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Browsing MIT Open Access Articles by Author "Guttag, John V."

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Browsing MIT Open Access Articles by Author "Guttag, John V."

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  • Syed, Zeeshan; Guttag, John V.; Stultz, Collin M. (Springer, 2007-03)
    This paper describes novel fully automated techniques for analyzing large amounts of cardiovascular data. In contrast to traditional medical expert systems our techniques incorporate no a priori knowledge about disease ...
  • Qureshi, Asfandyar; Weber, Rick; Balakrishnan, Hari; Guttag, John V.; Maggs, Bruce (Association for Computing Machinery / ACM Special Interest Group on Data Communications, 2009-08)
    Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this ...
  • Qureshi, Asfandyar; Weber, Rick; Balakrishnan, Hari; Guttag, John V.; Maggs, Bruce (Association for Computing Machinery, 2009-08)
    Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this ...
  • Singh, Anima; Liu, J.; Guttag, John V. (Institute of Electrical and Electronics Engineers (IEEE), 2011-03)
    We investigate several entropy based approaches to finding cut points for discretizing continuous ECG-based risk metrics. We describe two existing approaches, Shannon entropy and asymmetric entropy, and one new approach, ...
  • Syed, Z.; Scirica, B. M.; Stultz, Collin M.; Guttag, John V. (Institute of Electrical and Electronics Engineers, 2010-04)
    Information in electrocardiographic (ECG) signals is widely believed to have value in the short-term prediction of arrhythmias. This study evaluates the use of morphologic variability (MV), a recently proposed metric ...
  • Wu, Hao-Yu; Rubinstein, Michael; Shih, Eugene; Guttag, John V.; Durand, Fredo; Freeman, William T. (Association for Computing Machinery, 2012-07)
    Our goal is to reveal temporal variations in videos that are difficult or impossible to see with the naked eye and display them in an indicative manner. Our method, which we call Eulerian Video Magnification, takes a ...
  • Qureshi, Asfandyar; Shih, Eugene; Fan, Irene; Carlisle, Jennifer; Brezinski, Donna; Kleinman, Monica; Guttag, John V. (AMIA, 2010-11)
    Telemedicine has had a positive impact on some aspects of patient care. However, existing telemedicine systems that use high-quality video are inflexible, requiring investment in fixed infrastructure. We have overcome a ...
  • Syed, Zeeshan; Indyk, Piotr; Guttag, John V. (MIT Press, 2009-08)
    In this paper, we present an automated approach to discover patterns that can distinguish between sequences belonging to different labeled groups. Our method searches for approximately conserved motifs that occur with ...
  • Verma, Naveen; Shoeb, Ali H.; Bohorquez, Jose L.; Dawson, Joel L.; Guttag, John V.; Chandrakasan, Anantha P. (Institute of Electrical and Electronics Engineers (IEEE), 2010-04)
    This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on ...
  • Verma, Naveen; Shoeb, Ali H.; Chandrakasan, Anantha P.; Guttag, John V. (Institute of Electrical and Electronics Engineers, 2009-08)
    Continuous on-scalp EEG monitoring provides a non-invasive means to detect the onset of seizures in epilepsy patients, but cables from the scalp pose a severe strangulation hazard during convulsions. Since the power of ...
  • Syed, Zeeshan; Stultz, Collin M.; Kellis, Manolis; Indyk, Piotr; Guttag, John V. (Association for Computing Machinery (ACM), 2010-01)
    In this article, we propose a methodology for identifying predictive physiological patterns in the absence of prior knowledge. We use the principle of conservation to identify activity that consistently precedes an outcome ...
  • Wiens, J.; Guttag, John V. (Institute of Electrical and Electronics Engineers (IEEE), 2010-09)
    A major challenge in applying machine learning techniques to the problem of heartbeat classification is dealing effectively with inter-patient differences in electrocardiograms (ECGs). Inter-patient differences create a ...
  • Sung, Phil; Syed, Zeeshan; Guttag, John V. (Institute of Electrical and Electronics Engineers, 2009-04)
    This paper examines strategies to quantify differences in the morphology of time series while accounting for time skew in the observed data. We adapt four measures originally designed for signal shape comparison: Dynamic ...
  • Ganeshapillai, Gartheeban; Guttag, John V. (Springer Science + Business Media B.V., 2012-08)
    A modern intensive care unit (ICU) has automated analysis systems that depend on continuous uninterrupted real time monitoring of physiological signals such as electrocardiogram (ECG), arterial blood pressure (ABP), and ...
  • Ganeshapillai, Gartheeban; Liu, Jessica F.; Guttag, John V. (Institute of Electrical and Electronics Engineers (IEEE), 2011-12)
    We present an approach to identifying and reconstructing corrupted regions in a multi-parameter physiological signal. The method, which uses information in correlated signals, is specifically designed to preserve clinically ...
  • Shih, Eugene Inghaw; Shoeb, Ali H.; Guttag, John V. (Association for Computing Machinery, 2009-06)
    Epilepsy affects over three million Americans of all ages. Despite recent advances, more than 20% of individuals with epilepsy never achieve adequate control of their seizures. The use of a small, portable, non-invasive ...
  • Syed, Zeeshan; Guttag, John V. (Association for Computing Machinery, 2011-03)
    In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac ...
  • Ganeshapillai, Gartheeban; Guttag, John V. (Biosignals, 2011-01)
    We present a novel approach to segmenting a quasiperiodic multi-parameter physiological signal in the presence of noise and transient corruption. We use Weighted Time Warping (WTW), to combine the partially correlated ...
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