Saurabh Pal

Bio: Saurabh Pal is an academic researcher from University of Calcutta. The author has contributed to research in topics: Signal & Biometrics. The author has an hindex of 12, co-authored 61 publications receiving 742 citations. Previous affiliations of Saurabh Pal include Heritage Institute of Technology & Haldia Institute of Technology.

Significance of Exhaled Breath Test in Clinical Diagnosis: A Special Focus on the Detection of Diabetes Mellitus

Abstract: Analysis of volatile organic compounds (VOCs) emanating from human exhaled breath can provide deep insight into the status of various biochemical processes in the human body. VOCs can serve as potential biomarkers of physiological and pathophysiological conditions related to several diseases. Breath VOC analysis, a noninvasive and quick biomonitoring approach, also has potential for the early detection and progress monitoring of several diseases. This paper gives an overview of the major VOCs present in human exhaled breath, possible biochemical pathways of breath VOC generation, diagnostic importance of their analysis, and analytical techniques used in the breath test. Breath analysis relating to diabetes mellitus and its characteristic breath biomarkers is focused on. Finally, some challenges and limitations of the breath test are discussed.

Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data

Abstract: Incorporation of automated electrocardiogram (ECG) analysis techniques in home monitoring applications can ensure early detection of myocardial infarction (MI), thus reducing the risk of mortality. Most of the published techniques use advanced signal processing tools, a huge number of ECG features, and complex classifiers, which make their hardware implementation difficult. This paper proposes the use of harmonic phase distribution pattern of the ECG data for MI identification. The morphological and temporal changes of the ECG waveform caused by the presence of MI are reflected in the phase distribution pattern of the Fourier harmonics. Two discriminative features, clearly reflecting these variations, are identified for each of the three standard ECG leads (II, III, and V2). Classification of the healthy and MI data is performed using a threshold-based classification rule and logistic regression. The proposed technique has achieved an average detection accuracy of 95.6% with sensitivity and specificity of 96.5% and 92.7%, respectively, for classifying all types of MI data from the Physionet Physikalisch-Technische Bundesanstalt diagnostic ECG database. The robustness of the algorithm is confirmed with real data as well. The algorithm is also implemented and validated on a microcontroller-based Arduino board, which can serve as a prototype ECG analysis device. Apart from providing comparable performance to other reported techniques, the proposed technique provides distinct advantages in terms of computational simplicity of the features, significantly reduced feature dimension, and use of simple linear classifiers which ensure faster and easier MI identification.

The Merck Index

Abstract: The Merck Index of Chemicals and Drugs An Encyclopedia for the Chemist, Pharmacist, Physician and Allied Professions Sixth edition Pp xiv + 1167 (Rahway, NJ: Merck and Company, Inc, 1952) 750 dollars; thumb-indexed, 8 dollars

Structural and Chemical Biology of Terpenoid Cyclases.

Abstract: The year 2017 marks the twentieth anniversary of terpenoid cyclase structural biology: a trio of terpenoid cyclase structures reported together in 1997 were the first to set the foundation for understanding the enzymes largely responsible for the exquisite chemodiversity of more than 80000 terpenoid natural products. Terpenoid cyclases catalyze the most complex chemical reactions in biology, in that more than half of the substrate carbon atoms undergo changes in bonding and hybridization during a single enzyme-catalyzed cyclization reaction. The past two decades have witnessed structural, functional, and computational studies illuminating the modes of substrate activation that initiate the cyclization cascade, the management and manipulation of high-energy carbocation intermediates that propagate the cyclization cascade, and the chemical strategies that terminate the cyclization cascade. The role of the terpenoid cyclase as a template for catalysis is paramount to its function, and protein engineering can...

Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

Abstract: We propose new features for classification of epileptic seizure EEG signals.Features were extracted from PSR of IMFs of EEG signals.We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.LS-SVM classifier has been used for classification with the proposed features.Results were compared with other existing methods studied on the same EEG dataset. Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals.