Christopher G. Scully

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs.

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise.

Robust algorithm to locate heart beats from multiple physiological waveforms by individual signal detector voting.

Alarm fatigue is a major issue in patient monitoring that could be reduced by merging physiological information from multiple sensors, minimizing the impact of a single sensor failing. We developed a heart beat detection algorithm that utilizes multi-modal physiological waveforms (e.g. ECG, blood pressure, stroke volume, photoplethysmogram and electroencephalogram). The 100 record training set from the Physionet challenge 2014 was used for development. The algorithm was evaluated at three testing phases during the 2014 challenge consisting of 100 (phase I), 200 (phase II) and 300 (phase III) hidden records, respectively. A true positive was declared if a beat was detected within 150 ms of a reference annotation. The algorithm had a sensitivity of >99.9%, Positive Predictive Value of 99.7%, and an overall score (average of sensitivity and Positive Predictive Value) of 99.8% when applied to the training set. The best overall performance on the test sets were: 88.9%, 76.3% and 84.4% for phases I, II and III, respectively. We developed a robust heart beat detector that fuses annotations from multiple individual detectors. The algorithm improves the training results compared to ECG detections alone, and performs well on the test sets. Data fusion approaches like this one can improve patient monitoring and reduce false alarms.

Evaluation of heart rate and blood pressure variability as indicators of physiological compensation to hemorrhage before shock.

ABSTRACT Individual responses to hemorrhage vary, with varying periods of compensation before the development of shock. We characterized heart rate and blood pressure variability measures during hemorrhage of 25 mL/kgBody Weight for 15 min in conscious sheep (N = 7, 14 total hemorrhages) as markers of the transition from compensated to decompensated shock using the continuous wavelet transform. Heart rate–low frequency (HR-LF) and systolic blood pressure–low frequency (SBP-LF) indices were developed to represent the change in spectral power during hemorrhage as low-frequency (0.06 – 0.15 Hz) power divided by the sum of high (0.15 – 1.0 Hz)- and very low (0.02 – 0.06 Hz) frequency power. Heart rate rose from 96.3 (22.2) beats/min (mean [SD] across all trials) to a peak of 176.0 (25.4) beats/min occurring at a minimum time of 5.3 min to a maximum of 22.1 min (11.7 [1.6] min), depending on the trial, after the start of hemorrhage. During the HR-compensated response to hemorrhage, there was elevated HR-LF and SBP-LF in five of the seven animals. In these animals, HR-LF and SBP-LF dropped to below baseline levels around the time of the peak HR. The results from this conscious-animal study suggest that HR and SBP low-frequency power rise during the compensation phase of the response to hemorrhage in conscious sheep. Use of variability monitoring could aid in describing an individual’s current response to hemorrhage and anticipation of impending decompensation; however, individual differences in the response limit this potential.

Effect of hemorrhage rate on early hemodynamic responses in conscious sheep

Physiological compensatory mechanisms can mask the extent of hemorrhage in conscious mammals, which can be further complicated by individual tolerance and variations in hemorrhage onset and duration. We assessed the effect of hemorrhage rate on tolerance and early physiologic responses to hemorrhage in conscious sheep. Eight Merino ewes (37.4 ± 1.1 kg) were subjected to fast (1.25 mL/kg/min) and slow (0.25 mL/kg/min) hemorrhages separated by at least 3 days. Blood was withdrawn until a drop in mean arterial pressure (MAP) of >30 mmHg and returned at the end of the experiment. Continuous monitoring included MAP, central venous pressure, pulmonary artery pressure, pulse oximetry, and tissue oximetry. Cardiac output by thermodilution and arterial blood samples were also measured. The effects of fast versus slow hemorrhage rates were compared for total volume of blood removed and stoppage time (when MAP < 30 mmHg of baseline) and physiological responses during and after the hemorrhage. Estimated blood volume removed when MAP dropped 30 mmHg was 27.0 ± 4.2% (mean ± standard error) in the slow and 27.3 ± 3.2% in the fast hemorrhage (P = 0.47, paired t test between rates). Pressure and tissue oximetry responses were similar between hemorrhage rates. Heart rate increased at earlier levels of blood loss during the fast hemorrhage, but hemorrhage rate was not a significant factor for individual hemorrhage tolerance or hemodynamic responses. In 5/16 hemorrhages MAP stopping criteria was reached with <25% of blood volume removed. This study presents the physiological responses leading up to a significant drop in blood pressure in a large conscious animal model and how they are altered by the rate of hemorrhage.

Advancing Regulatory Science to Bring Novel Medical Devices for Use in Emergency Care to Market: The Role of the Food and Drug Administration

The Food and Drug Administration (FDA) performs regulatory science to provide science-based medical product regulatory decisions. This article describes the types of scientific research the FDAs Center for Devices and Radiological Health performs and highlights specific projects related to medical devices for emergency medicine. In addition, this article discusses how results from regulatory science are used by the FDA to support the regulatory process as well as how the results are communicated to the public. Regulatory science supports the FDAs mission to assure safe, effective, and high-quality medical products are available to patients.

Heartbeat fusion algorithm to reduce false alarms for arrhythmias

There is a need for patient monitoring algorithms to reduce alarm fatigue by rejecting clinically irrelevant alarms. We developed an algorithm using multimodal physiological waveforms (electrocardiogram, blood pressure, photoplethysmogram) and noise classifiers to improve arrhythmia detection by reducing the incidence of false alarms while maintaining a high true alarm rate as part of the Physionet Challenge 2015. Combining information from multiple physiological signals our algorithm was able to discard 362 of 456 false alarms (true negative rate [TNR] of 79%), while correctly classifying 268 of the 294 true alarms (true positive rate [TPR] of 91%) on the training set, a score of 73.8. When applied to the test set which had 343 false alarms and 157 true alarms, we achieved a TNR of 81%, TPR of 86% and score of 70.2. Our results support the concept that false alarms can be reduced in the intensive care unit by removing noise segments in signals and combining information from multiple physiological signals.

The Mixing Rate of the Arterial Blood Pressure Waveform Markov Chain is Correlated with Shock Index during Hemorrhage in Anesthetized Swine

Identifying the need for interventions during hemorrhage is complicated due to physiological compensation mechanisms that can stabilize vital signs until a significant amount of blood loss. Physiological systems providing compensation during hemorrhage affect the arterial blood pressure waveform through changes in dynamics and waveform morphology. We investigated the use of Markov chain analysis of the arterial blood pressure waveform to monitor physiological systems changes during hemorrhage. Continuous arterial blood pressure recordings were made on anesthetized swine (N=7) during a 5 min baseline period and during a slow hemorrhage (10 ml/kg over 30 min). Markov chain analysis was applied to 20 sec arterial blood pressure waveform segments with a sliding window. 20 ranges of arterial blood pressure were defined as states and empirical transition probability matrices were determined for each 20 sec segment. The mixing rate (2nd largest eigenvalue of the transition probability matrix) was determined for all segments. A change in the mixing rate from baseline estimates was identified during hemorrhage for each animal (median time of 13 min, ~10% estimated blood volume, with minimum and maximum times of 2 and 33 min, respectively). The mixing rate was found to have an inverse correlation with shock index for all 7 animals (median correlation coefficient of -0.95 with minimum and maximum of -0.98 and -0.58, respectively). The Markov chain mixing rate of arterial blood pressure recordings is a novel potential biomarker for monitoring and understanding physiological systems during hemorrhage.

Control-oriented physiological modeling of hemodynamic responses to blood volume perturbation

This paper presents a physiological model to reproduce hemodynamic responses to blood volume perturbation. The model consists of three sub-models: a control-theoretic model relating blood volume response to blood volume perturbation; a simple physics-based model relating blood volume to stroke volume and cardiac output; and a phenomenological model relating cardiac output to blood pressure. A unique characteristic of this model is its balance for simplicity and physiological transparency. Initial validity of the model was examined using experimental data collected from 11 animals. The model may serve as a viable basis for the design and evaluation of closed-loop fluid resuscitation controllers.

Evaluating performance of early warning indices to predict physiological instabilities

Patient monitoring algorithms that analyze multiple features from physiological signals can produce an index that serves as a predictive or prognostic measure for a specific critical health event or physiological instability. Classical detection metrics such as sensitivity and positive predictive value are often used to evaluate new patient monitoring indices for such purposes, but since these metrics do not take into account the continuous nature of monitoring, the assessment of a warning system to notify a user of a critical health event remains incomplete. In this article, we present challenges of assessing the performance of new warning indices and propose a framework that provides a more complete characterization of warning index performance predicting a critical event that includes the timeliness of the warning. The framework considers 1) an assessment of the sensitivity to provide a notification within a meaningful time window, 2) the cumulative sensitivity leading up to an event, 3) characteristics on if the warning stays on until the event occurs once a warning has been activated, and 4) the distribution of warning times and the burden of additional warnings (e.g., false-alarm rate) throughout monitoring that may or may not be associated with the event of interest. Using an example from an experimental study of hemorrhage, we examine how this characterization can differentiate two warning systems in terms of timeliness of warnings and warning burden.

Variability analysis for noisy physiological signals: A simulation study

Physiological monitoring is prone to artifacts originating from various sources such as motion, device malfunction, and interference. The artifact occurrence not only elevates false alarm rates in clinics but also complicates data analysis in research. When techniques to characterize signal dynamics and the underlying physiology are applied (e.g., heart rate variability), noise and artifacts can produce misleading results that describe the signal artifacts more than the physiology. Signal quality metrics can be applied to identify signal segments with noise and artifacts that would otherwise lead analyses to produce non-physiologic or misleading results. In this study we utilized simulated electrocardiogram signals and artifacts to demonstrate effects of noise on heart rate variability frequency domain methods. We then used these simulations to assess an automated artifact correction algorithm that included a signal quality index comparing electrocardiogram beats to a beat template. Simulation results show that the proposed algorithm can significantly improve estimation of signal spectra in presence of various artifacts. This algorithm can be applied to automatically clean real world physiological time series before conducting variability analysis.