Features Of QRS Complex Research Articles

The Neonatal QRS Complex and Its Association with Left Ventricular Mass

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.

Quantification of heart failure decompensation through mixed digital methodology

Abstract Background While natriuretic peptides (NPs) quantification and echocardiographic assessments are recommended for diagnosis of heart failure (HF), yet electrocardiographic determinants of cardiac rhythm alterations predisposing to sudden cardiac death (SCD) are lacking. Premature ventricular contractions (PVCs) are common in HF. In some patients, PVCs are triggered by the same mechanism that gives rise to ventricular tachycardia (VT). Appropriate clinical evaluation is important in these patients. Currently, smartwatches are becoming particularly popular to enhance health monitoring. Purpose The aims of this study were: to evaluate the feasibility and reliability of the clinical use of a smartwatch, to detect main features of QRS-complex (width, voltage), and to correlate PVCs with VT originating in different aetiologies of HF. Methods From November ‘22 to February ’23, 53 patients admitted to the Division of Cardiology of our academic hospital underwent standard ECG and smartwatch-based lead I acquisitions. Concordance between the two technologies was assessed by Cohen κ coefficient. Bland-Altman method for analysis of measurement agreement was used for comparison. Results Clinical characteristics of the subjects included in this study are described in the Table. All patients had diagnosis of acute or chronic HF and had PVCs (N= 47) and/or VT (N=3). Sustained and non-sustained VT were more common in patients with HF and reduced ejection fraction. In 7 cases we detected PVCs with the same standard ECG’s morphology and axis during recording ECG with the smartwatch. The Cohen κ coefficients for the measurement of the voltage and width of QRS complex were 0.833 (95% CI, 0.725-0.936) and 0.869 (95% CI, 0.747- 0.956), respectively; voltage and width of PVCs were 0.860 (95% CI, 0.579-1.00) and 0.822 (95% CI, 0.429-1.00), respectively. Concordance was found between the smartwatch ECG and standard ECG for QRS width (bias, −0.05; SD, 0.395; lower limit, −0.821; and upper limit, 0.727), for QRS voltage (bias, −0.004; SD, 0.034; lower limit, −0.073; and upper limit, 0.062), for PVC width (bias, −0.7; SD, 2.167; lower limit, −4.372; and upper limit, 4.12), PVC voltage (bias, −0.02; SD, 0.07; lower limit, -0.112; and upper limit, 0.162) using the Bland-Altman analysis. Conclusions Smartwatch can accurately measure QRS width and voltage and reproducibly detect PVCs compared with standard ECG. Valid correlation indices and good concordance between the two methods were observed using digitally-assessed lead I. These data could be of great interest due to the widespread diffusion of wearable devices; the smartwatch could be helpful especially when frequent monitoring of VT becomes necessary. Currently available smartwatches display, in this regard, an implementation for PVCs detection.

A Linearly Adaptive Sine–Cosine Algorithm with Application in Deep Neural Network for Feature Optimization in Arrhythmia Classification using ECG Signals

This work proposes a novel variant namely Linear Adaptive Sine–Cosine Algorithm (LA-SCA) of newly developed metaheuristic called Sine–Cosine Algorithm (SCA). The developed variant utilizes a linearly adaptive operator that is based on the number of generations, followed by an Opposition based Learning (OBL) model applied around the best solution. The applied mechanisms help LA-SCA to overcome the slow convergence problem in SCA and capable of achieving appropriate balance between macro and micro search ability of SCA. The performance of proposed LA-SCA is evaluated using a set of 23 state-of-the-art benchmark problems having a variety of characteristics and diverse complexities. The LA-SCA is statistically analysed by applying a Wilcoxon ranksum test and performance analysis using convergence curves and diversity analysis. In second part, the proposed LA-SCA is utilized to offer an intelligent Deep Neural Network (DNN) mechanism for improving the feature extraction of ECG signal for efficient classification of arrhythmia diseases. During the pre-processing step, Discrete Wavelet Transformation (DWT) is utilized to categorize ECG data followed by improving the extraction of QRS complex features using proposed LA-SCA. The MIT-BIH Arrhythmia ECG dataset is used in order to perform classification of signals among sixteen categories of arrhythmia disease. By using the classification analysis on the basis of variation including optimized feature vector and the LA-SCA method with DNN, the heart rate has been observed with an improved 99.29 percent accuracy as compared to other techniques. This achieved accuracy demonstrated the superiority of proposed model for appropriately classifying the ECG signal.

R-R Interval Estimation for Wearable Electrocardiogram Based on Single Complex Wavelet Filtering and Morphology-Based Peak Selection

Recent innovations in wearable electrocardiogram (ECG) devices have enabled various personal healthcare applications based on heart rate variability (HRV). However, wearable ECGs rarely undergo visual inspection by medical experts, hence may contain noise and artifacts. Because apparent changes in the recorded ECGs caused by noise and artifacts may hamper the extraction of QRS complexes, an R-R interval (RRI) estimation algorithm tolerant to these measurement faults is required as the initial step toward HRV analysis using wearable ECGs. This paper proposes a semi-real-time RRI estimation for wearable ECGs utilizing a two-stage structure. In the preprocessing stage, we use a complex-valued wavelet that can adaptively fit to morphological variations of the QRS complex while retaining computing resources for extracting the QRS complex features. In the decision stage, we make use of complex-valued features and select appropriate QRS complexes in consideration of three features: peak magnitude, peak location, and peak morphology (phase). Initial evaluations show that the QRS complex detection performance of the proposed method achieved the F1 score of 0.952 ± 0.040 when targeting pseudo ECG data created from open data assuming wearable ECGs, and of 0.986 ± 0.018 when targeting actual ECG data recorded by a shirt-type wearable ECG device during an exercise activity. Furthermore, the proposed method was able to suppress overlook or misdetection of QRS complexes, so the obtained RRIs are closer to the reference RRIs. The proposed method therefore contributes to achieving accurate HRV analysis using wearable ECGs in terms of obtaining accurate RRIs.

Removal of Power Line Interference From ECG Signals Using Adaptive Notch Filters of Sharp Resolution

The noise cancellation in electrocardiogram (ECG) signal is very influential to distinguish the essential signal features masked by noises. The power line interference (PLI) is the main source of noise in most of bio-electric signals. Digital notch filters can be used to suppress the PLI in ECG signals. However, the problems of transient interferences and the ringing effect occur, especially when the digitization of PLI does not meet the condition of full period sampling. In this paper, to obtain a better cancellation of the PLI, a designing approach, generating adaptive notch filter (ANF) of sharp resolution, is proposed. The proposed method is concise in algorithm and achieves a more comprehensive reduction of the PLI. It only requires on one fast Fourier transform on the input signal. The spectrum correction method, based on the information from the FFT spectrum of the corrupted signal, is utilized to estimate the harmonic parameters of the PLI. The information of a few main lobe spectral bins in the FFT spectrum is merged such that a compensation signal can be synthesized. By subtracting the compensational signal from the original measurement, the PLI within the investigated signal can substantially reduced. A distinguished advantage of the proposed ANF lies in the fact that no parameters are required to be specified, making the algorithm easier to be implemented. The proposed ANF outperforms conventional notch filters because it not only alleviates the undesirable effects but also better preserves the QRS-complex features in the filtered signal.

Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study

Patient-specific models of the ventricular myocardium, combined with the computational power to run rapid simulations, are approaching the level where they could be used for personalized cardiovascular medicine. A major remaining challenge is determining model parameters from available patient data, especially for models of the Purkinje-myocardial junctions (PMJs): the sites of initial ventricular electrical activation. There are no non-invasive methods for localizing PMJs in patients, and the relationship between the standard clinical ECG and PMJ model parameters is underexplored. Thus, this study aimed to determine the sensitivity of the QRS complex of the ECG to the anatomical location and regional number of PMJs. The QRS complex was simulated using an image-based human torso and biventricular model, and cardiac electrophysiology was simulated using Cardioid. The PMJs were modeled as discrete current injection stimuli, and the location and number of stimuli were varied within initial activation regions based on published experiments. Results indicate that the QRS complex features were most sensitive to the presence or absence of four “seed” stimuli, and adjusting locations of nearby “regional” stimuli provided finer tuning. Decreasing number of regional stimuli by an order of magnitude resulted in virtually no change in the QRS complex. Thus, a minimal 12-stimuli configuration was identified that resulted in physiological excitation, defined by QRS complex feature metrics and ventricular excitation pattern. Overall, the sensitivity results suggest that parameterizing PMJ location, rather than number, be given significantly higher priority in future studies creating personalized ventricular models from patient-derived ECGs.

QRS Complex Detection and Measurement Algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients.

We developed an automated approach for QRS complex detection and QRS duration (QRSd) measurement that can effectively analyze multichannel electrocardiograms (MECGs) acquired during abnormal conduction and pacing in heart failure and cardiac resynchronization therapy (CRT) patients to enable the use of MECGs to characterize cardiac activation in such patients. The algorithms use MECGs acquired with a custom 53-electrode investigational body surface mapping system and were validated using previously collected data from 58 CRT patients. An expert cohort analyzed the same data to determine algorithm accuracy and error. The algorithms: 1) detect QRS complexes; 2) identify complexes of the most prevalent morphology and morphologic outliers; and 3) determine the array-specific (i.e., anterior and posterior) and global QRS complex onsets, offsets, and durations for the detected complexes. The QRS complex detection algorithm had a positive predictivity and sensitivity of ≥96% for complex detection and classification. The absolute QRSd error was 17 ± 14 ms, or 12%, for array-specific QRSd and 12 ± 10 ms, or 8%, for global QRSd. The absolute global QRSd error (12 ms) was less than the interobserver variation in that measurement (15 ± 10 ms). The sensitivity, positive predictivity, and error of the algorithms were similar to the values reported for current state-of-the-art algorithms designed for and limited to simpler data sets and conduction patterns and within the variation found in clinical 12-lead ECG QRSd measurement techniques. These new algorithms permit accurate, real-time analysis of QRS complex features in MECGs in patients with conduction disorders and/or pacing.

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.

FPGA implementation of fractal patterns classifier for multiple cardiac arrhythmias detection

This paper proposes the fractal patterns classifier for multiple cardiac arrhythmias on field-programmable gate array (FPGA) device. Fractal dimension transformation (FDT) is employed to adjoin the fractal features of QRS-complex, including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic patterns, which can produce family functions and enhance features, making clear differences between normal and unhealthy subjects. The probabilistic neural network (PNN) is proposed for recognizing multiple cardiac arrhythmias. Numerical experiments verify the efficiency and higher accuracy with the software simulation in order to formulate the mathematical model logical circuits. FDT results in data self-similarity for the same arrhythmia category, the number of dataset requirement and PNN architecture can be reduced. Its simplified model can be easily embedded in the FPGA chip. The prototype classifier is tested using the MIT-BIH arrhythmia database, and the tests reveal its practicality for monitoring ECG signals.