Electrical and Electronic Engineering - Research Publications
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ItemComplex Correlation Measure: a novel descriptor for Poincare plot(BMC, 2009-08-13)BACKGROUND: Poincaré plot is one of the important techniques used for visually representing the heart rate variability. It is valuable due to its ability to display nonlinear aspects of the data sequence. However, the problem lies in capturing temporal information of the plot quantitatively. The standard descriptors used in quantifying the Poincaré plot (SD1, SD2) measure the gross variability of the time series data. Determination of advanced methods for capturing temporal properties pose a significant challenge. In this paper, we propose a novel descriptor "Complex Correlation Measure (CCM)" to quantify the temporal aspect of the Poincaré plot. In contrast to SD1 and SD2, the CCM incorporates point-to-point variation of the signal. METHODS: First, we have derived expressions for CCM. Then the sensitivity of descriptors has been shown by measuring all descriptors before and after surrogation of the signal. For each case study, lag-1 Poincaré plots were constructed for three groups of subjects (Arrhythmia, Congestive Heart Failure (CHF) and those with Normal Sinus Rhythm (NSR)), and the new measure CCM was computed along with SD1 and SD2. ANOVA analysis distribution was used to define the level of significance of mean and variance of SD1, SD2 and CCM for different groups of subjects. RESULTS: CCM is defined based on the autocorrelation at different lags of the time series, hence giving an in depth measurement of the correlation structure of the Poincaré plot. A surrogate analysis was performed, and the sensitivity of the proposed descriptor was found to be higher as compared to the standard descriptors. Two case studies were conducted for recognizing arrhythmia and congestive heart failure (CHF) subjects from those with NSR, using the Physionet database and demonstrated the usefulness of the proposed descriptors in biomedical applications. CCM was found to be a more significant (p = 6.28E-18) parameter than SD1 and SD2 in discriminating arrhythmia from NSR subjects. In case of assessing CHF subjects also against NSR, CCM was again found to be the most significant (p = 9.07E-14). CONCLUSION: Hence, CCM can be used as an additional Poincaré plot descriptor to detect pathology.
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ItemJeeva: Enterprise Grid Enabled Web Portal for Protein Secondary Structure Prediction(IEEE, 2008)This paper presents a Grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise Grid technology. The portal is used by research scientists to discover new prediction structures in a parallel manner. An SVM (Support Vector Machine)-based prediction algorithm is used with 64 sample protein sequences as a case study to demonstrate the potential of enterprise Grids.
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ItemProtein topology classification using two-stage support vector machines.(Universal Academy Press, 2006)The determination of the first 3-D model of a protein from its sequence alone is a non-trivial problem. The first 3-D model is the key to the molecular replacement method of solving phase problem in x-ray crystallography. If the sequence identity is more than 30%, homology modelling can be used to determine the correct topology (as defined by CATH) or fold (as defined by SCOP). If the sequence identity is less than 25%, however, the task is very challenging. In this paper we address the topology classification of proteins with sequence identity of less than 25%. The input information to the system is amino acid sequence, the predicted secondary structure and the predicted real value relative solvent accessibility. A two stage support vector machine (SVM) approach is proposed for classifying the sequences to three different structural classes (alpha, beta, alpha+beta) in the first stage and 39 topologies in the second stage. The method is evaluated using a newly curated dataset from CATH with maximum pairwise sequence identity less than 25%. An impressive overall accuracy of 87.44% and 83.15% is reported for class and topology prediction, respectively. In the class prediction stage, a sensitivity of 0.77 and a specificity of 0.91 is obtained. Data file, SVM implementation (SVMHEAVY) and result files can be downloaded from http://www.ee.unimelb.edu.au/ISSNIP/downloads/.
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ItemDisulphide Bridge Prediction using Fuzzy Support Vector Machines( 2005)One of the major contributors to the native form of protien is cystines forming covalent bonds in oxidized state. The Prediction of such bridges from the sequence is a very challenging task given that the number of bridges will rise exponentially as the number of cystines increases. We propose a novel technique for disulphide bridge prediction based on Fuzzy Support Vector Machines. We call the system DIzzy. In our investigation, we look at disulphide bond connectivity given two Cystines with and without a priori knowledge of the bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features such as the probability of occurrence of each amino acid in different secondary structure states along with psiblast profiles. The performance is compared with normal support vector machines. We evaluate our method and compare it with the existing method using SPX dataset.
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ItemPrediction of cystine connectivity using SVM(BIOMEDICAL INFORMATICS, 2005)One of the major contributors to protein structures is the formation of disulphide bonds between selected pairs of cysteines at oxidized state. Prediction of such disulphide bridges from sequence is challenging given that the possible combination of cysteine pairs as the number of cysteines increases in a protein. Here, we describe a SVM (support vector machine) model for the prediction of cystine connectivity in a protein sequence with and without a priori knowledge on their bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features (probability of occurrence of each amino acid residue in different secondary structure states along with PSI-blast profiles). We evaluate our method in SPX (an extended dataset of SP39 (swiss-prot 39) and SP41 (swiss-prot 41) with known disulphide information from PDB) dataset and compare our results with the recursive neural network model described for the same dataset.
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ItemPROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION(WORLD SCIENTIFIC PUBL CO PTE LTD, 2006-12)Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q3accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on .
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ItemAutomated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009-11)Obstructive sleep apnea or hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/hypopnea index (HI) (AHI). Total 82,535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 hypopnea events, and 3151 ECG epochs from 1862 apnea events were collected from 17 patients in the training set. Two-staged feedforward neural network model was trained using features from ECG signals with leave-one-patient-out cross-validation technique. At the first stage of classification, events (apnea and hypopnea) were classified from normal breathing events, and at the second stage, hypopneas were identified from apnea. Independent test was performed on 16 subjects' ECGs containing 483 hypopnea and 1352 apnea events. The cross-validation and independent test accuracies of apnea and hypopnea detection were found to be 94.84% and 76.82%, respectively, for training set, and 94.72% and 79.77%, respectively, for test set. The Bland-Altman plots showed unbiased estimations with standard deviations of +/- 2.19, +/- 2.16, and +/- 3.64 events/h for AI, HI, and AHI, respectively. Results indicate the possibility of recognizing apnea/hypopnea events based on shorter segments of ECG signals.