Development of an Automated Updated Selvester QRS Scoring System Using SWT-Based QRS Fractionation Detection and Classification

The Selvester score is an effective means for estimating the extent of myocardial scar in a patient from low-cost ECG recordings. Automation of such a system is deemed to help implementing low-cost high-volume screening mechanisms of scar in the primary care. This paper describes, for the first time to the best of our knowledge, an automated implementation of the updated Selvester scoring system for that purpose, where fractionated QRS morphologies and patterns are identified and classified using a novel stationary wavelet transform (SWT)-based fractionation detection algorithm. This stage informs the two principal steps of the updated Selvester scoring scheme-the confounder classification and the point awarding rules. The complete system is validated on 51 ECG records of patients detected with ischemic heart disease. Validation has been carried out using manually detected confounder classes and computation of the actual score by expert cardiologists as the ground truth. Our results show that as a stand-alone system it is able to classify different confounders with 94.1% accuracy whereas it exhibits 94% accuracy in computing the actual score. When coupled with our previously proposed automated ECG delineation algorithm, that provides the input ECG parameters, the overall system shows 90% accuracy in confounder classification and 92% accuracy in computing the actual score and thereby showing comparable performance to the stand-alone system proposed here, with the added advantage of complete automated analysis without any human intervention

On the detection of myocardial scar based on ECG/VCG analysis

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%)

Personalized reduced 3-lead system formation methodology for Remote Health Monitoring applications and reconstruction of standard 12-lead system

Remote Health Monitoring (RHM) applications encounter limitations from technological front viz. bandwidth, storage and transmission time and the medical science front i.e. usage of 2-3 lead systems instead of the standard 12-lead (S12) system. Technological limitations constraint the number of leads to 2-3 while cardiologists accustomed with 12-Lead ECG may find these 2-3 lead systems insufficient for diagnosis. Thus, the aforementioned limitations pose self-contradicting challenges for RHM. A personalized reduced 2/3 lead system is required which can offer equivalent information as contained in S12 system, so as to accurately reconstruct S12 system from reduced lead system for diagnosis. In this paper, we propose a personalized reduced 3-lead (R3L) system formation methodology which employs principal component analysis, thereby, reducing redundancy and increasing SNR ratio, hence, making it suitable for wireless transmission. Accurate S12 system is made available using personalized lead reconstruction methodology, thus addressing medical constraints. Mean R2 statistics values obtained for reconstruction of S12 system from the proposed R3L system using PhysioNet's PTB and TWA databases were 95.63% and 96.37% respectively. To substantiate the superior diagnostic quality of reconstructed leads, root mean square error (RMSE) metrics obtained upon comparing the ECG features extracted from the original and reconstructed leads, using our recently proposed Time Domain Morphology and Gradient (TDMG) algorithm, have been analyzed and discussed. The proposed system does not require any extra electrode or modification in placement positions and hence, can readily find application in computerized ECG machines

ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations

International audienceIn this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of-8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 msec and 22 msec, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 msec and 29 msec, the proposed method achieves better accuracy and smaller variability with respect to other methods. Keywords: Electrocardiogram (ECG), Extended Kalman Filter (EKF), Dynamic Time Warping (DTW), Fiducial Point Extraction, Denoising

Phase Space Reconstruction Based CVD Classifier Using Localized Features

This is the final version. Available on open access from Nature Research via the DOI in this recordData Availability: The datasets analysed during the current study are available in the ‘PhysioNet’; the web address is [https://physionet.org/cgi-bin/atm/ATM].This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients’ data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.Department of Science & Technology (DST

A modular low-complexity ECG delineation algorithm for real-time embedded systems

This work presents a new modular and lowcomplexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform realtime delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in run time to a wide range of modes and sampling rates, from a Ultra-low power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete High-accuracy delineation mode in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography (CSE) committee in the High-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultra-low-power 8-MHz TI MSP430 series microcontroller ranges from 0.2 to 8.5% according to the mode used

(Issue 2, April) Combating COVID-19@IITH

COVID-19 outbreak has drastically changed our routine, but it has also given us new ways to route our life. Alike academic, research @IITH has also continued to tackle ongoing circumstances. Almost all the engineering departments have collaborated and proposed solutions to different problems posed by Covid-19 and its implicated Lockdown. Read More..