A discrete wavelet transform (DWT) based feature extraction technique in the QT segment of digitized electrocardiograph recordings is proposed. At first, the signal is denoised by decomposing it using DWT technique and discarding the coefficients corresponding to the noise components. A multiresolution approach along with an adaptive thresholding is used for the detection of R-peaks. Then Q, S peak, QRS onset and offset points are identified. Finally, the T wave is detected. By detecting the baseline of the ECG data, height of R, Q, S and T wave are calculated. For R-peak detection, proposed algorithm yields sensitivity and positive predictivity of 99.8% and 99.6% respectively with MIT BIH Arrhythmia database, 99.84% and 99.98% respectively with PTB diagnostic ECG database. For time plane features, an average coefficient of variation of 3.21 is obtained over 150 leads tested from PTB data, each with 10,000 samples. © 2011 Elsevier Ltd. All rights reserved.

RAJARSHI GUPTA

Applied Physics

MADHUCHHANDA MITRA

S BANERJEE

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Measurement: Journal of the International Measurement Confederation

Electrocardiography (ECG) is popular non-invasive technique for preliminary level investigation on cardiovascular assessment. Computerized analysis of ECG can significantly contribute towards assisted diagnosis and in early detection of many cardiac diseases. Conventional automated ECG classifiers employing soft computing tools may suffer from the inaccuracies that may result in different clinical feature extraction stages. In this paper, we propose the use of a statistical index, namely, dissimilarity factor (D) for classification of normal and Inferior Myocardial Infarction (IMI) data, without the need of any direct clinical feature extraction. Time aligned ECG beats were obtained through filtering, wavelet decomposition processes, followed by PCA based beat enhancement to generate multivariate time series data. The T wave and QRS segments of IMI datasets from Lead II, III and aVF were extracted and compared with corresponding segments of healthy patients using Physionet ptbdb data. With 35 IMI datasets, the average composite dissimilarity factor Dc between normal data was found to be 0.39, and the same between normal and abnormal data were found to be 0.65. This paper shows the promise of descriptive statistical tools as an alternative for medical signal analysis. © 2016 IEEE.

2016 IEEE 1st International Conference on Control, Measurement and Instrumentation, CMI 2016

Dissimilarity factor based classification of inferior myocardial infarction ECG

Electrocardiogram (ECG) is an important tool for investigation of cardiac functions. ECG synthesis or modeling can be useful for biomedical applications involving data compressions, signal analysis and testing of medical systems. In this paper, we present a morphological modeling method of single lead ECG by two different approaches, viz., Fourier and Gaussian models. Single lead ECG data was preprocessed to remove unwanted noise and segmented in three zones, P-R, Q-R-S and S-T. The individual segments were then modeled to extract model coefficients. The residual of each segment, computed as difference between original and reconstructed samples were also modeled using Fourier model. The algorithms were validated with lead V2 from normal and Anterior Myocardial Infarction (AMI) data from Physionet. With 25 AMI datasets, the average PRDN and SNR were found to be 9.33 and 20.71 respectively with Gaussian model and 5.43 and 22.16 respectively with Fourier model. The Fourier model showed better reconstruction performance, but less memory efficient compared to the Gaussian model. © 2015 IEEE.

Proceedings of 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2015

Electrocardiogram synthesis using Gaussian and fourier models

The paper proposes an algorithm for delineation of ECG data based on multiresolution Discrete Wavelet Transform (DWT) and relative magnitude and slope comparison on selected time windows. The data is initially denoised using linear and nonlinear wavelet based filtering techniques to eliminate both low and high frequency ECG artifacts and also reduce the wide-band noises. The delineation algorithm involves combining specific detailed sub-bands of the denoised data and applying proper thresholds and time windows to identify the wave boundaries (QRS, P and T waves). The characteristic points of each wave (peaks, onset and offset) are then detected using relative magnitude and slope comparison within each wave. The algorithm yields sufficiently high detection sensitivity of 99.85% for QRS regions and also efficiently identifies the P and T wave features, as tested with the MIT-BIH arrhythmia database. © 2016 IEEE.

2016 2nd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2016

ECG delineation using multiresolution DWT and relative magnitude and slope comparison

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression. © 2016, Springer Science+Business Media New York.

Journal of Medical Systems

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis

Automatic classification of cardiac patterns has become a challenging problem as the morphological and temporal characteristics of the ECG signal shows significant variations for different subjects. Most of the classification methods use explicit time-plane features information like presence of abnormal Q wave, QS complexes, ST segment, R height, QT segment measurement etc. Also ECG signals gets corrupted by various forms of noises. Before any feature extraction technique ECG requires to be preprocessed for removal of artifacts and other high frequency noises. This paper presents an ECG based feature extraction and classification technique which does not require conventionally used time plane features also the features in use are extracted from noisy data. The developed method also extracts parameters which have sufficient distinguishing characteristic to classify normal and abnormal cardiac patterns. The proposed algorithm analyses ECG data through the scope of cross-wavelet transform (XWT) and explores the resulting spectral differences. R peaks are detected for beat segmentation and extraction of any other explicit time plane features is not required. The cross-correlation between two time domain signals gives the measure of similarity between two waveforms. The application of the Continuous Wavelet Transform to two time series and the cross examination of the two decomposition reveals localized similarities in time and frequency. Application of Cross Wavelet Transform to a pair of signals yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). A heuristically determined mathematical formula extracts parameter(s) from the wavelet cross spectrum and coherence. Empirical tests establish that the two parameter(s) are relevant for classification of normal and abnormal Cardiac patterns. Advantage of this method is: i) It efficiently works in noisy environment ii). Explicit time plane feature extraction is not required and eliminates the use of rule mining procedure thus reducing the computational complexity of the classifier. © 2014 IEEE.

Journal	Data powered by TypesetMeasurement: Journal of the International Measurement Confederation
Publisher	Data powered by TypesetELSEVIER SCI LTD
ISSN	0263-2241
Open Access	No