Rebeca Salas-Boni

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Over the past few years, reducing the number of false positive cardiac monitor alarms (FA) in the intensive care unit (ICU) has become an issue of the utmost importance. In our work, we developed a robust methodology that, without the need for additional non-ECG waveforms, suppresses false positive ventricular tachycardia (VT) alarms without resulting in(More)
PURPOSE Physiologic monitors are plagued with alarms that create a cacophony of sounds and visual alerts causing "alarm fatigue" which creates an unsafe patient environment because a life-threatening event may be missed in this milieu of sensory overload. Using a state-of-the-art technology acquisition infrastructure, all monitor data including 7 ECG leads,(More)
QRS detection based on ECG signal is the most straightforward method for heart beat detection. However, existing QRS detection methods do not work well when ECG signal is contaminated or missing. Other physiological signals also contain information about cardiac activity and ECG. Their information can be explored for robust heart beat detection. As part of(More)
Patient monitors in hospitals generate a high number of false alarms that compromise patients care and burden clinicians. In our previous work, an attempt to alleviate this problem by finding combinations of monitor alarms and laboratory test that were predictive of code blue events, called SuperAlarms. Our current work consists of developing a novel time(More)
QRS peak detection is a challenging problem when ECG signal is corrupted. However, additional physiological signals may also provide information about the QRS position. In this study, we focus on a unique benchmark provided by PhysioNet/Computing in Cardiology Challenge 2014 and Physiological Measurement focus issue: robust detection of heart beats in(More)
Many frequently arising problems involve finding the small-dimensional subspace that best captures the variablity of a set of observations belonging to a larger space, for example, finding its principal components. We propose an algorithm that finds this subspace through a series of orthogonal rotations, each represented as the exponential of a(More)
A procedure is developed for obtaining the lower dimensional representation of highdimensional observations stemming from different classes that best distinguishes among the classes. The method finds a low dimensional subspace such that the estimated probability of the projected data belonging to each population is as close to the true assignment as(More)
Background: Traditional methods for deciding whether two populations are distinguishable or not often make assumptions on the distribution of the data, and use these to measure dissimilarity. In this article a novel approach is proposed, where the performance of classifiers namely, their accuracy or MSE is used to determine whether two populations are(More)
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