Browsing by Author "Patel, Raajen"

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    A novel automated junctional ectopic tachycardia detection tool for children with congenital heart disease
    (Elsevier, 2022) Waugh, Jamie L. S.; Patel, Raajen; Ju, Yilong; Patel, Ankit B.; Rusin, Craig G.; Jain, Parag N.
    Background Junctional ectopic tachycardia (JET) is a prevalent life-threatening arrhythmia in children with congenital heart disease (CHD), with marked resemblance to normal sinus rhythm (NSR) often leading to delay in diagnosis. Objective To develop a novel automated arrhythmia detection tool to identify JET. Methods A single-center retrospective cohort study of children with CHD was performed. Electrocardiographic (ECG) data produced by bedside monitors is captured automatically by the Sickbay platform. Based on the detection of R and P wave peaks, 2 interpretable ECG features are calculated: P prominence median and PR interval interquartile range (IQR). These features are used as input to a simple logistic regression classification model built to distinguish JET from NSR. Results This study analyzed a total of 64.5 physician-labeled hours consisting of 509,833 cardiac cycles (R-R intervals), from 40 patients with CHD. The extracted P prominence median feature is much smaller in JET compared to NSR, whereas the PR interval IQR feature is larger in JET compared to NSR. The area under the receiver operating characteristic curve for the unseen patient test cohort was 93%. Selecting a threshold of 0.73 results in a true-positive rate of 90% and a false-positive rate of 17%. Conclusion This novel arrhythmia detection tool identifies JET, using 2 distinctive features of JET in ECG—the loss of a normal P wave and PR relationship—allowing for early detection and timely intervention.
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    oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
    (2015-04-21) Patel, Raajen; Baraniuk, Richard G; Veeraraghavan, Ashok; Sorensen, Danny C
    Kernel or similarity matrices are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction. For large datasets, the cost of forming and factoring such kernel matrices becomes intractable. To address this challenge, we introduce a new adaptive sampling algorithm called Accelerated Sequential Incoherence Selection (oASIS) that samples columns without explicitly computing the entire kernel matrix. We provide conditions under which oASIS is guaranteed to exactly recover the kernel matrix with an optimal number of columns selected. Numerical experiments on both synthetic and real-world datasets demonstrate that oASIS achieves performance comparable to state-of-the-art adaptive sampling methods at a fraction of the computational cost. The low runtime complexity of oASIS and its low memory footprint enable the solution of large problems that are simply intractable using other adaptive methods.
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    Rapid Calculation of Molecular Kinetics Using Compressed Sensing
    (American Chemical Society, 2018) Litzinger, Florian; Boninsegna, Lorenzo; Wu, Hao; Nüske, Feliks; Patel, Raajen; Baraniuk, Richard; Noé, Frank; Clementi, Cecilia; Center for Theoretical Biological Physics
    Recent methods for the analysis of molecular kinetics from massive molecular dynamics (MD) data rely on the solution of very large eigenvalue problems. Here we build upon recent results from the field of compressed sensing and develop the spectral oASIS method, a highly efficient approach to approximate the leading eigenvalues and eigenvectors of large generalized eigenvalue problems without ever having to evaluate the full matrices. The approach is demonstrated to reduce the dimensionality of the problem by 1 or 2 orders of magnitude, directly leading to corresponding savings in the computation and storage of the necessary matrices and a speedup of 2 to 4 orders of magnitude in solving the eigenvalue problem. We demonstrate the method on extensive data sets of protein conformational changes and protein-ligand binding using the variational approach to conformation dynamics (VAC) and time-lagged independent component analysis (TICA). Our approach can also be applied to kernel formulations of VAC, TICA, and extended dynamic mode decomposition (EDMD).