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Journal ArticleDOI

Real-time detection of electrocardiogram wave features using template matching and implementation in FPGA

18 Mar 2015-International Journal of Biomedical Engineering and Technology (Inderscience Publishers)-Vol. 17, Iss: 3, pp 290-313

TL;DR: An algorithm for real–time detection of wave peaks and their features from single lead ECG data, which was implemented on Xilinx Spartan III Field Programmable Gate Array (FPGA) and clinically validated by medical expert.

AbstractElectrocardiogram (ECG) can provide valuable clinical information on cardiac functions. This paper illustrates an algorithm for real–time detection of wave peaks and their features from single lead ECG data. At first, the ECG data was filtered for power line interference and high frequency noise. Then, a set of slope and polarity–based rule bases were generated from the first 6000 samples, which define templates of R–peak, P–and T–wave detection from the following beats. The algorithm was implemented on Xilinx Spartan III Field Programmable Gate Array (FPGA). For testing of the algorithm, ECG data was quantised at 8–bit resolution and delivered to the FPGA using synchronous transfer mechanism using parallel port of computer. Xilinx implementation results provided 97.58%, 98.4% and 97.78% detection sensitivity for P–, R– and T–waves, respectively. Different wave features (height, polarity and duration) were detected with an average error rate of 9.3%. The detected wave signatures were clinically validated by medical expert.

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Citations
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Journal ArticleDOI
TL;DR: This study presents a new field programmable gate array (FPGA)-based hardware implementation of the QRS complex detection, mainly based on the Pan and Tompkins algorithm, but applying a new, simple, and efficient technique in the detection stage.
Abstract: The continuous monitoring of cardiac patients requires an ambulatory system that can automatically detect heart diseases. This study presents a new field programmable gate array (FPGA)-based hardware implementation of the QRS complex detection. The proposed detection system is mainly based on the Pan and Tompkins algorithm, but applying a new, simple, and efficient technique in the detection stage. The new method is based on the centred derivative and the intermediate value theorem, to locate the QRS peaks. The proposed architecture has been implemented on FPGA using the Xilinx System Generator for digital signal processor and the Nexys-4 FPGA evaluation kit. To evaluate the effectiveness of the proposed system, a comparative study has been performed between the resulting performances and those obtained with existing QRS detection systems, in terms of reliability, execution time, and FPGA resources estimation. The proposed architecture has been validated using the 48 half-hours of records obtained from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) arrhythmia database. It has also been validated in real time via the analogue discovery device.

6 citations

Proceedings ArticleDOI
07 Oct 2020
TL;DR: Segment specific modelling approach for different wave segments of ECG signal provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.
Abstract: Electrocardiogram (ECG) modeling is useful for abnormality detection and data compression. The common research problem in modeling is retaining pathological information using minimum number of model coefficients. In this paper, a new modeling technique for different wave segments of ECG signal, viz., baseline to P-onset, P wave, P-offset to Q, QRS complex, S to T-onset, T wave and T-offset to next baseline is presented. The processing steps included preprocessing, R-peak detection, beat segmentation and waveform partitioning, followed by modeling of individual partitions. For P, QRS and T wave, Gaussian model was adopted and for other segments, Fourier model was adopted to minimize reconstruction error. For testing of the proposed model, normal sinus rhythm (NSR) and myocardial infarction (MI) data records of PTB Diagnostic ECG database (ptbdb) and atrial premature (APC), premature ventricular contraction (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB) data records of MIT-BIH arrhythmia database (mitdb) under PhysioNet were used. The average SNR, and MSE using proposed method for ptbdb NSR was 86.33, and 4.41×10-6, respectively; for AMI 96.18, and 3.70×10-6 respectively; for IMI 80.86, and 1.36×10-6 respectively; for mitdb NSR 90.94 and 3.50×10-6 respectively; for APC 89.42, and 2.34×10-6 respectively; for PVC 93.28 and 3.06×10-6, respectively; for LBBB 93.77 and 2.74×10-6, respectively; for RBBB 92.83 and 3.52×10-6 respectively. Segment specific modelling approach provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.

Cites background from "Real-time detection of electrocardi..."

  • ...The success of an ECG modeling largely depends upon the accuracy of beat delineation, since certain pathological beats pose a great challenge before the delineation [15], [16], [17], [18], [19], [20]....

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Journal ArticleDOI
Abstract: The rapid evolution of error-resilient programs intertwined with their quest for high throughput has motivated the use of Single Instruction, Multiple Data (SIMD) components in Field-Programmable G...
Patent
15 Sep 2020
Abstract: The invention discloses an ECG and inertial sensing data combined fall detection method and system. The method comprises the following steps: equipment comprising ECG and inertial sensing units is used for acquiring fall data and daily action data in the daily activity process of a human body; the collected fall data and daily action data are segmented according to a same time period, inertial sensing data characteristics and ECG data characteristics are extracted respectively, normalization is performed, and normalized sample data characteristics are obtained; the normalized sample data characteristics are subjected to dimension reduction by a principal component analysis algorithm to obtain dimension reduction data characteristics, the dimension reduction data characteristics are classified by a support vector machine algorithm, and a support vector machine classification model is obtained; and newly collected data are input into the support vector machine classification model, and fall detection results are obtained. Changes of the human fall process are reflected from two different dimensions of human physiological activity and posture, the defect of inaccurate data of a singletype is avoided, and accuracy of fall detection is improved.
Journal ArticleDOI
Abstract: The mortality rate due to cardiac abnormalities is enormous, making the development of wearables to monitor functioning of the heart of paramount importance. In this paper, wepresent a resource efficient and low power architecture using Integer Haar Wavelet Transform for the complete delineation of ECG signal. The novelty of the algorithm lies in the use of single scale wavelet coefficients to delineate P-QRS-T features making it computationally simple. The proposed architecture is implemented using Xilinx FPGA ZedBoard Zynq™ − 7000 platform, and utilises only 4.38% of the available resources. It is synthesised using 180 nm CMOS technology consuming 0.88 μ W power, making it area as well as power-efficient for the wearable IoT healthcare devices.

References
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Journal ArticleDOI
TL;DR: This work implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process, which has a sensitivity of 99.69 percent and positive predictivity of 98.77 percent when evaluated with the MIT/BIH arrhythmia database.
Abstract: We have investigated the quantitative effects of a number of common elements of QRS detection rules using the MIT/BIH arrhythmia database. A previously developed linear and nonlinear filtering scheme was used to provide input to the QRS detector decision section. We used the filtering to preprocess the database. This yielded a set of event vectors produced from QRS complexes and noise. After this preprocessing, we tested different decision rules on the event vectors. This step was carried out at processing speeds up to 100 times faster than real time. The role of the decision rule section is to discriminate the QRS events from the noise events. We started by optimizing a simple decision rule. Then we developed a progressively more complex decision process for QRS detection by adding new detection rules. We implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process. The resulting QRS detection algorithm has a sensitivity of 99.69 percent and positive predictivity of 99.77 percent when evaluated with the MIT/BIH arrhythmia database.

1,078 citations


"Real-time detection of electrocardi..." refers methods in this paper

  • ...QRS detection using morphological filtering followed by quadratic spline wavelet transforms and modulus maxima pair recognition is reported by Ieong et al. (2008a). Adaptive Lifting Scheme (ALS), a derivative of wavelet transform, was implemented by Li et al....

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  • ...Shukla and Macchiarulo (2008) implemented FPGA implementation of widely popular PanTompkins algorithm (Hamilton and Tompkins, 1986), using a threshold calculated on last...

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Journal ArticleDOI
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. >

1,034 citations


"Real-time detection of electrocardi..." refers methods in this paper

  • ...Homaeinezhad et al. (2014) extracted morphology-based templates for detection of P- and T-waves in the non-QRS region (or R–R interval)....

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  • ...Some popular approaches used for the purpose of detection of R-peak include derivative based approach (Friesen et al., 1990), artificial neural network based approach (Hu et al....

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Journal Article
TL;DR: The authors used an adaptive multilayer perceptron structure to model the nonlinear background noise so as to enhance the QRS complex, providing more reliable detection of QRS complexes even in a noisy environment.
Abstract: The authors have investigated potential applications of artificial neural networks for electrocardiographic QRS detection and beat classification. For the task of QRS detection, the authors used an adaptive multilayer perceptron structure to model the nonlinear background noise so as to enhance the QRS complex. This provided more reliable detection of QRS complexes even in a noisy environment. For electrocardiographic QRS complex pattern classification, an artificial neural network adaptive multilayer perceptron was used as a pattern classifier to distinguish between normal and abnormal beat patterns, as well as to classify 12 different abnormal beat morphologies. Preliminary results using the MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital, Cambridge, MA) arrhythmia database are encouraging.

269 citations


"Real-time detection of electrocardi..." refers background in this paper

  • ..., 1990), artificial neural network based approach (Hu et al., 1993), transform domain (Wavelet Transform, Hilbert Transform) approach (Di-Virgilio et al....

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Journal ArticleDOI
TL;DR: The design, test methods, and results of an ambulatory QRS detector are presented and the aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device.
Abstract: The design, test methods, and results of an ambulatory QRS detector are presented. The device is intended for the accurate measurement of heart rate variability (HRV) and reliable QRS detection in both ambulatory and clinical use. The aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device. The complete monitor system consists of a host computer and the detector unit. The detector device is constructed of a commonly available digital signal processing (DSP) microprocessor and other components. The QRS detection algorithm uses optimized prefiltering in conjunction with a matched filter and dual edge threshold detection. The purpose of the prefiltering is to attenuate various noise components in order to achieve improved detection reliability. The matched filter further improves signal-to-noise ratio (SNR) and symmetries the QRS complex for the threshold detection, which is essential in order to achieve the desired performance. The decision for detection is made in real-time and no search-back method is employed. The host computer is used to configure the detector unit, which includes the setting of the matched filter impulse response, and in the retrieval and postprocessing of the measurement results. The QRS detection timing accuracy and detection reliability of the detector system was tested with an artificially generated electrocardiogram (EGG) signal corrupted with various noise types and a timing standard deviation of less than 1 ms was achieved with most noise types and levels similar to those encountered in real measurements. A QRS detection error rate (ER) of 0.1 and 2.2% was achieved with records 103 and 105 from the MIT-BIH Arrhythmia database, respectively.

266 citations


"Real-time detection of electrocardi..." refers background in this paper

  • ..., 2000), matched filter based approach (Ruha et al., 1997; Lindecrantz and Lilja, 1988) and many other....

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Proceedings ArticleDOI
24 Sep 2000
TL;DR: A robust new algorithm for QRS defection using the properties of the Hilbert transform is proposed, which allows R waves to be differentiated from large, peaked T and P waves with a high degree of accuracy and minimizes the problems associated with baseline drift, motion artifacts and muscular noise.
Abstract: A robust new algorithm for QRS defection using the properties of the Hilbert transform is proposed. The method allows R waves to be differentiated from large, peaked T and P waves with a high degree of accuracy and minimizes the problems associated with baseline drift, motion artifacts and muscular noise. The performance of the algorithm was tested using the records of the MIT-BIH Arrhythmia Database. Beat by beat comparison was performed according to the recommendation of the American National Standard for ambulatory ECG analyzers (ANSI/AAMI EC38-1998). A QRS detection rate of 99.64%, a sensitivity of 99.81% and a positive prediction of 99.83% was achieved against the MIT-BIH Arrhythmia database. The noise tolerance of the new proposed QRS detector was also tested using standard records from the MIT-BIH Noise Stress Test Database. The sensitivity of the detector remains about 94% even for signal-to-noise ratios (SNR) as low as 6 dB.

178 citations


"Real-time detection of electrocardi..." refers methods in this paper

  • ..., 1993), transform domain (Wavelet Transform, Hilbert Transform) approach (Di-Virgilio et al., 1995; Xu and Liu, 2005; Benitez et al., 2000), matched filter based approach (Ruha et al....

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