Chathuri Daluwatte

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.

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 Blood was withdrawn until a drop in mean arterial pressure (MAP) of 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.

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.

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.

A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects

Assessing respiratory and cardiovascular system coupling can provide new insights into disease progression, but requires accurate analysis of each signal. Respiratory waveform data collected during spontaneous breathing are noisy and respiration rates from long term physiological experiments can vary over a wide range across time. There is a need for automatic and robust algorithms to detect breathing peaks in respiration signals for assessment of the coupling between the respiratory and cardiovascular systems. We developed an automatic algorithm to detect breathing peaks from a respiration signal. The algorithm was tested on respiration signals collected during hemorrhage in a conscious ovine model (N=9, total length = 11.0h). The breathing rate varied from 15 to as high as 160 breaths/min for some animals during the hemorrhage protocol. The sensitivity of the algorithm to detect respiration peaks was 93.7% with a precision of 94.5%. The developed algorithm presents a promising approach to detect breathing peaks in respiration signals from spontaneously breathing subjects. The algorithm was able to consistently identify breathing peaks while the breathing rate varied from 15 to 160 breaths/min.

Multivariate physiological recordings in an experimental hemorrhage model

In this paper we describe a data set of multivariate physiological measurements recorded from conscious sheep (N = 8; 37.4 ± 1.1 kg) during hemorrhage. Hemorrhage was experimentally induced in each animal by withdrawing blood from a femoral artery at two different rates (fast: 1.25 mL/kg/min; and slow: 0.25 mL/kg/min). Data, including physiological waveforms and continuous/intermittent measurements, were transformed to digital file formats (European Data Format [EDF] for waveforms and Comma-Separated Values [CSV] for continuous and intermittent measurements) as a comprehensive data set and stored and publicly shared here (Appendix A). The data set comprises experimental information (e.g., hemorrhage rate, animal weight, event times), physiological waveforms (arterial and central venous blood pressure, electrocardiogram), time-series records of non-invasive physiological measurements (SpO2, tissue oximetry), intermittent arterial and venous blood gas analyses (e.g., hemoglobin, lactate, SaO2, SvO2) and intermittent thermodilution cardiac output measurements. A detailed explanation of the hemodynamic and pulmonary changes during hemorrhage is available in a previous publication (Scully et al., 2016) [1].

A novel ECG detector performance metric and its relationship with missing and false heart rate limit alarms

PURPOSE Performance of ECG beat detectors is traditionally assessed on long intervals (e.g.: 30min), but only incorrect detections within a short interval (e.g.: 10s) may cause incorrect (i.e., missed+false) heart rate limit alarms (tachycardia and bradycardia). We propose a novel performance metric based on distribution of incorrect beat detection over a short interval and assess its relationship with incorrect heart rate limit alarm rates. BASIC PROCEDURES Six ECG beat detectors were assessed using performance metrics over long interval (sensitivity and positive predictive value over 30min) and short interval (Area Under empirical cumulative distribution function (AUecdf) for short interval (i.e., 10s) sensitivity and positive predictive value) on two ECG databases. False heart rate limit and asystole alarm rates calculated using a third ECG database were then correlated (Spearmans rank correlation) with each calculated performance metric. MAIN FINDINGS False alarm rates correlated with sensitivity calculated on long interval (i.e., 30min) (ρ=-0.8 and p<0.05) and AUecdf for sensitivity (ρ=0.9 and p<0.05) in all assessed ECG databases. Sensitivity over 30min grouped the two detectors with lowest false alarm rates while AUecdf for sensitivity provided further information to identify the two beat detectors with highest false alarm rates as well, which was inseparable with sensitivity over 30min. PRINCIPAL CONCLUSIONS Short interval performance metrics can provide insights on the potential of a beat detector to generate incorrect heart rate limit alarms.

Progression and variability of physiologic deterioration in an ovine model of lung infection sepsis

In this study, a lung infection model of pneumonia in sheep (n = 12) that included smoke inhalation injury followed by methicillin-resistant Staphylococcus aureus placement into the lungs was used to investigate hemodynamic and pulmonary dysfunctions during the course of sepsis progression. To assess the variability in disease progression, animals were retrospectively divided into survivor (n = 6) and nonsurvivor (n = 6) groups, and a range of physiological indexes reflecting hemodynamic and pulmonary function were estimated and compared to evaluate variability in dynamics underlying sepsis development. Blood pressure and heart rate variability analyses were performed to assess whether they discriminated between the survivor and nonsurvivor groups early on and after intervention. Results showed hemodynamic deterioration in both survivor and nonsurvivor animals during sepsis along with a severe oxygenation disruption (decreased peripheral oxygen saturation) in nonsurvivors separating them from survivor animals of this model. Variability analysis of beat-to-beat heart rate and blood pressure reflected physiologic deterioration during infection for all animals, but these analyses did not discriminate the nonsurvivor animals from survivor animals.NEW & NOTEWORTHY Variable pulmonary response to injury results in varying outcomes in a previously reported animal model of lung injury and methicillin-resistant Staphylococcus aureus-induced sepsis. Heart rate and blood pressure variability analyses were investigated to track the varying levels of physiologic deterioration but did not discriminate early nonsurvivors from survivors.