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Sakyajit Bhattacharya

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3 papers

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JBHI Journal 2022 Journal Article

Synthetic PPG Signal Generation to Improve Coronary Artery Disease Classification: Study With Physical Model of Cardiovascular System

Oishee Mazumder
Rohan Banerjee
Dibyendu Roy
Sakyajit Bhattacharya
Avik Ghose
Aniruddha Sinha

This paper presents a novel approach of generating synthetic Photoplethysmogram (PPG) data using a physical model of the cardiovascular system to improve classifier performance with a combination of synthetic and real data. The physical model is an in-silico cardiac computational model, consisting of a four-chambered heart with electrophysiology, hemodynamic, and blood pressure auto-regulation functionality. Starting with a small number of measured PPG data, the cardiac model is used to synthesize healthy as well as PPG time-series pertaining to coronary artery disease (CAD) by varying pathophysiological parameters. A Variational Autoencoder (VAE) structure is proposed to derive a statistical feature space for CAD classification. Results are presented in two perspectives namely, (i) using artificially reduced real disease data and (ii) using all the real disease data. In both cases, by augmenting with the synthetic data for training, the performance (sensitivity, specificity) of the classifier changes from (i) (0. 65, 1) to (1, 0. 9) and (ii) (1, 0. 95) to (1, 1). The proposed hybrid approach of combining physical modelling and statistical feature space selection generates realistic PPG data with pathophysiological interpretation and can outperform a baseline Generative Adversarial Network (GAN) architecture with a relatively small amount of real data for training. This proposed method could aid as a substitution technique for handling the problem of bulk data required for training machine learning algorithms for cardiac health-care applications.

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AAAI Conference 2017 Conference Paper

ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets

Sakyajit Bhattacharya
Vaibhav Rajan
Harsh Shrivastava

Determining mortality risk is important for critical decisions in Intensive Care Units (ICU). The need for machine learning models that provide accurate patient-speciﬁc prediction of mortality is well recognized. We present a new algorithm for ICU mortality prediction that is designed to address the problem of imbalance, which occurs, in the context of binary classiﬁcation, when one of the two classes is signiﬁcantly under–represented in the data. We take a fundamentally new approach in exploiting the class imbalance through a feature transformation such that the transformed features are easier to classify. Hypothesis testing is used for classiﬁcation with a test statistic that follows the distribution of the difference of two χ2 random variables, for which there are no analytic expressions and we derive an accurate approximation. Experiments on a benchmark dataset of 4000 ICU patients show that our algorithm surpasses the best competing methods for mortality prediction.

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IJCAI Conference 2016 Conference Paper

Dependency Clustering of Mixed Data with Gaussian Mixture Copulas

Vaibhav Rajan
Sakyajit Bhattacharya

Heterogeneous data with complex feature dependencies is common in real-world applications. Clustering algorithms for mixed - continuous and discrete valued - features often do not adequately model dependencies and are limited to modeling meta-Gaussian distributions. Copulas, that provide a modular parameterization of joint distributions, can model a variety of dependencies but their use with discrete data remains limited due to challenges in parameter inference. In this paper we use Gaussian mixture copulas, to model complex dependencies beyond those captured by meta-Gaussian distributions, for clustering. We design a new, efficient, semiparametric algorithm to approximately estimate the parameters of the copula that can fit continuous, ordinal and binary data. We analyze the conditions for obtaining consistent estimates and empirically demonstrate performance improvements over state-of-the-art methods of correlation clustering on synthetic and benchmark datasets.

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