Madhuchhanda Mitra

Empirical mode decomposition based ECG enhancement and QRS detection

In this paper an Empirical Mode Decomposition (EMD) based ECG signal enhancement and QRS detection algorithm is proposed. Being a non-invasive measurement, ECG is prone to various high and low frequency noises causing baseline wander and power line interference, which act as a source of error in QRS and other feature extraction. EMD is a fully adaptive signal decomposition technique that generates Intrinsic Mode Functions (IMF) as decomposition output. Here, first baseline wander is corrected by selective reconstruction based slope minimization technique from IMFs and then high frequency noise is removed by eliminating a noisy set of lower order IMFs with a statistical peak correction as high frequency noise elimination is accompanied by peak deformation of sharp characteristic waves. Then a set of IMFs are selected that represents QRS region and a nonlinear transformation is done for QRS enhancement. This improves detection accuracy, which is represented in the result section. Thus in this method a single fold processing of each signal is required unlike other conventional techniques.

A Rough-Set-Based Inference Engine for ECG Classification

In this paper, a rule-based rough-set decision system for the development of a disease inference engine is described. For this purpose, an offline-data-acquisition system of paper electrocardiogram (ECG) records is developed using image-processing techniques. The ECG signals may be corrupted with six types of noise. Therefore, at first, the extracted signals are fed for noise removal. A QRS detector is also developed for the detection of R-R interval of ECG waves. After the detection of this R-R interval, the P and T waves are detected based on a syntactic approach. The isoelectric-level detection and base-line correction are also implemented for accurate computation of different attributes of P, QRS, and T waves. A knowledge base is developed from different medical books and feedbacks of reputed cardiologists regarding ECG interpretation and essential time-domain features of the ECG signal. Finally, a rule-based rough-set decision system is generated for the development of an inference engine for disease identification from these time-domain features

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.

A lossless ECG data compression technique using ASCII character encoding

A software based lossless ECG compression algorithm is developed here. The algorithm is written in the C-platform. The algorithm has applied to various ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB). Here, a difference array has been generated from the corresponding input ECG data and this is multiplied by a large number to convert the number of arrays into integers. Then those integers are grouped in both forward and reverse direction, out of which few are treated differently. Grouping has been done in such a way that every grouped number resides under valid ASCII value. Then all the grouped numbers along with sign bit and other necessary information are converted into their corresponding ASCII characters. The reconstruction algorithm has also been developed in using the reversed logic and it has been observed that data is reconstructed with almost negligible difference as compared with the original (PRD 0.023%).

ECG signal compression using ASCII character encoding and transmission via SMS

Abstract Software based efficient and reliable ECG data compression and transmission scheme is proposed here. The algorithm has been applied to various ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB). First of all, R-peaks are detected by differentiation and squaring technique and QRS regions are located. To achieve a strict lossless compression in the QRS regions and a tolerable lossy compression in rest of the signal, two different compression algorithms have used. The whole compression scheme is such that the compressed file contains only ASCII characters. These characters are transmitted using internet based Short Message Service (SMS) and at the receiving end, original ECG signal is brought back using just the reverse logic of compression. It is observed that the proposed algorithm can reduce the file size significantly (compression ratio: 22.47) preserving ECG signal morphology.

ECG Acquisition and Automated Remote Processing

ECG Acquisition and Automated Remote Processing - Libros de Medicina - Electrocardiografia - 99,99

ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform

In this paper, a novel methodology, based on discrete wavelet transform (DWT) is developed for extraction of characteristic features from twelve - lead Electrocardiogram recordings. The first step of this method is to denoise the signal using DWT technique. A multiresolution approach along with thresholding is used for the detection of R - Peaks in each cardiac beats. Followed, by this other fiducial points (Q and S) are detected and QRS onset and offset points are identified. Baseline is also detected and heights of R, Q, S waves are calculated. This, algorithm was validated using PTB diagnostic database giving a sensitivity of 99.6% and MITDB Arrhythmia, giving a sensitivity of 99.8%. The QRS vectors are calculated for normal and patients with Anteroseptal MI and a comparative study is presented. Accordingly, it has been found that classification of normal and AS MI is possible by computing the QRS vector. And a simple classification rule is established for this purpose.

An FPGA implementation of real-time QRS detection

This paper illustrates a simple algorithm for real time QRS detection from ECG data. The algorithm is implemented on Xilinx field programmable gate array using very small number of memory cells. Single lead Synthetic ECG using ptb-db database (from Physionet) is generated from a personal computer using the parallel port (LPT1) at 1 ms sampling interval and delivered to the FPGA (Field Programmable Gate Array) board. At first, from the first 1500 samples, the QRS detection algorithm calculates some characteristic amplitude and slope based signatures which are used to form a rule base. These rules are used for detecting the next incoming QRS regions accurately. The index points of R-peaks are determined and shown in the LEDs using switch-based commands.

An ECG data compression method via R-Peak detection and ASCII Character Encoding

Efficient and reliable electrocardiogram (ECG) compression system can increase the processing speed of real-time ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In the present paper, a software based effective ECG data compression algorithm is proposed. The whole algorithm is written in C- platform. The algorithm is tested on various ECG data of all the 12 leads taken from PTB Diagnostic ECG Database (PTB-DB). In this compression methodology, all the R-Peaks are detected at first by differentiation technique and QRS regions are located. To achieve a strict lossless compression in QRS regions and a tolerable lossy compression in rest of the signal, two different compression algorithms have developed. In lossless compression method a difference array has been generated from the corresponding input ECG “Voltage” values and then those are multiplied by a considerably large integer number to convert them into integer. In the next step, theses integer numbers are grouped in both forward and reverse direction maintaining some logical criteria. Then all the grouped numbers along with sign bit and other necessary information (position of critical numbers, forward/reverse grouping etc.) are converted into their corresponding ASCII characters. Whereas in lossy area, first of all, the sampling frequency of the original ECG signal is reduced to one half and then, only the “Voltage” values are gathered from the corresponding input ECG data and those are amplified and grouped only in forward direction. Then all the grouped numbers along with sign bit and other necessary information are converted into their corresponding ASCII characters. It is observed that this proposed algorithm can reduce the file size significantly. The data reconstruction algorithm has also been developed using the reversed logic and it is seen that data is reconstructed preserving the significant ECG signal morphology.

QRS Complex detection using Empirical Mode Decomposition based windowing technique

In this work an Empirical Mode Decomposition based QRS complex detection algorithm is proposed. Other decomposition techniques use some predetermined basis function for transformation and hence may not be applicable for all kind of signals. Being a fully data driven adaptive technique, the present method depends on selection of proper and optimum set of IMFs to generate an intermediate signal. Some simple mathematical operations are performed on that signal to highlight the R peak. Then the Q and S points are detected by windowing technique. Only second and third IMFs are required for QRS complex detection. The proposed method is tested with PTB diagnostic database and MIT-BIH Arrhythmia database. The R peak detection success rate is 98.67%. Sensitivity and specificity of QRS complex detection is 98.88% and 99.04% respectively.