QRS Complex Of Electrocardiogram Research Articles

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ±0.317 and 0.302 ±0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https://github.com/lileitech/MI_inverse_inference.

Multidimensional assessment of adverse events of bupropion: A large-scale data analysis from the FAERS database

ObjectiveBupropion, a monocyclic antidepressant, aids in smoking cessation, treats major depression, and prevents severe depression in seasonal affective disorder patients. Yet, its adverse reactions remain insufficiently studied. MethodsAll data from the raw data packages for 78 quarters from the 1st quarter of 2004 to the 2nd quarter of 2023 were extracted from the FDA Adverse Event Reporting System (FAERS) database and imported into the SAS9.4 software for data cleaning and analysis. The Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) methods were used to analyze drug adverse events and assess their compliance with various screening criteria. ResultsThe results showed a total of 36,862 reports related to Bupropion use, identifying 364 positive reaction terms (PT) covering 23 System Organ Classes (SOCs). In addition to known side effects, some new potential adverse reactions were found, such as Stool analysis abnormal, Oculocephalogyric reflex absent, Suspected suicide, and so on. At the same time, reactions like Encephalopathy neonatal, Hyponatraemic coma, and Electrocardiogram QRS complex prolonged were prominently ranked. Notably, occurrences such as Urine amphetamine positive and Amphetamines positive were relatively high, suggesting extra caution for these potential adverse reactions during clinical use of Bupropion. ConclusionThese findings highlight the potential health risks of long-term Bupropion use, especially concerning efficacy, positive drug tests, and suicidal tendencies. Therefore, it is recommended to monitor and assess patients using Bupropion more stringently to use this therapeutically potential drug more safely and effectively.

Amitriptyline intoxication in bullfrogs causes widening of QRS complexes in electrocardiogram.

Amitriptyline intoxication is caused by its suicidal or accidental overdose. In the present study, by intravenously injecting 1.5 or 3.0 mg/kg amitriptyline into bullfrogs, we actually revealed that amitriptyline causes the widening of QRS complexes in electrocardiogram (ECG). In simultaneous recordings of the cardiac action potential, amitriptyline decreased the slope of phase 0 in the action potential, indicating the inhibition of the inward sodium currents during this phase. The following treatment with sodium bicarbonate quickly restored the widened QRS complexes in the ECG, demonstrating the counteraction with the sodium channel blockade caused by amitriptyline. The dual recordings of ECG waveforms and the action potential in cardiomyocytes enabled us to demonstrate the mechanisms of characteristic ECG abnormalities caused by amitriptyline intoxication.

Electrocardiogram QRS Complex Duration as a Predictor of Hospital Prognosis in Patients With COVID-19

Aim To determine the effect of major electrocardiographic (ECG) parameters on the prognosis of patients with COVID-19.Material and methods One of systemic manifestations of COVID-19 is heart injury. ECG is the most simple and available method for diagnosing the heart injury, which influences the therapeutic approach. This study included 174 hospitalized patients with COVID-19. Major ECG parameters recorded on admission and their changes before the discharge from the hospital or death of the patient, were analyzed, and the effect of each parameter on the in-hospital prognosis was determined. Results were compared with the left ventricular ejection fraction (LV EF), laboratory data, and results of multispiral computed tomography (MSCT) of the lungs.Results ECG data differed on admission and their changes differed for deceased and discharged patients. Of special interest was the effect of the QRS complex duration at baseline and at the end of treatment on the in-hospital survival and mortality rate. The Cox regression analysis showed that the QRS complex duration (relative risk (RR) 2.07, 95% confidence interval (CI): 1.17-3.66; р=0.01), MSCT data (RR, 1.54; 95 % CI: 1.14-2.092; р=0.005), and glomerular filtration rate (GFR) (RR, 0.98; 95 % CI: 0.96-0.99; р=0.001) had the highest predictive significance. In further comparison of these three indexes, the QRS duration and GFR retained their predictive significance, and a ROC analysis showed that the cut-off QRS complex duration was 125 ms (р=0.001). Patients who developed left bundle branch block (LBBB) in the course of disease also had an unfavorable prognosis compared to other intraventricular conduction disorders (р=0.038). The presence of LBBB was associated with reduced LV EF (р=0.0078). The presence of atrial fibrillation (AF) significantly predetermines a worse outcome both at the start (р=0.011) and at the end of observation (р=0.034). A higher mortality was observed for the group of deceased patients with ST segment deviations, ST elevation (р=0.0059) and ST depression (р=0.028).Conclusion Thus, the QTc interval elongation, LBBB that developed during the treatment, AF, and increased QRS complex duration are the indicators that determine the in-hospital prognosis of patients with COVID-19. The strongest electrocardiographic predictor for an unfavorable prognosis was the QRS complex duration that allowed stratification of patients to groups of risk.

Heartbeat Detection in Seismocardiograms with Semantic Segmentation.

Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability.

A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet Transform (DWT) Algorithm.

This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.

A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

R-Wave Singularity: A New Morphological Approach to the Analysis of Cardiac Electrical Dyssynchrony.

R-wave singularity (RWS) measures the intermittence or discontinuousness of R waves. It has been broadly used in QRS (QRS complex of electrocardiogram) detection, electrocardiogram (ECG) beats classification, etc. In this article, we novelly developed RWS to the analysis of QRS morphology as the measurement of ventricular dyssynchrony and tested the hypothesis that RWS could enhance the discrimination between control and acute myocardial infarction (AMI) patients. Holter ECG recordings were obtained from the Telemetric and Holter ECG Warehouse database, among which database Normal was extracted as normal controls (n = 202) and database AMI (n = 93) as typical subjects of autonomic nervous system dysfunction and cardiac electrical dyssynchrony with high risk for cardiac arrhythmias and sudden cardiac death. Experimental results demonstrate that RWS measured by Lipschitz exponent calculated from 5-min Holter recordings was significantly less negative in early AMI and late AMI than that in Normal subjects for overall, elderly, and elderly male groups, which suggested the heterogeneous depolarization of the ventricular myocardium during AMI. Receiver operating characteristic curve analyses show that combined with heart rate variability parameters, Lipschitz exponent provides higher accuracy in distinguishing between the patients with AMI and healthy control subjects for overall, elderly, elderly male, and elderly female groups. In summary, our study demonstrates the significance of using RWS to probe the cardiac electrical dyssynchrony for AMI. Lipschitz exponent may be valuable and complementary for existing cardiac resynchronization therapy and autonomic nervous system assessment.

Optimal design of zero-phase digital Riesz FIR fractional-order differentiator

This article introduces an optimal design of finite impulse response-type Riesz fractional-order digital differentiator (FODD) of orders p = 0.3, 0.5, and 0.75. A very little study on Riesz-based differentiator has been reported so far. Moreover, none of the published Riesz FODD-based literature has studied the optimal design of Riesz FODD, which is explored in this article. In the reported literature, the Riesz FODD is realized by adopting conventional mathematical approaches, which are not efficient for the Riesz FODD-type design problem where the nature of the search landscape is multidimensional, multimodal, non-uniform, and nonlinear. To solve this problem, the design parameters of the proposed Riesz FODDs are estimated by using three independent metaheuristic optimization algorithms called salp swarm algorithm (SSA), gases Brownian motion optimization (GBMO), and gravitational search algorithm (GSA). In SSA, the adaptive variation of the iteration-dependent parameters helps the search agents not only to avoid the suboptimal solutions, but also ensures the faster convergence to reach the near-global optimal solution compared to GSA- and GBMO-based design approaches. The maximum phase error (MPE) and maximum absolute magnitude error (MAME) of the proposed SSA-based Riesz FODD are as low as 3.7119E−014 degree and − 9.6824 dB, respectively, for p = 0.3; 3.1523E−014 degree and − 15.6443 dB, respectively, for p = 0.5; and 3.5780E−014 degree and − 18.7525 dB, respectively, for p = 0.75. In terms of MPE and MAME, the proposed SSA-based Riesz FODD significantly outperforms the GSA- and GBMO-based Riesz FODD designs. The proposed SSA-based Riesz FODD of order p = 0.5 is also implemented on the TMS320C6713 digital signal processor, and its precise zero-phase performance is tested on real electrocardiogram (ECG) signal. Moreover, the proposed Riesz FODD is utilized in the ECG QRS complex identification system to demonstrate its real-time application.

Reconstructing QRS Complex From PPG by Transformed Attentional Neural Networks

Technology that translates photoplethysmogram (PPG) into the QRS complex of electrocardiogram (ECG) would be transformative for people who require continuously monitoring. However, directly decoding the QRS complex of ECG from PPG is challenging because PPG signals usually have different offsets due to 1) different devices, and 2) personal differences, which makes the alignment difficult. In this paper, we make the first attempt to reconstruct the QRS complex of ECG only from the recording of PPG by an end-to-end deep learning-based approach. Specifically, we propose a novel encoder-decoder architecture containing three components: 1) a sequence transformer network which automatically calibrates the offset, 2) an attention network, which dynamically identifies regions of interest, and 3) a new QRS complex-enhanced loss for better reconstruction. The experiment results on a real dataset demonstrate the effectiveness of the proposed method: 3.67% R peak failure rate of the reconstructed ECG and high correlation of pulse transit time between the reconstructed QRS complex and the groundtruth QRS complex (ρ = 0.844), which creates a new opportunity for low-cost clinical studies via the waveform-level reconstruction of the QRS complex of ECG from PPG.

Robust identification of QRS-complexes in electrocardiogram signals using a combination of interval and trigonometric threshold values

Combination of interval and amplitude threshold-based algorithms have been tested rigorously over the last few decades for detecting the QRS-complexes in electrocardiogram (ECG) signals. However, the very eccentric nature of appearance of the QRS-complexes often make an amplitude threshold-based algorithm fail. This paper presents a unique combination of trigonometric feature and interval threshold-based algorithm for efficient detection of the QRS-complexes. First, the ECG signal is denoised, and the first-difference (FD) of the denoised ECG is calculated. The FD signal is then sequentially passed through a Hilbert and Shannon energy (SE) transformers. Next, a histogram-based analysis technique is applied on the SE-transformed data to filter out the contributions of the low frequency components of the ECG signal. Next, each of the modified SE-transformed data having a non-zero amplitude value is mapped onto the denoised ECG signal, and a unique trigonometric feature is extracted from that corresponding location of the denoised ECG signal. Finally, the QRS-complexes are identified using a combination of trigonometric and interval-threshold values. Wired and wireless ECG signals are collected from seven databases, and are used as the evaluation test-beds of the proposed algorithm. The performance of the algorithm is found highly-competent compared to that of the state-of-the-art methods. The significance of the proposed algorithm is that, not only the detection-accuracy of the algorithm is high, it is also fast; the trigonometric feature can be used to extract the breathing rate and pattern; the trigonometric feature can also be used for the identification of the abnormal QRS-complexes.

Endocardial electro-anatomic mapping in healthy horses: Normal sinus impulse propagation in the left and right atrium and the ventricles

Understanding the depolarisation pattern of the equine heart under normal physiologic conditions, and its relationship to the surface electrocardiogram (ECG), is of uppermost importance before any further research can be done about the pathophysiology of complex arrhythmias. In the present study, a 3D electro-anatomical mapping system was used to evaluate the qualitative and quantitative depolarisation patterns and correlation to the surface ECG of both the atrial and ventricular endocardium in seven healthy horses in sinus rhythm under general anaesthesia. Bipolar activation maps of the endocardium were analysed.The first atrial activation was located at the height of the terminal crest. Only one interatrial conduction pathway was recognised. The first and second P wave deflections represent the right and left atrial depolarisation, respectively. Bundle of His electrograms could be recorded in 5/7 horses. Left ventricular activation started at the mid septum and right ventricular activation started apically from the supraventricular crest. This was followed by separate depolarisations at the height of the mid free wall. Further ventricular depolarisation occurred in an explosive pattern. Electrically active tissue could be found in all pulmonary veins. In contrast to findings of previous studies, all parts of the ventricular depolarisation contributed to the surface ECG QRS complex. This study provides a reference for the normal sinus impulse endocardial propagation pattern and for conduction velocities in equine atria and ventricles.

Semantic Segmentation of QRS Complex in Single Channel ECG with Bidirectional LSTM Networks

In this paper, bidirectional Long Short-Term Memory (BiLSTM) networks are designed to realize the semantic segmentation of QRS complex in single channel electrocardiogram (ECG) for the tasks of R peak detection and heart rate estimation. Three types of seq2seq BiLSTM networks are introduced, including the densely connected BiLSTM with attention model, the BiLSTM U-Net, and the BiLSTM U-Net++. To alleviate the sparse problem of the QRS labels, symmetric label expansion is applied by extending the single R peak into a time interval of fixed length. Linear ensemble method is introduced that averages the outputs of different BiLSTM networks. The cross-validation results show significant increase of the accuracy and decrease of discontinuous gaps in the QRS interval prediction by the ensembling over singular neural networks.

Single channel high noise level ECG deconvolution using optimized blind adaptive filtering and fixed-point convolution kernel compensation

An electrocardiogram (ECG) is used to record the electrical activity of the heart. However, ECG signals are susceptible to the noise from various sources which increases the probability of misinterpretation and can affect the diagnostic process. Traditional noise cancellation techniques, which uses finite and deterministic coefficient, are not efficient, since the ECG signals are non-stationary. Thus, adaptive filters are commonly utilized on such signal as they can adjust their coefficient according to the changing nature of non-stationary signal. Adaptive algorithms still have a disadvantage that they require the model of noise or desired signal. In this paper, a novel algorithm is introduced based on fixed-point convolution kernel compensation for finding a model for using an adaptive filter; then a recursive least square method is used for completing steps of deconvolution of the ECG signal. The deconvolution method can be used for denoising ECG signals in very low signal to noise ratio circumstances and also can be used in blind source separation applications such as separation of fetal ECG from maternal ECG. ECG signals were utilized in this study are taken from the MIT-BIH Arrhythmia database for showing the performance of the algorithm on denoising applications. The results demonstrate that the proposed algorithm renders a much-improved performance in removing the noise from ECG signals, especially in a scenario where signal to noise ratio is negative. Moreover, the noninvasive fetal ECG dataset (NI-FECG) provided by Physionet is also used for fetal ECG extraction by a single thoracic channel. By comparing fetal ECG extraction methods in the literature and the proposed method, it reveals that the proposed method can extract the QRS complex of fetal ECG by a single thoracic channel as accurate as other methods which use abdominal channels.

Detection of electrocardiogram QRS complex based on modified adaptive threshold

<p>It is essential for medical diagnoses to analyze Electrocardiogram (ECG signal). The core of this analysis is to detect the QRS complex. A modified approach is suggested in this work for QRS detection of ECG signals using existing database of arrhythmias. The proposed approach starts with the same steps of previous approaches by filtering the ECG. The filtered signal is then fed to a differentiator to enhance the signal. The modified adaptive threshold method which is suggested in this work, is used to detect QRS complex. This method uses a new approach for adapting threshold level, which is based on statistical analysis of the signal. Forty-eight records from an existing arrhythmia database have been tested using the modified method. The result of the proposed method shows the high performance metrics with sensitivity of 99.62% and a positive predictivity of 99.88% for QRS complex detection.</p>

Approach to Improving the Automated System Computer Analysis of the Electrocardiogram

Purpose is to develop and test an approach to the improvement of automated electrocardiogram (ECG) computer analysis systems, which increases physician productivity. Materials and methods. The existing prototype of the ECG computer analysis system developed by the authors is used as materials. Dynamic time warping (DTW) method, as well as structural and object-oriented software development techniques were used. Results and discussion. The main idea of improving automated computer-assisted ECG analysis systems is to provide the doctor with convenient software tools that allow you to: set various templates for QRS complexes; select templates and automatically find QRS complexes in ECG that are most similar to the selected template. The main result of the work is an algorithm that allows to quantify the degree of similarity of a QRS complex isolated from an ECG and a given pattern. The algorithm is based on the DTW method. The proposed improvement was tested when modifying the existing ECG computer analysis system. As a result of the modification, convenient interactive tools were added to the system, which allow the doctor to navigate the QRS complexes while viewing the ECG, taking into account their degree of similarity to the selected template. This gave the doctor the opportunity to quickly assess the patient’s condition and make a conclusion. Conclusion. The proposed approach allows you to modify the automated computer-aided analysis of the ECG in order to increase the productivity of the doctor.

An Efficient and Robust Digital Fractional Order Differentiator Based ECG Pre-Processor Design for QRS Detection.

This paper presents an efficient infinite impulse response type digital fractional order differentiator (DFOD) based electrocardiogram (ECG) pre-processor to detect QRS complexes. First, an efficient optimizer namely, Antlion optimization algorithm is employed to solve the proposed DFOD design problem. Then, the designed DFOD is deployed in the pre-processing stage of a threshold independent R-peak detection technique. Finally, the proposed QRS complex detector is thoroughly assessed on the standard ECG datasets of MIT/BIH Arrhythmia, MIT/BIH ST Change, MIT/BIH Supraventricular Arrhythmia, European ST-T, QT, and T-Wave Alternans Challenge databases to show the wide sense practicability of the proposed DFOD-based QRS detector. The root-means-square magnitude error (RMSME) and the average group delay (τDD) metrics of the proposed DFOD are as low as -38.17 dB and 0.04 samples, respectively. The percentage of improvement in terms of RMSME metric compared to the best-reported approach is 15%. The overall sensitivity of 99.89% and positive predictivity of 99.88% are incurred by considering all the six databases. To the best of the authors' knowledge, it is the first time when the evolutionary algorithm based IIR-type DFOD is employed for the QRS complex detection and establishing its performance superiority. The results so obtained are compared with the results of all the recently reported QRS detectors. The proposed DFOD based ECG pre-processor has a great potential to robustly generate the feature signal related to the ECG QRS complex irrespective of the ECG morphology. Thus, the proposed DFOD based QRS detector can be employed in clinical ECG monitoring devices to augment the QRS detection performance.

Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex

Detection of QRS complexes in electrocardiogram (ECG) signal is crucial for automated cardiac diagnosis. Automated QRS detection has been a research topic for over three decades and several of the traditional QRS detection methods show acceptable detection accuracy, however, the applicability of these methods beyond their study-specific databases was not explored. The non-stationary nature of ECG and signal variance of intra and inter-patient recordings impose significant challenges on single QRS detectors to achieve reasonable performance. In real life, a promising QRS detector may be expected to achieve acceptable accuracy over diverse ECG recordings and, thus, investigation of the model's generalization capability is crucial. This paper investigates the generalization capability of convolutional neural network (CNN) based-models from intra (subject wise leave-one-out and five-fold cross validation) and inter-database (training with single and multiple databases) points-of-view over three publicly available ECG databases, namely MIT-BIH Arrhythmia, INCART, and QT. Leave-one-out test accuracy reports 99.22%, 97.13%, and 96.25% for these databases accordingly and inter-database tests report more than 90% accuracy with the single exception of INCART. The performance variation reveals the fact that a CNN model's generalization capability does not increase simply by adding more training samples, rather the inclusion of samples from a diverse range of subjects is necessary for reasonable QRS detection accuracy.

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.