Weighted Time Warping for Temporal Segmentation of Multi-Parameter Physiological Signals

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 signals. We then use the relationship between the channels and the repetitive morphology of the time series to partition it into quasiperiodic units by matching it against a constantly evolving template. The method can accurately segment a multi-parameter signal, even when all the individual channels are so corrupted that they cannot be individually segmented. Experiments carried out on MIMIC, a multi-parameter physiological dataset recorded on ICU patients, demonstrate the effectiveness of the method. Our method performs as well as a widely used QRS detector on clean raw data, and outperforms it on corrupted data. Under additive noise at SNR 0 dB the average errors were 5:81 ms for our method and 303:48 ms for the QRS detector. Under transient corruption they were 2:89 ms and 387:32 ms respectively

Learning connections in financial time series

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 125-132).Much of modern financial theory is based upon the assumption that a portfolio containing a diversified set of equities can be used to control risk while achieving a good rate of return. The basic idea is to choose equities that have high expected returns, but are unlikely to move together. Identifying a portfolio of equities that remain well diversified over a future investment period is difficult. In our work, we investigate how to use machine learning techniques and data mining to learn cross-sectional patterns that can be used to design diversified portfolios. Specifically, we model the connections among equities from different perspectives, and propose three different methods that capture the connections in different time scales. Using the "correlation" structure learned using our models, we show how to build selective but well-diversified portfolios. We show that these portfolios perform well on out of sample data in terms of minimizing risk and achieving high returns. We provide a method to address the shortcomings of correlation in capturing events such as large losses (tail risk). Portfolios constructed using our method significantly reduce tail risk without sacrificing overall returns. We show that our method reduces the worst day performance from -15% to -9% and increases the Sharpe ratio from 0.63 to 0.71. We also provide a method to model the relationship between the equity return that is unexplained by the market return (excess return) and the amount of sentiment in news releases that hasn't been already reflected in the price of equities (excess sentiment). We show that a portfolio built using this method generates an annualized return of 34% over a 10-year time period. In comparison, the S&P 500 index generated 5% return in the same time period.by Gartheeban Ganeshapillai.Ph. D

Methods to improve the signal quality of corrupted multi-parameter physiological signals

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 121-125).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 the Photo Plethysmogram (PPG). Unfortunately, these signals are often corrupted by noise, artifacts, and missing data, which can result in a high incidence of false alarms. We present a novel approach to improve the Signal Quality of a multi-parameter physiological signal by identifying the corrupted regions in the signal, and reconstructing them using the information available in correlated signals. The method is specifically designed to preserve the clinically most signicant aspects of the signals. We use template matching to jointly segment the multi-parameter signal, morphological dissimilarity to estimate the quality of the signal segment, similarity search to nd the closest match from a database of templates, and time-warping to reconstruct the corrupted segment using the matching template. Experiments carried out on the MIT-BIH Arrhythmia Database, a multi-parameter ECG database with many clinically signicant arrhythmias, demonstrate the effectiveness of the method. Our method improved the classification accuracy of the beat type by more than 700% on the signal corrupted with white Gaussian noise, and increased the similarity to the original signal, as measured by the normalized residual distance, by more than 250%. When the method was applied to the multi-parameter physiological signal data from Cinc Challenge 2010 database at Physionet.org, our method improved the classification accuracy of beat type by more than 33 times on a signal corrupted with white Gaussian noise, and increased the similarity to the original signal by more than 280%.by Gartheeban Ganeshapillai.S.M

Reconstruction of ECG signals in presence of corruption

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 significant aspects of the signals. We use template matching to jointly segment the multi-parameter signal, morphological dissimilarity to estimate the quality of the signal segment, similarity search using features on a database of templates to find the closest match, and time-warping to reconstruct the corrupted segment with the matching template. In experiments carried out on the MIT-BIH Arrhythmia Database, a two-parameter database with many clinically significant arrhythmias, our method improved the classification accuracy of the beat type by more than 7 times on a signal corrupted with white Gaussian noise, and increased the similarity to the original signal, as measured by the normalized residual distance, by more than 2.5 times.Quanta Computer (Firm