FPGA-based denoising and beat detection of the ECG signal
01 May 2015-pp 1-5
TL;DR: The hardware system has achieved an overall accuracy of 98% in the beat detection phase, while providing the detected beats and the classification of irregular heart beat rates in real time.
Abstract: In this work, an efficient digital system is designed using hardware to filter the Electrocardiogram (ECG) signal and to detect the QRS complex (beats). The system implementation has been done by using a Field Programmable Gate Array (FPGA). In the first phase of the hardware system implementation, Finite Impulse Response (FIR) filters are designed for preprocessing and denoising the ECG signal. The filtered signal is then used as the input of the second phase of the hardware implementation to detect and classify the ECG beats. The entire system has been implemented on ALTERA DE II FPGA by designing synthesizable finite state machines. Quartus II tool has been used to simulate and test the system. The designed system has been tested on ECG waves from the MIT-BIH Arrhythmia database by windowing the signal and applying adaptive signal and noise thresholds in each window of processing. The hardware system has achieved an overall accuracy of 98% in the beat detection phase, while providing the detected beats and the classification of irregular heart beat rates in real time.
Citations
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31 Jul 2018
TL;DR: Li et al. as mentioned in this paper proposed a coupled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance, the proposed architecture is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch photo recognition methods.
Abstract: In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential information about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a coupled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Convolutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common embedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with different facial attributes, while the verification task reduces the intra-personal variations by pulling together all the features that are related to one person. The learned discriminative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state-of-the-art models in sketch-photo recognition algorithms.
19 citations
TL;DR: The research ideas and conceptualization of a framework to determine the factors that influence the effectiveness of a personal healthcare response monitoring system from a systems engineering perspective and a SD model for smartphone-based healthcare monitoring is introduced are presented.
Abstract: With the rapid growth of technology and the advances in smart healthcare, patients nowadays struggle with finding suitable smart healthcare interfaces that offer the most effective and manageable personalized care. System dynamics (SD) investigates the behavior of various factors of a system and thus, can offer plausible solutions in this regard. This article presents the research ideas and conceptualization of a framework to determine the factors that influence the effectiveness of a personal healthcare response monitoring system from a systems engineering perspective. A SD model for smartphone-based healthcare monitoring is introduced. Specifically, smartphone-based heart monitoring using electrocardiogram (ECG) is studied in this article from a SD point of view. The model includes factors such as patient wellbeing and care, cost, convenience, user friendliness, in addition to other embedded ECG system design and performance metrics (e.g., accuracy, real-time response, etc.). The model has been rigorously tested and the simulation results reflect the dynamics of the model and the effectiveness of smartphone-based ECG monitoring in various scenarios. The proposed framework has the potential to facilitate visualizing the effectiveness of smartphone-based healthcare monitoring systems for users.
12 citations
01 Oct 2016
TL;DR: The hardware implementations can be further viewed as an industrial ECG monitoring framework where system dynamics modeling can be applied to minimize the risks associated with the framework using any of the three implementations.
Abstract: This work represents the design and verification of three different finite impulse response (FIR) filter implementations for removing the noise of electrocardiogram (ECG) signals. Generally, ECG signals may be contaminated with different noise sources such as body movement and respiration, electromyography (EMG) interference, power line interference and the baseline wander noise. The FIR filter coefficients are calculated to attenuate the 60 Hz frequencies. The advanced filter design tool available with MATLAB is used to first determine the FIR filter coefficients. These coefficients are then used in three different FIR filter implementations: regular implementation, pipelined implementation and pipelined multiply-accumulate (MAC) implementation. The three implementations are designed using VHDL and the Quartus II design toolset. A test bench is also designed to verify the operation of each filter implementation, and the Modelsim simulator available with Quartus is used to run the tests. The synthesized reports for the three different implementations show the resource utilization and the maximum operating frequency. As a result, the regular (direct) design produces the simplest design but consumes more resources and operates at lower frequencies. The pipelined architecture consumes more resources but it enhances the operating frequency. The pipelined MAC implementation requires the least resources and operates at extremely higher performance, however, the main drawback is its complexity. The hardware implementations can be further viewed as an industrial ECG monitoring framework where system dynamics modeling can be applied to minimize the risks associated with the framework using any of the three implementations.
9 citations
Additional excerpts
...On the other hand, some algorithms have been introduced that exploit real-time analysis of bio-signals such as ECG and Electroencephalogram (EEG) based on high performance, field programmable gate array (FPGA)-based embedded designs and hardware implementations [12,13]....
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TL;DR: An all-in-one waveform generator, lock-in amplifier and Proportional-Integral-Differential (PID) controller, embedded in a single Field Programmable Gate Array (FPGA), advanced with a novel automatic relocking mechanism.
Abstract: We report an all-in-one waveform generator, lock-in amplifier and Proportional-Integral-Differential (PID) controller, embedded in a single Field Programmable Gate Array (FPGA). The PID controller is advanced with a novel automatic relocking mechanism, which is capable of self-finding and relocking at the desired setpoint upon unlocking of the PID due to disturbances. The instrument is designed in such a way that these devices can either be used individually or simultaneously. Digital implementation via software processing of the associated modules is used and the firmware is embedded in an FPGA IC, which makes it compact and reconfigurable. The instrument consists of a hardware for establishing external linkage and a Python based computer-controlled interface to control it from a remote PC as well as for acquiring and plotting relevant data. A steep roll-off of the filters (6 dB/octave to 24 dB/octave), low noise density (30 nV/√Hz @ 100 kHz) in the lock-in amplifier and a PID bandwidth of 100 kHz makes it useful for wide range of applications. Versatility of the instrument is demonstrated in three different experiments, (i) Auger electron spectroscopy, (ii) low coherence optical tomography and (iii) laser frequency stabilization followed by self identification of the setpoint upon unlocking and automatic relocking to that.
8 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
References
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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
"FPGA-based denoising and beat detec..." refers background or methods in this paper
...The moving window integration is obtained from the equation below [12]: 1 1 2...
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...The authors in [14] proposed modification to the existing Pan and Tompkins algorithm where they reduced the data processing by introducing an algorithm which has only one set of adaptive threshold computations instead of two in Pan and Tompkins algorithm [12]....
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...The authors in [12] are considered to be the pioneers who worked on the development of QRS detection algorithm....
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...As a result, the number of samples (N) in the moving window integration is determined empirically [12]....
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TL;DR: An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points and the relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated.
Abstract: An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious base line drift and noise. >
1,637 citations
"FPGA-based denoising and beat detec..." refers methods in this paper
...Some of them are derived from Artificial Neural Network [3, 4, 5, 6], Wavelet Transforms [7, 8, 9], Genetic Algorithms [10], and Filter Banks [11]....
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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
"FPGA-based denoising and beat detec..." refers background in this paper
...Due to its features, it becomes the basis for the determination and classification of the heart rate (arrhythmia) [2]....
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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.
1,137 citations
TL;DR: A multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG) which incorporates a filter bank which decomposes the ECG into subbands with uniform frequency bandwidths and inherently lends itself to a computationally efficient structure.
Abstract: The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.
767 citations
"FPGA-based denoising and beat detec..." refers methods in this paper
...Some of them are derived from Artificial Neural Network [3, 4, 5, 6], Wavelet Transforms [7, 8, 9], Genetic Algorithms [10], and Filter Banks [11]....
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