications of hemodynamic instability (heart rate > 100 beats/min, systolic blood pressure < 90 mm Hg, and shock index of > 1) in the same window. The observed sensitivity and specificity of AHI were 96.9% and 79.0%, respectively, with an area under the curve (AUC) of 0.90 for heart rate and systolic blood pressure. For the shock index analysis, AHI's sensitivity was 72.0% and specificity was 80.3% with an AUC of 0.81. CONCLUSIONS: The AHI-derived hemodynamic status appropriately detected the various gold standard indications of hemodynamic instability (hypotension, tachycardia and hypotension, and shock index > 1). AHI may provide continuous dynamic hemodynamic monitoring capabilities in patients who traditionally have intermittent static vital sign measurements.

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. In this study, we utilized an ocular-brain bioimpedance interface to assess real time changes in cerebral blood volume in response to a number of physiological challenges. We hypothesize that changes in brain bioimpedance (dz) would track changes in cerebral blood volume. Anesthetized animals were instrumented for monitoring of intracranial pressure (ICP), mean arterial blood pressure, cerebral perfusion pressure (CPP) and cerebral blood flow (CBF). Bioimpedance was monitored continuously through electrocardiographic electrodes placed over the eyelids. Interventions such as hyperventilation, vasopressor administration, creation of an epidural hematoma, and systemic hemorrhage were used to manipulate levels of ICP, CPP, and CBF. The dz correlated with changes in ICP, CPP, and CBF (r = -0.72 to -0.88, p < 0.0001). The receiver operating characteristic for dz at different thresholds of CPP and CBF showed high impedance performance with area under the curve between 0.80-1.00 (p < 0.003) and sensitivity and specificity varying between 83%-100% and 70%-100%, respectively. Our preliminary tests show that brain bioimpedance as measured through the ocular-brain interface tracks changes in CPP and CBF with high precision and may prove to be valuable in the future in assessing changes in cerebral blood volume and CAR.

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. The outputs of the beat detection algorithms are combined using a machine learning approach. For the ventricular tachycardia and ventricular fibrillation alarms, separate classification models are trained to distinguish between the normal and abnormal beats. This information, along with alarm-specific criteria, is used to decide if the alarm is false. The results indicate that the presented method was effective in suppressing false alarms when it was tested on a hidden validation dataset.

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. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.

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. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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. The system detects the tooth boundaries and irregular regions, and extracts 77 features from each image. These features include statistical measures of color space, grayscale image, as well as Wavelet Transform and Fourier Transform based features. Used in this study were 88 occlusal surface photographs of extracted teeth examined and scored by ICDAS experts. Seven ICDAS codes which show the different stages in caries development were collapsed into three classes: score 0, scores 1 and 2, and scores 3 to 6. The system shows accuracy of 86.3%, specificity of 91.7%, and sensitivity of 83.0% in ten-fold cross validation in classification of the tooth images. While the system needs further improvement and validation using larger datasets, it presents promising potential for clinical diagnostics with high accuracy and minimal cost. This is a notable advantage over existing systems requiring expensive imaging and external hardware.

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. The proposed translational computation system aims to integrate clinical insight and computational analysis by identifying and segmenting objects of interest within histological images. Objects of interest and background regions are automatically distinguished by dynamically determining 3 optimal threshold values for the 3 color components of an input image. The threshold values are determined by means of entropy computations that are based on probability distributions of the color intensities of pixels and the spatial similarity of pixel intensities within neighborhoods. The effectiveness of the proposed system was tested over 21 histologically stained images containing approximately 1800 cell nuclei, and the overall performance of the algorithm was found to be promising, with high accuracy and precision values.

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. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

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. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.