FPGA-based system for heart rate monitoring
01 Sep 2019-Iet Circuits Devices & Systems (The Institution of Engineering and Technology)-Vol. 13, Iss: 6, pp 771-782
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.
Citations
More filters
TL;DR: To achieve diagnosing a wide range of cardiac diseases and continuous monitoring, a homecare-oriented ECG diagnosis platform is designed based on a large-scale multilabel deep conventional neural network.
Abstract: The accurate electrocardiogram (ECG) interpretation is important for several potentially life-threatening cardiac diseases. Recently developed deep learning methods show their ability to distinguish some severe heart diseases. However, since deep neural network requires a high cost on memory consumption and computation, implementation scenarios of these interpretation methods are constrained to nonportable devices. Few commercial portable devices only have heartbeat detection ability, and therefore, only a few simple cardiac diseases can be diagnosed. In this article, to achieve diagnosing a wide range of cardiac diseases and continuous monitoring, a homecare-oriented ECG diagnosis platform is designed based on a large-scale multilabel deep conventional neural network. The accuracy of the proposed neural network model is guaranteed by our constructed large-scale ECG dataset, which is comprised of 206 468 standard 12-lead ECG recordings from 89 488 patients, with respect to 26 types of most common heart rhythms and conduction abnormalities. Meanwhile, targeting lightweight homecare or wearable applications, algorithm-hardware co-optimization is conducted to accelerate the model computation on an embedded platform with field-programmable gate array (FPGA) for continuous monitoring. Channel-level pruning and parameters quantization strategy are employed to optimize the network, and a reconfigurable accelerator hardware architecture is designed to accelerate the convolution computation on FPGA. The final quantified model achieved a promising $F_{1}$ score of 0.913% and 86.7% exact match ratio, in which parameters and floating-point operations per second (FLOPs) are significantly penalized compared to the original large-scale model. Real-time analysis is performed. Specifically, the average processing time for each ECG record is 2.895 s, and it can be applied to homecare or portable ECG diagnosis devices for continuous monitoring.
15 citations
TL;DR: In this article, eight arrhythmic ECG signals from vital signals were designed mathematically, and then modelled on FPGA by VHDL and Xilinx-Vivado software.
Abstract: In this study, eight arrhythmic ECG signals from vital signals [sinus tachycardia, supraventricular tachycardia, premature ventricular complex (PVC), atrial fibrillation, AV block: 3rd degree, ventricular fibrillation, sinus bradycardia, first-degree AV block] were designed mathematically, and then modelled on FPGA by VHDL and Xilinx-Vivado software. The mathematical extrapolation of the signals was created in accordance with the literature and after examining the time and amplitude values of many ECG signals from the Physiobank ATM section of the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. These signals were synthesized for the Zynq-7000 XC7Z020 FPGA chip for using in biomedical calibration applications and ECG simulators. The ECG signals were modelled with a 14-bit AD9767 DAC module that worked in coherence with this development board, and observed in real-time by 4 channel oscilloscope. Matlab-based ECG signals were taken as reference and compared with the results obtained from the FPGA-based ECG signals design. The FPGA chip resource consumption values obtained after the place–route process, the test results obtained from the design, the MSE (mean squared error) values of the designed signals, the operating frequencies of the system and each signal have been presented. The maximum operating speed of this system is 651.827 MHz. In this study, it has been shown that FPGA-based ECG signal generation system can be implemented on FPGA chips, and the designed system can be safely used in ECG simulators.
7 citations
09 Feb 2021
TL;DR: In this article, a simple and efficient single channel of electro myogram signal (EMG) acquisition circuit was designed to create two databases that contains EMG signals matrices of both flexion and extension of the arm.
Abstract: In order to develop a prototype of upper limb prosthetic, we present in this paper our contribution to the design of an intelligent classification system for the arm's flexion and extension. The first step, we designed a simple and efficient single channel of electro myogram signal (EMG) acquisition circuit in order to create two databases that contains EMG signals matrices of both flexion and extension of the arm. Our work proves that only one statistical feature, the energy of detail coefficients for the first four decomposition levels, is sufficient to represent these databases. We applied the principal component analysis PCA to reduce the data space and keep the most relevant ones. In order to detect flexion or extension movement, classification by Support Vector Machines (SVM) has made possible for us to achieve recognition rate of 100% using a wise choice of discret wavelet transform (DWT).
7 citations
03 Nov 2020
TL;DR: The study aims to establish an FPGA design model for epileptic seizures with discrete wavelet decomposition (DWT) and principal component analysis (PCA) to determine the optimum parameters of support vector machine (SVMs) for the EEG classification data.
Abstract: The study aims to establish an FPGA design model for epileptic seizures with discrete wavelet decomposition (DWT) and principal component analysis (PCA) to determine the optimum parameters of support vector machine (SVMs) for the EEG classification data. The FPGA Hardware implementation is described in this paper. Firstly, an optimized software-based medical diagnostic approach has been developed to determine the EEG class using only the variance calculated for each DWT level. This features extracted optimization leads to reduce the FPGA prototype size and to save energy consumption. Secondly, the proposed method has been designed and implemented on the Nexys 4 Artix 7 board using the Xilinx System Generator (XSG) for DSP. The performance evaluation of the proposed system has been made through two comparative studies, the first one, between the floating-point Matlab results and the fixed-point XSG results. The classification performances obtained from the proposed FPGA fixed-point implementation were compared to those obtained from the MATLAB floating-point. The second comparison was performed between the resulting performances and those obtained with the existing work in literature.
6 citations
Cites background from "FPGA-based system for heart rate mo..."
...The FPGAs are advantageous compared to other programmable chips by their low cost, their reconfigurability, and their highly parallel and interconnected architecture [1], [2]....
[...]
References
More filters
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...
11,407 citations
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
6,686 citations
TL;DR: It is proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents.
Abstract: The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images. >
4,064 citations
TL;DR: The history of the database, its contents, what is learned about database design and construction, and some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database are reviewed.
Abstract: The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.
3,111 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