QRS Complex Detection Research Articles

A Modified Algorithm for QRS Complex Detection for FPGA Implementation

This work is part of the Psypocket project which aims to conceive an embedded system able to recognize the stress state of an individual based on physiological and behavioural modifications. In this paper, one of the physiological data, the electrocardiographic (ECG) signal, is focused on. The QRS complex is the most significant segment in this signal. By detecting its position, the heart rate can be learnt. In this paper, a field-programmable gate array (FPGA) architecture for QRS complex detection is proposed. The detection algorithm adopts the integer Haar transform for ECG signal filtering and a maximum finding strategy to detect the location of R peak of the QRS complex. The ECG data are originally recorded by double-precision decimal with the sampling frequencies of 2000 Hz. For the FPGA implementation, they should be converted to integers with rounding operation. To find the best multiplying factor for rounding, the comparison is performed in MATLAB. Besides, to reduce the computation load in FPGA, the feasibility of the reduction in the sampling frequency is tested in MATLAB. The FPGA Cyclone EP3C5F256C6 is used as the target chip, and all the components of the system are implemented in VHSIC hardware description language. The testing results show that the proposed FPGA architecture achieves a high detection accuracy (98.41%) and a good design efficiency in terms of silicon consumption and operation speed. The proposed architecture will be adopted as a core unit to make a FPGA system for stress recognition.

Algorithm for detection the QRS complexes based on support vector machine

The efficiency of computer ECG analysis depends on the accurate detection of QRS-complexes. This paper presents an algorithm for QRS complex detection based of support vector machine (SVM). The proposed algorithm is evaluated on annotated standard databases such as MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity Se = 98.32% and specificity Sp = 95.46% for MIT-BIH Arrhythmia database. This algorithm can be used as the basis for the software to diagnose electrical activity of the heart.

The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices**Project supported by the National Natural Science Foundation of China (Nos. 61574040, 61234002, 61525401).

Electrocardiogram (ECG) can be used as a valid way for diagnosing heart disease. To fulfill ECG processing in wearable devices by reducing computation complexity and hardware cost, two kinds of adaptive filters are designed to perform QRS complex detection and motion artifacts removal, respectively. The proposed design achieves a sensitivity of 99.49% and a positive predictivity of 99.72%, tested under the MIT-BIH ECG database. The proposed design is synthesized under the SMIC 65-nm CMOS technology and verified by post-synthesis simulation. Experimental results show that the power consumption and area cost of this design are of 160 μW and 1.09 × 10 5 μm2, respectively.

QRS complex detection based on primitive

The detection of the QRS complex is one of the most important issues in electrocardiogram (ECG) signal analysis. Although research on the detection of the R-peak has demonstrated a high detection rate through a diverse number of studies, research on the detection of the onset and offset boundaries of the QRS complex has proven to be difficult, as the locations of these endpoints are often unclear, and the detection results are difficult to interpret. Hence, detection research through improved algorithms continues to be an important component of the ECG signal analysis, especially given the importance of the QRS complexs role in the diagnosis of arrhythmia through measuring the length of the onset and offset of the QRS complex. This paper proposes an improved algorithm that focuses on the primitive of the QRS complex for detecting the onset and offset of the complex based on the morphological characteristics of the QRS complex. The proposed algorithm was tested through experiments based on QT database (QT-DB) data provided by Physionet, and the outcome revealed not only the reliable detection of the QRS complex boundaries but also results that were superior to the location information recorded in the QT-DB.

A novel approach to phase space reconstruction of single lead ECG for QRS complex detection

Abstract Two-dimensional reconstructed phase space (RPS) of single lead Electrocardiogram (ECG) is usually implemented by plotting the ECG amplitude x(t + τ) versus x(t) into the two-dimensional coordinate system, where the value of time delay τ determined the morphology of the reconstructed trajectory. However, the value of τ is very difficult to select because different theories derived different τ. In this paper, a novel approach to phase space reconstruction of single lead ECG without using τ is proposed. The first two coordinates (x, y) from (x, y, t) were projected into the x-y coordinate system, where x is the amplitude of the ECG and y is the first order difference of x. Besides, time t is corresponding to the sampling time moment. As QRS complex is usually the most striking waveform that dominant with the highest amplitude or the highest slope, the largest semi-circle in the RPS is usually derived from QRS complex. The location of QRS complex in the original ECG is determined by the time coordinate t that corresponds to the largest semi-circle in the x-y coordinate system. The algorithm was developed at the MIT-BIH Arrhythmia Database (109494 beats within 24 h in total) and was tested on the Long-term ST Database (8897780 beats within 1991.8 h in total). The accuracy (ACC), the sensitivity (SEN) and the positive predictivity value (PPV) for the MIT-BIH Arrhythmia Database were 99.81%, 99.87% and 99.93%, respectively; while the corresponding values for the Long-term ST Database were 99.87%, 99.96% and 99.91%, respectively. Meanwhile, the consuming time was only 6.73 ms for processing 6 s’ ECG data. Furthermore, the anti-noise ability of the proposed method was tested on the MIT-BIH Noise Stress Test Database (4265 beats in total at each noise level for one lead ECG). Both ACC and PPV were higher than 85% and the SEN was higher than 99% even when the signal-to-noise ratio (SNR) was as low as 0 dB. In conclusion, the proposed algorithm achieves better performance on QRS complex detection when in comparison with the state-of-the-art methods, and it is suited for the detection of QRS complex in the ECG associated with poor signal quality and severe arrhythmia.

On the detection of Cardiac Arrhythmia with Principal Component Analysis

The Electrocardiogram (ECG) signal is used to record the electrical activity of heart. The subtle variations in ECG attributes are used by cardiologists for diagnosis of heart anomalies. But, for prognosis of cardiac ailments feature extraction from electrocardiographic signal becomes extremely difficult due to presence of noise. With the aim of noise reduction, a hybrid technique involving Extended Kalman filter along with Discrete Wavelet transform for effectively improving signal quality is focused as a powerful tool. The performance of denoising algorithm is evaluated in terms of signal to noise ratio and mean square error. On denoised signal, a quick, simple and effectual approach based on Principal Component Analysis is proposed for R-peak and QRS complex detection. The beat detector performance is validated with MIT-BIH arrhythmia database, yielding a sensitivity of 99.93%, positive predictivity of 99.98% and a 0.079% detection error rate, being a positive outcome in comparison with recent researches. Later, different types of arrhythmias are detected on the basis of heart rate and morphological characteristics of ECG waveform.

Denoising and Beat Detection of ECG Signal by Using FPGA

This paper introduces an efficient digital system design using hardware concepts to filter the Electrocardiogram (ECG) signal and to detect QRS complex (beats). The system implementation has been done using a Field Programmable Gate Array (FPGA) in two phases. In the first phase, Finite Impule 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 to detect and classify the ECG beats. The entire system has been implemented on ALTERA DE II FPGA by desinging synthesizable finite state machines. The design has been tested on ECG waves from the MIT-BIH Arrhythmia database by windowing the signal and applying adaptive signal and noise theresholds 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 heat-beat rates in real-time. The synthesized hardware of the ECG denoising and beat detection system yields reasonable hardware resources, making the system attractive to be eventually fabricated as a stand alone hardware system or integrated/embedded within a portable electronic device for monitoring patients’ conditions on a daily basis conveinently.

A Novel Short-Term Event Extraction Algorithm for Biomedical Signals.

In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications, namely ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection. Rel-En successfully identified the events in these settings. Compared to the state-of-the-art, better or comparable results were obtained on QRS-complex and K-complex detection. For iPPG peak detection, the proposed method was used as a preprocessing step to a fixed threshold algorithm that lead to a significant improvement in overall results. While easily defined and computed, Rel-En robustly extracted short-term events of interest. The proposed algorithm can be implemented by two filters and its parameters can be selected easily and intuitively. Furthermore, Rel-En algorithm can be used in other biomedical signal processing applications where a need of short-term event extraction is present.

QRS complex detection in ECG signals using locally adaptive weighted total variation denoising

The QRS complex is the most prominent feature in the electrocardiogram (ECG), therefore, its detection is required for delineation of other waves and segments in the ECG and derivation of additional clinically useful information. QRS detection is complicated by factors like varying QRS morphologies, noise, artefacts and interference from tall and pointed P- and T-waves. In this paper, we propose a novel technique for QRS detection by preprocessing the ECG using weighted total variation (WTV) denoising. A local estimate of noise in the signal block under consideration is used to determine the regularization parameter in WTV minimization, which determines the amount of smoothing applied. This makes the denoising locally adaptive. The weights are chosen so as to give preference to preservation of QRS complexes over P- and T-waves while smoothing. Thus, the technique can simultaneously reduce the higher frequency noise as well as the lower frequency interference from P- and T-waves, in spite of the fact that they have overlapping spectra with the QRS complexes. The proposed method is evaluated on the MIT-BIH arrhythmia database and gives improved detection accuracy over established and state-of-the-art techniques. The technique has low computational load, therefore, it can be used for fast offline QRS detection in long duration ECG records, as well as real-time QRS detection in block-by-block processing mode. The average values of sensitivity, positive predictivity and detection error rate are 99.90%, 99.88% and 0.23%, for the offline implementation, respectively, and 99.86%, 99.85% and 0.29%, for the real-time mode, respectively.

Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities

Analysis of electrocardiogram (ECG) signal provides valuable information about the heart conditions of the patient to the clinicians. The wavelet transform is an effective tool for extracting discriminative features in ECG signal classification for automatic diagnosis of cardiac arrhythmia. In this paper we proposed an improved algorithm to detect QRS complex features based on the multiresolution wavelet transform to classify four types of ECG beats. Extracted features are used for classifying cardiac abnormalities: normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), Paced beats (P) using neural network (NN) and support vector machines (SVM) classifier. The performance of the method is evaluated in terms of sensitivity, specificity, accuracy for 48 recorded ECG signals obtained from the MIT–BIH arrhythmia database. The proposed process achieved high detection performance with less error rate of 0.42% in detecting QRS complex. The classifier confirmed its superiority with an average accuracy of 96.67% and 98.39% in NN and SVM respectively. The classification accuracy of SVM approach proves superior for the proposed method to that of the NN classifier with extracted parameter in detecting ECG arrhythmia beats.

A new method for QRS detection in ECG signals using QRS-preserving filtering techniques.

Detection of QRS complexes in ECG signals is required for various purposes such as determination of heart rate, feature extraction and classification. The problem of automatic QRS detection in ECG signals is complicated by the presence of noise spectrally overlapping with the QRS frequency range. As a solution to this problem, we propose the use of least-squares-optimisation-based smoothing techniques that suppress the noise peaks in the ECG while preserving the QRS complexes. We also propose a novel nonlinear transformation technique that is applied after the smoothing operations, which equalises the QRS amplitudes without boosting the supressed noise peaks. After these preprocessing operations, the R-peaks can finally be detected with high accuracy. The proposed technique has a low computational load and, therefore, it can be used for real-time QRS detection in a wearable device such as a Holter monitor or for fast offline QRS detection. The offline and real-time versions of the proposed technique have been evaluated on the standard MIT-BIH database. The offline implementation is found to perform better than state-of-the-art techniques based on wavelet transforms, empirical mode decomposition, etc. and the real-time implementation also shows improved performance over existing real-time QRS detection techniques.

ECG signal analysis using modified S-transform.

Accurate detection of QRS complexes is essential for the investigation of heart rate variability. Several transform techniques have been proposed and extensively used for the detection and analysis of QRS complexes. In this proposed work, the de-noised ECG signal is subjected to a modified S-transform for QRS complex detection.The performance analysis of the proposed work is evaluated using parameters such as sensitivity, positive predictivity and accuracy. The algorithm delivers sensitivity, positive predictivity and overall accuracy of 99.91, 99.91 and 99.77%, respectively. Furthermore, a search back mechanism is employed, which specifies the filtered electrocardiogram (ECG) segment, which was traced for the true R-peak locations. The modified S-transform based QRS complex detection algorithm provides an excellent search back range of only ±2 samples in comparison with other earlier proposed algorithms.

A QRS-complex detector of the electrocardiogram signal for the long-term monitoring of the patient’s condition

A procedure for detection of the QRS complexes of electrocardiogram (ECG) signals for systems of long-term monitoring of the patient’s condition based on successive use of the band-pass filtering, the Hilbert transform, and an adaptive threshold algorithm is considered. The efficiency of different QRS-complex detectors has been investigated for model ECG signals in the presence of interferences of various nature and intensity. The efficiency of the proposed procedure has been additionally tested using various clinic records of ECG signals from the MIT Physionet database.

Efficient ECG Compression and QRS Detection for E-Health Applications

Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

Real time QRS complex detection using DFA and regular grammar

BackgroundThe sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration.ResultsThe proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks’ amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed.ConclusionsRegular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

BEAT CLASSIFICATION USING HYBRID WAVELET TRANSFORM BASED FEATURES AND SUPERVISED LEARNING APPROACH

This paper describes an automatic heartbeat recognition based on QRS detection, feature extraction and classification. In this paper five different type of ECG beats of MIT BIH arrhythmia database are automatically classified. The proposed method involves QRS complex detection based on the differences and approximation derivation, inversion and threshold method. The computation of combined Discrete Wavelet Transform (DWT) and Dual Tree Complex Wavelet Transform (DTCWT) of hybrid features coefficients are obtained from the QRS segmented beat from ECG signal which are then used as a feature vector. Then the feature vectors are given to Extreme Learning Machine (ELM) and k- Nearest Neighbor (kNN) classifier for automatic classification of heartbeat. The performance of the proposed system is measured by sensitivity, specificity and accuracy measures.

Design of a Low-Complexity Real-Time Arrhythmia Detection System

This paper presents a low-complexity real-time arrhythmia detection system that includes the QRS complex and arrhythmia detection. The ECG data in the MIT-BIH arrhythmia database were used for simulations and verification. For QRS complex detection, this paper proposes the advanced So and Chan for detecting the R-peak and baseline of the QRS complex. Compared with the accuracy obtained using the original So and Chan method (94.61%), an accuracy of 99.29% was obtained using the advanced So and Chan method. For arrhythmia detection, the proposed system is implemented with an advanced sum of trough and various features of disease symptoms. It can identify tachycardia, bradycardia, premature contraction, and two types of cardiovascular diseases; its detection accuracy can reach 98.05%. If a morbid state occurs, a warning message will be sent to a user. Because of its low complexity, the proposed detection system can be integrated with wearable electronic devices for detecting an arrhythmia immediately.

Heartbeat classification system based on neural networks and dimensionality reduction

Introduction This paper presents a complete approach for the automatic classification of heartbeats to assist experts in the diagnosis of typical arrhythmias, such as right bundle branch block, left bundle branch block, premature ventricular beats, premature atrial beats and paced beats. Methods A pre-processing step was performed on the electrocardiograms (ECG) for baseline removal. Next, a QRS complex detection algorithm was implemented to detect the heartbeats, which contain the primary information that is employed in the classification approach. Next, ECG segmentation was performed, by which a set of features based on the RR interval and the beat waveform morphology were extracted from the ECG signal. The size of the feature vector was reduced by principal component analysis. Finally, the reduced feature vector was employed as the input to an artificial neural network. Results Our approach was tested on the Massachusetts Institute of Technology arrhythmia database. The classification performance on a test set of 18 ECG records of 30 min each achieved an accuracy of 96.97%, a sensitivity of 95.05%, a specificity of 90.88%, a positive predictive value of 95.11%, and a negative predictive value of 92.7%. Conclusion The proposed approach achieved high accuracy for classifying ECG heartbeats and could be used to assist cardiologists in telecardiology services. The main contribution of our classification strategy is in the feature selection step, which reduced classification complexity without major changes in the performance.

VLSI Implementation of a Cost-Efficient Micro Control Unit With an Asymmetric Encryption for Wireless Body Sensor Networks

This paper presents a very large-scale integration (VLSI) circuit design of a micro control unit (MCU) for wireless body sensor networks (WBSNs) in cost-intention. The proposed MCU design consists of an asynchronous interface, a multisensor controller, a register bank, a hardware-shared filter, a lossless compressor, an encryption encoder, an error correct coding (ECC) circuit, a universal asynchronous receiver/transmitter interface, a power management, and a QRS complex detector. A hardware-sharing technique was added to reduce the silicon area of a hardware-shared filter and provided functions in terms of high-pass, low-pass, and band-pass filters according to the uses of various body signals. The QRS complex detector was designed for calculating QRS information of the ECG signals. In addition, the QRS information is helpful to obtain the heart beats. The lossless compressor consists of an adaptive trending predictor and an extensible hybrid entropy encoder, which provides various methods to compress the different characteristics of body signals adaptively. Furthermore, an encryption encoder based on an asymmetric cryptography technique was designed to protect the private physical information during wireless transmission. The proposed MCU design in this paper contained 7.61k gate counts and consumed 1.33 mW when operating at 200 MHz by using a 90-nm CMOS process. Compared with previous designs, this paper has the benefits of increasing the average compression rate by over 12% in ECG signal, providing body signals analysis, and enhancing security of the WBSNs.

Probabilistic data fusion model for heart beat detection from multimodal physiological data

Automatic detection of heart beats constitutes the basis for electrocardiogram (ECG) analysis and mainly relies on detecting QRS complexes. Detection is typically performed by analyzing the ECG signal. However, when signal quality is low, it often leads to the triggering of false alarms. A contemporary approach to reduce false alarm rate is to use multimodal data such as arterial blood pressure (ABP) or photoplethysmogram (PPG) signals. To leverage the correlated temporal nature of these signals, a probabilistic data fusion model for heart beat detection is proposed. A hidden Markov model is used to decode waveforms into segments. A Bayesian network is employed for capturing intersegmental coupling between waveforms and detecting heart beats. The performance of the proposed system was evaluated on a dataset provided by PhysioNet Challenge 2014: Robust Detection of Heart Beats in Multimodal Data. The proposed method is comparatively analyzed with a baseline hidden Markov model method for ECG and an improvement of 9% in sensitivity and 26% in positive predictivity is observed. The efficiency of the proposed model is also compared with related data fusion methods and a comparable performance is found. The robustness of the method is analyzed by inducting Gaussian noise into the dataset. A performance gain of 31% both in sensitivity and positive predictivity is obtained in the worst case where both ECG and ABP are noisy with -6 dB signal-to-noise ratio.