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ABSTRACT Accurate diagnosis of wide QRS complex tachycardia (WCT) with the earliest ventricular origin potentially located in the His‐Purkinje system, and exploration of its etiology are helpful for further diagnosis and treatment. This article finds through specific cases that one of the possible etiologies is aconitine poisoning.

Introduction: The QRS complex is the most prominent waveform within the electrocardiograph (ECG) signal. The accurate detection of the QRS complex is an essential step in the ECG analysis algorithm, which can provide fundamental information for the monitoring and diagnosis of the cardiovascular diseases. Methods: Seven public ECG datasets were used in the experiments. A simple and effective QRS complex detection algorithm based on the deep neural network (DNN) was proposed. The DNN model was composed of two parts: a feature pyramid network (FPN) based backbone with dual input channels to generate the feature maps, and a location head to predict the probability of point belonging to the QRS complex. The depthwise convolution was applied to reduce the parameters of the DNN model. Furthermore, a novel training strategy was developed. The target of the DNN model was generated by using the points within 75 milliseconds and beyond 150 milliseconds from the closest annotated QRS complexes, and artificial simulated ECG segments with high heart rates were generated in the data augmentation. The number of parameters and floating point operations (FLOPs) of our model was 26976 and 9.90M, respectively. Results: The proposed method was evaluated through a cross-dataset test and compared with the sophisticated state-of-the-art methods. On the MITBIH NST, the proposed method demonstrated slightly better sensitivity (95.59% vs. 95.55%) and lower presicion (91.03% vs. 92.93%). On the CPSC 2019, the proposed method have similar sensitivity (95.15% vs.95.13%) and better precision (91.75% vs. 82.03%). Discussion: Experimental results show the proposed algorithm achieved a comparable performance with only a few parameters and FLOPs, which would be useful for the application of ECG analysis on the wearable device. [ABSTRACT FROM AUTHOR]

Background: as a relatively high number of ST-segment elevation myocardial infarction (STEMI) patients develop major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI), our aim was to determine the significance, and possible predictive value of QRS complex width and ST-segment elevation. Methods: our patient sample included 200 PCI-treated STEMI patients, which were divided into two groups based on the following duration of symptoms: (I) less than 6 h, and (II) 6 to 12 h. For every patient, an ECG was performed at six different time points, patients were followed for up to six years for the occurrence of MACE. Results: the mean age was 60.6 ± 11.39 years, and 142 (71%) were male. The 6–12 h group had significantly wider QRS complex, higher ST-segment elevation, lower prevalence of ST-segment resolution as well as MACE prevalence (p < 0.05). ECG parameters, QRS width, and magnitude of ST-segment elevation were proved to be independent significant predictors of MACE in all measured time points (p < 0.05). Even after controlling for biomarkers of myocardial injury, these ECG parameters remained statistically significant predictors of MACE (p < 0.05). Conclusion: our study highlights that wider QRS complex and a more pronounced ST-segment elevation are associated with longer total ischemic time and higher risk of long-term MACE. [ABSTRACT FROM AUTHOR]

Ventricular depolarization refers to the electrical activation and subsequent contraction of the ventricles, visible as the QRS complex on a 12-lead electrocardiogram (ECG). A well-organized and efficient depolarization is critical for cardiac function. Abnormalities in ventricular depolarization may indicate various pathologies and can be present in all leads if the condition is general, or in a subgroup of anatomically contiguous leads if the condition is limited to the corresponding anatomic location of the heart. Furthermore, the assessment of depolarization abnormalities on the ECG may either be identified visually or this may depend on further processing. In recent decades, assessment of depolarization abnormalities has received a lot of attention for cardiac risk stratification. This risk stratification aims to identify patients at high risk of adverse cardiac events, to tailor preventive or therapeutic interventions. In this review, we provide an oversight of different techniques for assessing abnormal ventricular depolarization and their value in diagnosing certain conditions, in risk stratification of adverse events, and in guiding therapeutic decisions. This includes QRS alterations directly corresponding to cardiac conditions, such as bundle branch blocks, or the presence of a delta wave, and also metrics aiming to qualitatively or quantitatively assess myocardial scarring, such as QRS (micro)fragmentation and QRS-scoring, and techniques assessing abnormal late depolarizations, such as signal-averaged ECG. While most established assessments of abnormal depolarization rely on human interpretation and are limited by visual detection, recently introduced analyses, such as QRS micro-fragmentation, aim to tackle these limitations. Besides eliminating bias, these automated analyses bypass the need for human interpretation, thereby paving the way for large population studies.

The morphological identification of QRS complex is a key in the detection of abnormal ECG, which acts as the basis for disease diagnosis. The existing QRS morphological recognition methods either identify only a few morphologies, or are sensitive to parameter settings, and the performance is not ideal. Based on this, a sliding window voting strategy based on hidden Markov model (SWVHMM) is proposed to automatically identify QRS morphologies. Firstly, each QRS complex is divided into four phases, and a sliding window is set for each phase to extract samples. Secondly, the waveform of each phase is regarded as a state, and the cluster center of the window samples acts as the observation to construct a state-constrained Hidden Marko model. Finally, we vote on the result of the combination of different phase windows to identify the target morphology pattern with the largest possibility. On the real data set labelled by professional doctors, compared with existing methods, our method improves F1 measure by 5.97%, 5.49% and 2.27%, respectively. The results show that SWVHMM can identify a variety of morphology patterns with improved accuracy. [ABSTRACT FROM AUTHOR]

To evaluate QRS complex features during the first month of life and the association with echocardiographic measurements of left ventricular mass in neonates. Prospective cohort study of neonates with electrocardiography (ECG) and echocardiography performed during the first month of life. Left ventricular mass index (LVMI) was determined by echocardiography and the correlation with electrocardiographic markers of LVMI outliers (≥ 98th percentile) were analyzed. We included 17,450 neonates (52% boys; median age at examination 11 days) and found an increase in median QRS duration and LVMI during the first month of life (54 vs. 56 ms and 24.7 vs. 28.6 g/m2 at days 0–4 and 25–30, respectively; both p < 0.001). All investigated ECG features (QRS duration, QRS area in V1/V6, maximum amplitudes of S-V1/R-V6, and the Sokolow–Lyon voltage product) showed no to low correlation with LVMI, resulting in low sensitivities (0–9.0%), but high specificities (97.2–98.1%), and area under the curve values close to the identity line (0.49–0.61) for identifying LVMI outliers. Adjustment of outlier definition for LVMI and threshold for QRS features had no significant effect on sensitivity. We present reference values for QRS complex features and their association with LVMI in neonates from a large, unselected, population-based cohort. The QRS complex gradually evolved during the first month of life but had a low correlation with LVMI. Our results indicate a poor diagnostic value of using ECG features to identify LVMI outliers in neonates. Trial Registry Copenhagen Baby Heart, NCT02753348, https://clinicaltri-als.gov/ct2/show/NCT02753348?cond=Copenhagen+Baby+Heart&draw=2&rank=1, deidentified individual participant data will not be made available. [ABSTRACT FROM AUTHOR]

The QRS complex duration is commonly used to prognosticate severity, predict outcomes, and indicate treatment in overdose. However, literature to support this practice is mixed in tricyclic antidepressant overdoses and absent in non-tricyclic antidepressant overdoses. Our objective was to assess the validity of QRS complex duration as a prognostic marker in overdose. This was a secondary analysis of cases reported to the Toxicology Investigators Consortium between January 1, 2010, and December 31, 2022. Cases were assessed to determine the six xenobiotics most associated with QRS complex prolongation. All cases involving these six xenobiotics, regardless of QRS complex duration, constituted the study cohort. Inclusion criteria were cases of patients older than 12 years old with single-xenobiotic exposures. Clinical outcomes evaluated were seizure, ventricular dysrhythmia, metabolic acidosis, and death. Of 94,939 total cases, diphenhydramine, amitriptyline, bupropion, quetiapine, nortriptyline, and cocaine were most associated with QRS complex prolongation. Inclusion criteria were met by 4,655 cases of exposure to these xenobiotics. QRS complex prolongation was associated with increased odds ratio of seizure in all included xenobiotics, of ventricular dysrhythmia in all included xenobiotics except nortriptyline, and of metabolic acidosis or death in all included xenobiotics except nortriptyline and quetiapine. A normal QRS complex duration had a negative predictive value of greater than or equal to 93.0 percent of developing metabolic acidosis and 98.0 percent of developing a ventricular dysrhythmia or death from the xenobiotics studied. This study demonstrates that patients with QRS complex prolongation from all six xenobiotics studied had an increased prevalence and odds of developing severe outcomes. Furthermore, patients who did not develop QRS complex prolongation were unlikely to develop a ventricular dysrhythmia, metabolic acidosis, or death. These findings were noted in six xenobiotics that mechanistically can cause QRS complex prolongation through sodium channel or gap junction inhibition. Identification of patients at risk for severe outcomes after overdose can be aided by measuring the QRS complex duration. If prospectively validated, these outcomes have implications on risk stratification, disposition level of care, and appropriateness of treatments. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global deaths. Among these, heart attacks and strokes were responsible for 85%, whereas CVDs caused 38% of the premature deaths (under age of 70) affected by non-communicable diseases. The rate of death can be delayed and may be prevented by efficiently analyzing the ECG signals (i.e., captured by a non-invasive method) at the early stage of the disease. QRS complex in ECG provides pivotal information about the heart diseases. Many researchers have analyzed the ECG signal by traditional approach and machine learning methods for identifying the heart disorders. Performance of these techniques depend on accurate detection of different parameters (such as: P-, Q-, R-, S-, T-waveforms, QRS complex duration, R-peak, PR-interval, and RR-interval) from the ECG signals. This review paper provides a detail discussion and comparison of various ECG analysis techniques along with their pros and cons. It summarizes the ECG capturing method, databases available for disease detection & classification, and performance measures used by the researchers. Based on these, a future road map is suggested for real time ECG analysis (for identifying the heart related conditions) captured from the wearable devices and suggested the precautionary steps by the artificial system and experts. This method will help in identifying the co-relation of heart disorders with other body organs (such as: retina and brain parts) by analyzing ECG, fundus image, and magnetic resonance imaging (MRI) of human brain.

Since the outbreak of coronavirus also known as COVID-19, there have been several studies on the disease. This study investigates patients’ electrocardiography (ECG) properties for an accurate prediction of this infectious disease. Our findings will be useful to medical practitioners in the accurate prognosis of COVID-19. We analyzed ECG datasets of patients who had tested positive for COVID-19 and Normal Persons who had tested negative. Using the analyzed dataset, we designed, developed, and evaluated twelve machine-learning models to predict COVID-19 with different combinations of learning algorithms and feature engineering techniques. Time, frequency, and time-frequency domain features were extracted using Time Series Feature Extraction Library (TSFEL). A combination of domains’ features comprising of Time, Frequency, and Time-Frequency attributes for prediction were also investigated. We deployed a T-test to determine the necessary and enough features for our predictive modeling. The ECG morphological feature analysis was based on peak-to-peak detection, onset, and offset of the QRS complex. K-nearest neighbors (KNN), Random Forest, and Support Vector Machine (SVM) learning algorithms were deployed to classify the ECG signals for the prediction of COVID-19. For each feature domain, the Shapley Additive Explanations (SHAP) visualization tool was used to ‘open’ the black box for explainability and interpretability of feature importance and weights. SHAP’s result suggests that extracted feature weight distribution in different algorithms shows consistency in feature importance per domain. However, the weight and the proportion are not the same. Performance evaluation of our classifiers was based on accuracy, sensitivity, precision, and F1 score. Our experimental results show that the classification encompassing all domain features (Temporal, Spectral, and wavelet) with Random Forest has a slight edge over other models. The proposed model has accuracy, sensitivity, precision, and F1 score of 97.09%, 98%, 97%, and 97%, respectively. COVID-19 continues to pose a threat to our world with its constant mutation, we believe that a study on its accurate prediction is critical for an effective universal mitigation strategy.

This paper presents the implementation of a sense amplifier for a low-power cardiac pacemaker using the Differential Voltage Current Conveyor (DVCC). Two significant aspects of the pacemaker are sensing and pacing. The pulse generator, which is the heart of the pacemaker, consists of a sense amplifier, a logic unit and a timing control unit. The sense amplifier comprises an instrumentation amplifier, a bandpass filter and a comparator that are used to detect the QRS complex wave from the cardiac signal. Based on the output of a sense amplifier, the logic unit and the timing control unit decide whether to pace the heart or not, which achieves the requirement of the demand pacing. In this paper, a novel design of the sense amplifier using a DVCC is proposed, and the simulations are performed using 130-nm TSMC technology. Furthermore, the modes of the pacemaker VVI, DDD and rate-responsive algorithms have been implemented using the structural approach in VHDL by taking into consideration the timing cycles of a pacemaker. The design analysis shows that the proposed model of pacemaker is highly efficient and consumes significantly less energy. [ABSTRACT FROM AUTHOR]

Finite rate of innovation (FRI) schemes have been proposed to reconstruct a class of discrete-time signals having small number of nonzero coefficients (sparse signals) from a limited number of observations. However, these reconstruction schemes achieve optimal performance up to a certain signal-to-noise ratio (SNR) and breakdown for smaller SNR values. Moreover, these are not universal as they are aware of the number of nonzero coefficients (a.k.a. L0 norm) for reconstruction of the signal. In this paper, we propose a novel FRI reconstruction scheme based on error decrease detector criterion to extend the current scheme to a universal one which enables reconstructing signals with an unknown number of nonzero coefficients. With noiseless conditions, we show that the proposed FRI scheme achieves perfect reconstruction of the original signal. And also, computer simulations for the noisy case are presented where the proposed scheme shows improvements over the traditional FRI scheme in the breakdown SNR. Further, an application of the proposed universal FRI scheme on reconstruction of magnetic resonance images and QRS complexes is demonstrated. [ABSTRACT FROM AUTHOR]

The Real-time wearable Electrocardiogram (ECG) monitoring device is a perfect choice for assisting in detecting cardiovascular disease. A novel ECG beat classification algorithm is presented for continuous heart monitoring on low-processing-capacity wearable devices. We discuss the different wearable wristwatch monitoring system, which allows for continuous 24-h heart rate monitoring. This paper introduces a novel method for classifying arrhythmias based on deep learning. The method relies on QRS/PT detection, Sigma-Delta Modulation (SDM), One-Dimensional Convolution Neural Networks (1D-CNN) algorithms. The QRS/PT wave detection system is based on the 1D-CNN and SDM framework with local minimum and local maximum point algorithms. We proposed a Long Short-Term Memory (LSTM) recurrent neural network with a blend classifier. The classifier combines two small LSTM networks' predictions using two different features directly extracted from 1D-CNN and SDM bitstreams. The proposed model is evaluated by detecting QRS/PT waves and classifying arrhythmias using the MIT-BIH Arrhythmia Database. Five different classifications are performed and evaluated by the AAMI standard: N, F, Q, S, and V. The values for accuracy, positive predictivity, sensitivity, and F1-score are 99.56%, 96.5%, 93.87%, and 95.18%, respectively. The proposed algorithm detects QRS/PT in approximately 2050 ms and classifies each heartbeat in approximately 40–60 ms with wearable devices, and it consumes 1.5 μ w total power. The results indicate that our proposed system outperforms previous research in accuracy and meets both computation time and low power consumption requirements. [ABSTRACT FROM AUTHOR]

Background and Aim: Prolonging the QT interval in the right bundle branch block (RBBB) can create challenges for electrophysiologists in estimating repolarization time and eliminating the effect of depolarization changes on QT interval. In this study, we aimed to develop a practice formula to eliminate the effect of depolarization changes on QT interval in patients with RBBB. Methods: This prospective study evaluated accidentally induced RBBB in patients undergoing electrophysiological study. Two expert electrophysiologists recorded the ECG parameters, including QRS duration, QT interval, and cycle length, in the patients. The formula was developed based on QT interval differences (with and without RBBB) and its proportion to QRS. Additionally, the Bazzet, Rautaharju, and Hodge formulas were used to evaluate QTc. Results: We evaluated 96 patients in this study. The mean QT interval without RBBB was 369.39 ± 37.38, reaching 404.22 ± 39.23 after inducing RBBB. ΔQT was calculated as 34.83 ± 17.61, and the ratio of ΔQT/QRS with RBBB was almost 23%. Our formula is: (QTwith RBBB − 23% × QRS). Subtraction of 25% instead of 23% seems more straightforward and practical. Our formula could also predict the QTc interval in RBBB based on the Bazzet, Rautaharju, and Hodge formulas. Conclusion: Previous formulas for QT correction were hard to apply in the clinical setting or were not specified for RBBB. Our new formula allows a rapid and practical method for QT correction in RBBB in clinical practice. [ABSTRACT FROM AUTHOR]

Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan–Tompkins (AMPT), which is a simplified version of the well-established Pan–Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan–Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5–20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection. [ABSTRACT FROM AUTHOR]

An electrocardiogram (ECG) is a noninvasive test, determining any defect in the heart rate or rhythm or changes in the shape of the QRS complex is very significant to detect cardiac arrhythmia. In this study, novel web-ECG simulation tools were proposed using MATLAB Builder NE with WebFigure and ASP.NET platform. The proposed web-ECG simulation tools consisted of two components. First, involved the analyses of normal real ECG signals by calculating the P, Q, R, S, and T values and detecting heart rate, while the second part related to extracting the futures of several types of abnormality real ECG. For calculating the PQRST values, simple and new mathematical equations are proposed in the current study using MATLAB. The Web ECG is capable to plot normal ECG signals and five arrhythmia cases, so the users are able to calculate PQRST easily using the proposed simple method. ECG simulation tools have been tested for validity and educational contributions with 62 undergraduate and graduate students at the Al-Nahrain University-Biomedical Engineering Department, Iraq. The proposed ECG simulation tools have been designed for academic learning to be run easily by a student using only any web browsers without the need for installing MATLAB or any extra programs. The proposed tools could provide a laboratory course for ECG signal analysis using a few buttons, as well as increase and develop the educational skills of students and researchers.

Sudden cardiac death (SCD) is a devastating cardiovascular condition that occurs suddenly within 1 hour of onset, usually without warning. The primary cause is a disruption in the heart's electrical system, leading to the cessation of blood flow and oxygen delivery to vital organs. Despite medical advancements, SCD prognosis remains poor, necessitating risk identification for lifesaving interventions. Hence, in this study, we analyse the morphological changes in electrocardiogram (ECG) signals associated with various cardiac conditions, including SCD and other conditions that can lead to SCD development. The ECG signals were pre-processed using a two-stage filter technique involving wavelet transform (WT) and progressive switching mean filter (PSMF) to eliminate noise and outliers. The denoised signals were then segmented and utilized for extracting temporal and amplitude features related to the P-wave, QRS complex, and T-wave components. These extracted features are further refined and given to the novel Ensemble Growing (EG) technique, which enhances the classification accuracy of different cardiac conditions. Examination of experimental findings revealed that the temporal features play an important role in the development of SCD. In particular, the prolonged durations of t_P-wave, t_QRS complex, t_T-wave, t_PpRp, t_RpSp, t_RpTp, t_PpQp, t_PpSp,t_PpTp, t_QpSp, and t_QpTp are closely associated with SCD. Furthermore, by incorporating significant temporal and amplitude features along with EG technique, produced an impressive SCD prediction accuracy of 99.82 % for 1 hour before its onset. This method offers advantages, including efficient handling of multiple cardiac conditions and real-time predictions, representing a major advancement towards proactive cardiac care and early SCD prediction. [ABSTRACT FROM AUTHOR]

Propafenone is a drug that is most commonly used in medicine for the prevention of supraventricular tachycardias, such as atrial fibrillation. It belongs to the Class IC class antiarrhythmics. Propafenone is well resorbed from the gastrointestinal tract (about 95%), but the bioavailability is low, only 12%. Almost all drug metabolism takes place in the liver, and in the bloodstream 95% of the drug is bound to plasma proteins, which is why hemodiafiltration has no effect in the treatment of possible overdose. The initial dose that clinicians prescribe to patients is generally 150 mg of the drug, two to three times a day, while the maximum dose would be twice as high. The drug is switched off if the QRS complex on the ECG expands by more than 20% compared to the original state. In literature we found, about 60 cases of propafenone poisoning were described.1 One retrospective study found that propafenone overdose mortality was 23%. Due to the strong binding to plasma proteins (> 95%) and the large volume of distribution, hemodialysis is not effective. In addition, to general emergency measures, it is necessary to monitor the vital indicators of patients in the intensive care unit. Defibrillation, as well as dopamine and isoproterenol infusion, are effective for controlling heart rhythm and blood pressure. Case report: 19-year-old female patient was treated at the Clinic for Internal Diseases due to intentional poisoning with propafenone tablets. The patient was hospitalized due to suicidal poisoning with propafenone tablets, and she drank 32 tablets. Upon admission to the Department of Intensive Care, cardiopulmonary resuscitation was immediately started due to pulseless cardiac activity and dyspnea, as well as bizarre QRS complexes on the ECG monitor, which is interpreted as a proarrhythmogenic effect. During resuscitation, the patient received several ampoules of adrenalin and atropine, and electroconversion was performed about 30 times due to ventricular fibrillation. Over the next two days, the clinical condition gradually stabilizes, after which the patient becomes agitated and states that she will attempt suicide again, and a psychiatrist is consulted and a transfer to the Psychiatric Clinic is indicated. [ABSTRACT FROM AUTHOR]

Abstract Background and Aim Prolonging the QT interval in the right bundle branch block (RBBB) can create challenges for electrophysiologists in estimating repolarization time and eliminating the effect of depolarization changes on QT interval. In this study, we aimed to develop a practice formula to eliminate the effect of depolarization changes on QT interval in patients with RBBB. Methods This prospective study evaluated accidentally induced RBBB in patients undergoing electrophysiological study. Two expert electrophysiologists recorded the ECG parameters, including QRS duration, QT interval, and cycle length, in the patients. The formula was developed based on QT interval differences (with and without RBBB) and its proportion to QRS. Additionally, the Bazzet, Rautaharju, and Hodge formulas were used to evaluate QTc. Results We evaluated 96 patients in this study. The mean QT interval without RBBB was 369.39 ± 37.38, reaching 404.22 ± 39.23 after inducing RBBB. ΔQT was calculated as 34.83 ± 17.61, and the ratio of ΔQT/QRS with RBBB was almost 23%. Our formula is: (QTwith RBBB − 23% × QRS). Subtraction of 25% instead of 23% seems more straightforward and practical. Our formula could also predict the QTc interval in RBBB based on the Bazzet, Rautaharju, and Hodge formulas. Conclusion Previous formulas for QT correction were hard to apply in the clinical setting or were not specified for RBBB. Our new formula allows a rapid and practical method for QT correction in RBBB in clinical practice.

Background: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. Methods: In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. Results: Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. Conclusions: The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed. [ABSTRACT FROM AUTHOR]

Nowadays, a computer-aided diagnosis system is required to monitor the cardiac patients continuously and detecting the heart diseases automatically. In this paper, a new field programmable gate array-based morphological feature extraction approach is proposed for electrocardiogram signal analysis. The proposed architecture is mainly based on the Generalized Synchrosqueezing transform but a detrended fluctuation analyzer is applied in the reconstruction stage for capturing the maximum information of QRS complexes and P-waves by eliminating a set of noisy intrinsic modes. Then, a correntropy envelope is determined from the QRS enhanced signal for localizing the QRS region accurately. Also, an adaptive heuristic framework is introduced to detect the true P-wave from the P-wave enhanced reconstructed signal by analyzing both the positive and negative amplitudes. In addition, a root mean square Error estimation-based adaptive thresholding approach is used to estimate the T-wave after removing the P-QRS complexes. The proposed architecture has been implemented on field programmable gate array using the Xilinx Vertex 7 platform. The performance of the proposed architecture is validated by performing a comparative study between the resultant performances and those attained with state-of-the-art feature descriptors, in terms of Sensitivity, accuracy, positive prediction, error rate and field programmable gate array resources estimation. The proposed sensitivity, accuracy and positive prediction are 99.84%, 99.85% and 99.86% for QRS detection approach. The proposed sensitivity, accuracy and positive prediction are 99.45%, 99.23% and 99.78% for P-wave detection approach. The proposed sensitivity, accuracy and positive prediction are 99.58%, 99.65% and 100% for T-wave detection approach. The simulation results show that the proposed architecture overtakes existing designs and minimizes hardware complexity, which proves the suitability of this approach on real-time applications of electrocardiogram signals. [ABSTRACT FROM AUTHOR]

Objective To explore the correlation between ECG QRS amplitude parameters, and blood pressure and left ventricular hypertrophy ( LVH) in patients with essential hypertension with left ventricular hypertrophy (EH-LVH). Methods A total of 126 patients with EH-LVH were enrolled as research objects. They were divided into mild group (n = 51), moderate group ( n = 47) and severe group ( n = 28) according to the degree of LVH. We compared the blood pressure, LVH indexes [ left ventricular posterior wall thickness ( LVPWT), interventricular septal thickness ( IVST) and left ventricular mass index ( LVMI) ], and ECG QRS amplitude parameters [SVl + RV5 amplitude, the sum of QRS amplitudes of all leads (12∑QRSWA), RⅠ + SⅢ amplitude] among the three groups. The correlation between QRS amplitude parameters, and blood pressure and LVH indexes was analyzed while the relationship between QRS amplitude parameters and the degree of LVH was also analyzed. Results Systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the severe group are significantly higher than those in the mild and moderate groups while these parameters in the moderate group are also significantly higher than those in the mild group (P < 0. 05). The LVPWT, IVST and LVMI values of the severe group are significantly higher than those of the mild and moderate groups while the above indexes of the moderate group are also significantly higher than those of the mild group (P < 0. 05). The RⅠ + SⅢ amplitude, 12∑QRSWA and SVl + RV5 amplitude in the severe group are all significantly lower than those in the mild and moderate groups while these amplitudes of the moderate group are also significantly lower than those in the mild group (P < 0. 05). Pearson correlation analysis shows that the RⅠ + SⅢ amplitude, 12∑QRSWA and SVl+ RV5 amplitude are all negatively correlated with DBP, SBP, LVPWT, IVST and LVMI (P < 0. 05). The RⅠ + SⅢ amplitude, 12∑QRSWA and SVl + RV5 amplitude are all correlated with the degree of LVH (P < 0. 05). Conclusion The QRS complex in ECGs of EH-LVH patients are obviously abnormal, presenting as the decrease of the RⅠ + SⅢ amplitude, 12∑QRSWA and SVl + RV5 amplitude, which are closely related to blood pressure and LVH indexes. The degree of LVH could be evaluated by measuring QRS complex in the ECG of EH-LVH patients. [ABSTRACT FROM AUTHOR]

Myxomatous mitral valve disease (MMVD) is the most common cardiac disease in dogs. The association of QRS notching (nQRS) or fragmentation (fQRS) with disease severity is currently unknown. The study objective was to assess the prevalence of nQRS and fQRS in dogs with MMVD and its severity according to ACVIM classification and to compare the results with a group of healthy dogs. This retrospective cross-sectional study included 34 healthy control dogs and 155 dogs with spontaneous MMVD (42% of dogs in class B1, 23% in class B2 and 35% in class C). fQRS was defined as nQRS complexes in two contiguous leads in the frontal plane (leads I and aVL) and (II, III or aVF). A one-way ANOVA with Bonferroni post-hoc test was used to assess the differences in continuous data between control and MMVD groups. Of the MMVD group, 58% showed nQRS in at least one lead and 27% presented fQRS. There was no difference between the number of leads with a nQRS and disease severity (p = 0.75) nor did the number of leads with a nQRS correlate with left atrial size (r = 0.48; p = 0.5). The number of dogs with fQRS did not differ among classes of MMVD (p = 0.21). nQRS and fQRS were more prevalent in dogs with MMVD compared to control dogs (p

Aims Fragmented QRS complex with visible notching on standard 12-lead electrocardiogram (ECG) is understood to represent depolarization abnormalities and to signify risk of cardiac events. Depolarization abnormalities with similar prognostic implications likely exist beyond visual recognition but no technology is presently suitable for quantification of such invisible ECG abnormalities. We present such a technology. Methods and results A signal processing method projects all ECG leads of the QRS complex into optimized three perpendicular dimensions, reconstructs the ECG back from this three-dimensional projection, and quantifies the difference (QRS 'micro'-fragmentation, QRS- μf) between the original and reconstructed signals. QRS 'micro'-fragmentation was assessed in three different populations: cardiac patients with automatic implantable cardioverter-defibrillators, cardiac patients with severe abnormalities, and general public. The predictive value of QRS- μf for mortality was investigated both univariably and in multivariable comparisons with other risk factors including visible QRS 'macro'-fragmentation, QRS- Mf. The analysis was made in a total of 7779 subjects of whom 504 have not survived the first 5 years of follow-up. In all three populations, QRS- μf was strongly predictive of survival (P < 0.001 univariably, and P < 0.001 to P = 0.024 in multivariable regression analyses). A similar strong association with outcome was found when dichotomizing QRS- μf prospectively at 3.5%. When QRS- μf was used in multivariable analyses, QRS- Mf and QRS duration lost their predictive value. Conclusion In three populations with different clinical characteristics, QRS- μf was a powerful mortality risk factor independent of several previously established risk indices. Electrophysiologic abnormalities that contribute to increased QRS- μf values are likely responsible for the predictive power of visible QRS- Mf. [ABSTRACT FROM AUTHOR]

Objectives: We investigated the diagnostic accuracy of fragmented QRS (fQRS) numbers in surface electrocardiogram (ECG) in heart failure (HF) patients for prediction of cardiac resynchronization therapy (CRT) non-responders. Methodology: In this study, patients with HF who were candidates for CRT implantation were enrolled. A 12-lead surface electrocardiogram was conducted with the aim of finding fQRS. Presence of more than 2 notching in the R or S wave in wide QRS complexes in at least two adjacent leads corresponding to a coronary bed, considered as fQRS. All patients underwent transthoracic echocardiography 3-6 months after the CRT implantation for the evaluation of Left Ventricular End Diastolic Volume (LVEDV), Left Ventricular End Systolic Volume (LVESV), and Left ventricle ejection fraction (LVEF). The data were analyzed by SPSS v.22 software. P-value of less than 0.05 considered significant. Results: We investigated on a total of 73 patients with HF that 64.38% of them responded to CRT. Most of responders were patients without fQRS complexes (80%). LVEF, LVESV, and LVEDV were different significantly before and after CRT implantation in patients with fQRS (p<0.001). Our results showed that the presence of at least one fQRS can lead to nonresponsiveness to CRT. Therefore, we take cut-off point equal-greater than one into account as the best cut-off point for response to CRT. The area under the curve (AUC) for CRT prediction was 0.715 (95% CI: 0.598-0.815, P=0.003). Conclusion: In conclusion, the presence of even one fQRS in the surface ECG can predict CRT non-responsiveness with good accuracy. [ABSTRACT FROM AUTHOR]

Aim. To evaluate the dynamics of cardiac arrhythmias on the background of cardiac contractility modulation (MCC) in patients with chronic heart failure (CHF) and various forms of atrial fibrillation (AF) on the basis of daily electrocardiogram (ECG) monitoring. Materials and methods. In 100 patients with CHF and AF, the following studies were performed before implantation of the MCC device and after 12 months of follow-up: 12-channel ECG with an estimate of the width of the QRS complex, transthoracic echocardiography (EchoCG), and Holter ECG monitoring. All patients received long-term optimal drug therapy for CHF before surgery. Results. The results obtained indicate that there is no effect of MCC on the development and progression of ventricular arrhythmias in patients with CHF and AF during the year of follow-up, both extrasystole and tachyarrhythmias, regardless of the etiology and LVEF (less than 35% or more than 35%), and a decrease in the frequency of AF paroxysms in patients with CHF during treatment. These results are due to the reverse remodeling of the LV myocardium under the influence of the MCC device. Conclusion. The use of MСС in patients with CHF and AF is a safe method of therapy that does not induce cardiac arrhythmias, including ventricular extrasystole. Large-scale comparative studies are required to evaluate these results.

BackgroundAs a novel physiological pacing technique, left bundle branch pacing (LBBP) can preserve the left ventricular (LV) electrical and mechanical synchronization by directly capturing left bundle branch (LBB). Approximately 60–90% of LBBP were confirmed to have captured LBB during implantation, implying that up to one-third of LBBP is actually left ventricular septal pacing (LVSP). LBB capture is critical for distinguishing LBBP from LVSP.Methods and resultsA total of 15 articles were included in the analysis by searching PubMed, EMBASE, Web of Science, and the Cochrane Library database till August 2022. Comparisons of paced QRS duration between LVSP and LBBP have not been uniformly concluded, but the stimulus artifact to LV activation time in lead V5 or V6 (Stim-LVAT) was shorter in LBBP than LVSP in all studies. Stim-LVAT was used to determine LBB capture with a sensitivity of 76–95.2% and specificity of 78.8–100%, which varied across patient populations.ConclusionThe output-dependent QRS transition from non-selective LBBP to selective LBBP or LVSP is direct evidence of LBB capture. LBB potential combined with short Stim-LVAT can predict LBB capture better. Personalized criteria rather than a fixed value of Stim-LVAT are necessary to confirm LBB capture in different populations, especially in patients with LBB block or heart failure.

ObjectiveTo investigate the clinical characteristics of patients with low QRS voltage in precordial leads of routine 12-lead ECG. MethodsRetrospective analysis was performed on 131 patients examined by routine 12-lead ECG and diagnosed with low voltage in precordial leads during hospitalization. We observed their clinical characteristics. Results Among the 131 patients, there are 65 cases (49.62%) with low voltage in all precordial leads, and 52 cases (39.69%) with low voltage in all limb leads. Coronary heart disease complicated by cardiac insufficiency is the most common underlying disease (26 cases, accounting for 19.85%), followed by malignant tumor (23 cases, accounting for 17.56%) among which lung cancer accounts for the largest proportion (9 cases, accounting for 6.87%). A total of 45 cases (34.35%) are accompanied with anemia and 40 cases (30.53%) are complicated by hypoproteinemia. The average level of blood hemoglobin and blood albumin are both below the normal range. Conclusion Coronary heart disease complicating cardiac insufficiency is the most common underlying disease of patients with low QRS voltage in precordial leads of routine 12-lead ECG, and most of these patients have complications affecting QRS voltage. [ABSTRACT FROM AUTHOR]

Left bundle branch block (LBBB) is a common disorder in the heart's electrical conduction system that leads to the ventricles' uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Therefore, early automated detection is vital. This work aimed to detect the LBBB through the QRS electrocardiogram (ECG) complex segments taken from the MIT-BIH arrhythmia database. The used data contain 2655 LBBB (abnormal) and 1470 normal signals (i.e., 4125 total signals). The proposed method was employed in the following steps: (i) QRS segmentation and filtration, (ii) application of the Maximal Overlapped Discrete Wavelet Transform (MODWT) on the ECG R wave, (iii) selection of the detailed coefficients of the MODWT (D2, D3, D4), kurtosis, and skewness as extracted features to be fed into the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The obtained results proved that the proposed method performed well based on the achieved sensitivity, specificity, and classification accuracies of 99.81%, 100%, and 99.88%, respectively (F-Score is equal to 0.9990). Our results showed that the proposed method was robust and effective and could be used in real clinical situations. [ABSTRACT FROM AUTHOR]

The aim of the study was to compare the heart rate, QRS interval, and R wave amplitude across three electrocardiogram models, and assess the ability of each of them to provide electrocardiograms (ECG) for clinical interpretation. The three electrocardiogram models included ECG Seiva Praktik Veterinary, CardioStore ECG and AliveCor Veterinary Heart Monitor. The data were collected from twelve healthy adult captive green iguanas (Iguana iguana) monitored under a manual restraint at a room temperature of 22.6-28.0 °C. The ECGs using the Seiva Praktik and CardioStore ECG veterinary electrocardiography were performed with standard 4 lead ECG recordings. The AliveCor Veterinary Heart Monitor was placed (with the use of gel) directly on the lateral body wall. The mean heart rate was 42 ± 8 beats/min (CardioStore), 50 ± 11 beats/min (Seiva Praktik Veterinary), and 51 ± 9 beats/min (AliveCor Veterinary Heart Monitor). No significant difference in the heart rate was observed. A significant difference (P < 0.05) in the QRS duration was observed between the CardioStore and AliveCor Veterinary Heart Monitor. Significant differences (P < 0.01) in the R wave amplitude were detected between the CardioStore and AliveCor Veterinary Heart Monitor and between the Seiva Praktik Veterinary and AliveCor Veterinary Heart Monitor. The ECGs produced by the Seiva Praktik VVeeterinary and CardioStore machines were interpretable at 100%, while those produced by the AliveCor Veterinary Heart Monitor were interpretable at 66%. Seiva Praktik Veterinary is most appropriately used as an anaesthesia monitoring tool. AliveCor Veterinary Heart Monitor could be used as an additional diagnostic tool, but the results should be ideally confirmed with a standard ECG machine. Seiva Praktik Veterinary is the most appropriate tool for monitoring the ECG within the anaesthesia, while CardioStore might be most appropriately used as an advanced diagnostic tool by virtue of its software assistance. The ECGs obtained with AliveCor Veterinary Heart Monitor should be confirmed using a standard ECG machine.

QRS detection is a crucial task for ECG signal analysis, which is the preliminary and essential step to further recognition and diagnosis. This paper proposes a U-Net based method for QRS detection. The method consists of three steps including preprocessing, U-Net model, and density-based spatial clustering of applications with noise(DBSCAN). The normalization is carried out using the Z-score method in preprocessing. In this study, location prediction is conducted by the U-Net model. Subsequently, the U-Net outputs are thresholded and clustered by DBSCAN. Finally, the middle points of the cluster are regards as the R-peak of the QRS complex. We demonstrate that the proposed method achieving high accuracy on ECG signals from the MIT-BIH Arrhythmia Database(MITDB). The experimental results show an average sensitivity of 99.98 %, positive predictivity of 99.95 %, accuracy of 99.93 %, and F1-score of 99.97 %. Compared with other existing methods, the overall performance is comparable and even better in terms of accuracy and F1-score. [ABSTRACT FROM AUTHOR]

Background: Paradoxical ventriculophasic response (P-VR), a rare entity, has not been fully investigated. This study sought to compare the electrocardiographic features of P-VR and typical ventriculophasic response (T-VR).Methods: The 12‑lead electrocardiogram (ECG) data recorded before implantation of a cardiac implantable electronic device were analyzed in patients with greater than second-degree atrioventricular block (AVB). P-VR or T-VR was defined as present if the PP interval interposing a QRS complex was prolonged or shortened, respectively, by >3% compared with the preceding PP interval without a QRS complex when a QRS complex occurred within a span of 60% of the preceding PP interval.Results: Of 95 patients (age 80 ± 9 years; 49 men) with heart block, 1868 instances (an instance was defined as a set of PP intervals without a QRS complex and the subsequent PP interval interposing a QRS complex) from 214 ECGs were analyzed: 894 instances from 122 ECGs in 64 patients with complete AVB (cAVB) and 974 instances from 92 ECGs in 43 patients with 2:1 AVB (12 showed both cAVB and 2:1 AVB). P-VR was observed in 48 patients (51%). The position of the interposed QRS complex relative to the preceding PP interval was earlier in P-VR than in T-VR. The PP interval was shorter in P-VR than in T-VR.Conclusion: P-VR was present in >50% of patients and was affected by the position of the interposed QRS complex and the PP interval. [ABSTRACT FROM AUTHOR]

Applying a steep pulse voltage of appropriate amplitude to a cell membrane can induce transient and reversible breakdown of the membrane, which has broad application prospects in biomedicine and clinical fields. However, the noise generated by the steep pulse seriously interferes with a patient’s electrocardiogram (ECG) signal resulting in decrease in the accuracy of the ECG feature point detection algorithm. Thus, doctors are unable to understand the state of the patient during treatment, thus limiting complete benefits of the therapy. To eliminate the interference caused by the steep pulse, we analyzed the characteristics of steep pulse interference and established the mathematical model of steep pulse noise. Moreover, we proposed an ECG signal filtering algorithm based on variational mode decomposition (VMD) to extract the steep pulse interference component superimposed on the ECG signal. The proposed algorithm could identify and eliminate the steep pulse interference component. We also designed an ECG signal preprocessing algorithm to reduce the decomposition layer of the VMD algorithm, which improved the real-time performance and reduced the memory consumption. To identify the random noise in the medical environment accompanied by the occurrence of steep pulses, we analyzed the characteristics of random noise in the sub-signal after VMD. Further, we proposed a threshold denoising algorithm based on VMD for sub-signal energy estimation. On the basis of the characteristics of a band-pass filter bank with VMD, we proposed a QRS complex detection algorithm based on VMD sub-signal recombination. Combined with the filtering algorithm, the proposed algorithm was able to improve the accuracy of ECG signal detection. By conducting experiments on ECG signals from the MIT–BIH database with Gaussian white noise and simulated steep pulse interference and those collected in the medical environment, we compared and analyzed the filtering algorithm and QRS complex detection algorithm.

Left bundle branch pacing (LBBP) is a physiological pacing technique that captures the left bundle branch (LBB) directly, causing the left ventricle (LV) to be excited earlier than the right ventricle (RV), resulting in a “iatrogenic” right bundle branch block (RBBB) pacing pattern. Several studies have recently shown that permanent LBBP can completely or partially narrow the wide QRS duration of the intrinsic RBBB in most patients with bradycardia, although the mechanisms by which this occurs has not been thoroughly investigated. This article presents a review of the LBBP in patients with intrinsic RBBB mentioned in current case reports and clinical studies, discussing the technique, possible mechanisms, future clinical explorations, and the feasibility of eliminating the interventricular dyssynchronization accompanied with LBBP.