Development of an embedded system and MATLAB-based GUI for online acquisition and analysis of ECG signal
TL;DR: A low-cost method for online acquisition of ECG signal for storage and processing using a MATLAB-based Graphical User Interface (GUI) to perform online analysis on the ECG data to compute the different time-plane features and display the same on the GUI along with theECG signal plot.
About: This article is published in Measurement.The article was published on 2010-11-01. It has received 73 citations till now. The article focuses on the topics: Personal computer & Serial communication.
Citations
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TL;DR: The literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above.
326 citations
TL;DR: An 8-channel system for capturing bioelectric signals and transmitting them by the ZigBee protocol, which is a small, portable system with optimised power supply so that it can be battery fed and can be easily integrated into a Wireless Sensor Network based on ZigBee technology.
46 citations
10 Dec 2013
TL;DR: A combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals using bi-orthogonal wavelet family is presented.
Abstract: Noisy ECG signals contain variations in the amplitudes or in the time intervals which represents the abnormalities associated with the heart; thereby making visual diagnosis difficult for cardiovascular diseases. Hence, to facilitate proper analysis of ECG; this paper presents a combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals. The proposed algorithm involves sub-band decomposition of ECG signal using bi-orthogonal wavelet family. The wavelet detail coefficients containing the noisy components are then processed by morphological operators using linear structuring elements. The morphological filter processes only the corrupted bands without affecting the signal parameters. Simulation results of the proposed algorithm show noteworthy suppression of noise in terms of higher signal-to-noise ratio preserving the ST segment and R wave of ECG.
43 citations
Cites background from "Development of an embedded system a..."
...ECG signals are plagued by impulse noise, due to muscle activities; it appears as false positive and negative voltage fluctuations (in the form of spikes of short duration) in the ECG signal [1-2]....
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01 Jan 2013
TL;DR: Experimental results show that the proposed morphological filtering algorithm yields effective pre-processing of ECG signals, thereby eliminating the discussed artifacts.
Abstract: Pre-Processing of Electrocardiographic (ECG) signals involves the baseline wander elimination and impulse noise filtering to facilitate automated analysis. In this paper a new morphological filtering algorithm using combinations of flat (two dimensional) structuring elements is proposed for pre-processing of ECG signals. Usage of two dimensional structuring elements, (over single dimension) aids in controlling effective inhibition of noise, leading to reconstruction with minimal signal distortion. Signal to noise ratio (SNR) and Root Mean Squared Error (RMSE) are used as quantitative evaluation measures for optimizing the selection of size of the structuring elements. Experimental results show that the proposed algorithm yields effective pre-processing of ECG signals, thereby eliminating the discussed artifacts.
28 citations
TL;DR: A MATLAB-based tool and algorithm is presented that converts a printed or scanned format of the ECG into a digitized ECG signal that makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease.
Abstract: Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/−0.021 (p-value < 0.001), QRS interval 1+/− SD (p-value < 0.001), QT interval 0.981 +/− 0.023 p-value < 0.001, and RR interval 1 +/− 0.001 p-value < 0.001. Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease.
28 citations
References
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01 Nov 1996
TL;DR: In this paper, some old and new circuit techniques are described for the compensation of the amplifier's most important nonideal effects including the noise (mainly thermal and 1/f noise), the input-referred dc offset voltage as well as the finite gain.
Abstract: In linear IC's fabricated in a low-voltage CMOS technology, the reduction of the dynamic range due to the dc offset and low frequency noise of the amplifiers becomes increasingly significant. Also, the achievable amplifier gain is often quite low in such a technology, since cascoding may not be a practical circuit option due to the resulting reduction of the output signal swing. In this paper, some old and some new circuit techniques are described for the compensation of the amplifier's most important nonideal effects including the noise (mainly thermal and 1/f noise), the input-referred dc offset voltage as well as the finite gain resulting in a nonideal virtual ground at the input.
1,889 citations
TL;DR: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats and results are an improvement on previously reported results for automated heartbeat classification systems.
Abstract: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.
1,449 citations
TL;DR: The authors provide an overview of these recent developments as well as of formerly proposed algorithms for QRS detection, which reflects the electrical activity within the heart during the ventricular contraction.
Abstract: The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.
1,307 citations
"Development of an embedded system a..." refers methods in this paper
...Simple QRS detection algorithms are based on one of the methods like derivative, filter-banks, wavelets, mathematical morphology and correlation [23,24]....
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TL;DR: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types.
Abstract: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >
1,083 citations
"Development of an embedded system a..." refers methods in this paper
...Simple QRS detection algorithms are based on one of the methods like derivative, filter-banks, wavelets, mathematical morphology and correlation [23,24]....
[...]
TL;DR: A CMOS low-power low-noise monolithic instrumentation amplifier is described and it can produce variable gains of 14/20/26/40 dB, which are set by control software.
Abstract: A CMOS low-power low-noise monolithic instrumentation amplifier (IA) is described. The power drain is reduced by use of current feedback and by use of only single-stage operational transconductance amplifiers in the low-frequency loop. The bandwidth of the IA is designed for medical purposes (0.5-500 Hz) and it can produce variable gains of 14/20/26/40 dB, which are set by control software.
529 citations