Premature Ventricular Contraction Detection Research Articles

Design of a low-area hardware architecture to predict early signs of sudden cardiac arrests

Sudden cardiac arrest (SCA) results in an unexpected and untimely death within minutes, and its early prediction can alert cardiac patients to a timely medical diagnosis. To detect early symptoms of an SCA, the detection and classification of ventricular tachycardias (VT) are of utmost importance. In this work, a low-area yet highly accurate hardware architecture for VT classification is proposed based on the detection of premature ventricular contraction (PVC) beats. After pre-processing of the ECG signals using a wavelet-based pre-processing unit, a characteristics-matching algorithm is used to detect the PVC beats, and a low-complexity adaptive decision-based logic classifier is used to classify them into four types of VTs, namely monomorphic, polymorphic, non-sustained VT (NSVT), and sustained VT (SVT). FPGA verification of the hardware architecture for the VT classifier using the Nexys 4 DDR Artix-7 board utilizes 10.4 % of the total available resources and displays the type of VT and the number of PVCs detected to help in determining the severity of SCA and the need for medical attention. The ASIC implementation of the proposed PVC-based VT classification using the SCL 180 nm CMOS technology results in an area overhead of 0.02 mm2 and a power consumption of 3.47 μW for a high accuracy rate of 98.2 %. When compared to the existing CA detection systems for wearable devices, the proposed one consumes the least area while achieving high detection rates.

Prevalence of frequent premature ventricular contractions and nonsustained ventricular tachycardia in older women screened for atrial fibrillation in the Women’s Health Initiative

BackgroundFrequent premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) have been associated with cardiovascular disease and mortality. Their prevalence, especially in ambulatory populations, is understudied and limited by few female participants and the use of short-duration (24- to 48-hour) monitoring. ObjectiveThe objective of this study was to report the prevalence of frequent PVCs and NSVT in a community-based population of women likely to undergo electrocardiogram (ECG) screening by sequential patch monitoring. MethodsParticipants from the Women’s Health Initiative Strong and Healthy (WHISH) trial with no history of atrial fibrillation (AF) but 5-year predicted risk of incident AF ≥5% by CHARGE-AF score were randomly selected to undergo screening with 7-day ECG patch monitors at baseline, 6 months, and 12 months. Recordings were reviewed for PVCs and NSVT (>5 beats); data were analyzed with multivariate regression models. ResultsThere were 1067 participants who underwent ECG screening at baseline, 866 at 6 months, and 777 at 12 months. Frequent PVCs were found on at least 1 patch from 4.3% of participants, and 1 or more episodes of NSVT were found in 12 (1.1%) women. PVC frequency directly correlated with CHARGE-AF score and NSVT on any patch. Detection of frequent PVCs increased with sequential monitoring. ConclusionIn postmenopausal women at high risk for AF, frequent PVCs were relatively common (4.3%) and correlated with higher CHARGE-AF score. As strategies for AF screening continue to evolve, particularly in those individuals at high risk of AF, the prevalence of incidental ventricular arrhythmias is an important benchmark to guide clinical decision-making.

An effective premature ventricular contraction detection algorithm based on adaptive template matching and characteristic recognition

An effective premature ventricular contraction detection algorithm based on adaptive template matching and characteristic recognition

Efficient Premature Ventricular Contraction Detection Based on Network Dynamics Features

Efficient Premature Ventricular Contraction Detection Based on Network Dynamics Features

Second-Order Level-Crossing Sampling Analog to Digital Converter for Electrocardiogram Delineation and Premature Ventricular Contraction Detection.

This article presents an electrocardiogram (ECG) delineation and arrhythmia heartbeat detection system using a novel second-order level-crossing sampling analog to digital converter (ADC) for real-time data compression and feature extraction. The proposed system consists of the front-end integrated circuit of the data converter, the delineation algorithm, and the arrhythmia detection algorithm. Compared with conventional level-sampling ADCs, the proposed circuit updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has sloped sampling levels. The computing is done digitally and is implemented by modifying the digital control logic of a conventional Successive-approximation-register (SAR) ADC. The system separates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the digital value of the selected sampling points and the timestamp between the selected sampling points. The main advantages are data savings for the data converter and the following digital signal processing or communication circuits, which are ideal for low-power sensors. The test chip was fabricated using a 180 nm CMOS process. When sensing sparse signals such as ECG signals the proposed ADC achieves a compression factor of 8.33. The delineation algorithm uses a triangle filter method to locate the fiducial points and measures the intervals, slopes, and morphology of the QRS complex and the P/T waves. Those extracted features are then used in the arrhythmia heartbeat detection algorithm to identify Premature Ventricular Contraction (PVC). The overall performance of the system is evaluated using the MIT-BIH database and the QT database, which is also compared with the recently reported systems. The accuracy, sensitivity, specificity, PPV, and F1 score are 97.3%, 89.6%, 97.8%, 73.3%, and 0.81 for detecting PVC.

Novel PVC detection algorithm in an implantable cardiac monitor for longitudinal risk monitoring

Abstract Background Guideline therapy is recommended in patients with frequent premature ventricular contractions (PVCs). Longitudinal monitoring of the PVC burden can effectively guide treatment options, stratify patient risk, and manage HF progression. Implantable cardiac monitors (ICMs) may provide a suitable long-term diagnostic tool to monitor PVC burden and guide clinical therapy. Objective Quantify the performance of a novel QRS morphology and timing-based PVC detection algorithm as implemented for an ICM. Methods The algorithm utilizes self-adapting timing-based ectopy detection and dynamic measures of QRS morphology to identify unifocal and multifocal PVCs. The training dataset consisted of 200 sECG episodes recorded in implanted BIOMONITOR (BM) devices, collected from 48 patients. The algorithm was then evaluated on 227 sixty-second duration sECG episodes from 79 BM patients in the CERTITUDE registry. Each beat was adjudicated as normal, PVC, or PAC by at least two clinical experts. In a total of 22,433 beats, 756 true PVCs were assessed (3.4% PVC prevalence). Overall, patient averaged, and generalized estimating equation (GEE) estimates for sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated, as well as patient-specific PVC burden (PVCs/Total Beats). Results Patient-averaged PVC detection sensitivity was 73.8%, with a PPV of 89.2%, specificity of 99.6%, and NPV of 99.1%. Using a GEE model to adjust for multiple episodes per patient, the algorithm achieved detection sensitivity of 66.6±9.8% (95% CI) and PPV of 86.6±6.5%, with specificity of 99.5±0.4% and NPV of 99.0±0.5%. The main reasons for missed PVC detections were morphological similarities to normal beats, insufficient prematurity, or lack of a compensatory pause. Additionally, the algorithm faithfully represented the PVC burden, with a median difference of -0.24% between the PVC burden calculated by the detection algorithm and the adjudicated burden. Algorithm burden was highly correlated with the true PVC burden for each patient, with a Pearson correlation coefficient of 0.80 (p < 0.0001). Conclusions The PVC detection algorithm was highly specific, achieving a specificity of 99.6% and a sensitivity of 73.8%, and accurately representing the overall PVC burden for each patient. This PVC detection algorithm would provide a valuable clinical diagnostic marker to monitor PVC burden trends to guide therapy as well as assess the longitudinal risk of HF. Measurement of PVC burden combined with the 5.5-year longevity of the BIOMONITOR ICM could provide an innovative approach to optimize clinical management of patients.Example PVCs detected by the algorithm

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.

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module

Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in rare instances. However, frequent PVCs can lead to more serious arrhythmias, such as atrial fibrillation. Several PVC detection models have been proposed to enable early diagnosis of arrhythmias; however, they lack reliability and generalizability due to the variability of electrocardiograms across different settings and noise levels. Such weaknesses are known to aggravate with new data. Therefore, we present a deep learning model with a novel attention mechanism that can detect PVC accurately, even on unseen electrocardiograms with various noise levels. Our method, called the Denoise and Contrast Attention Module (DCAM), is a two-step process that denoises signals with a convolutional neural network (CNN) in the frequency domain and attends to differences. It focuses on differences in the morphologies and intervals of the remaining beats, mimicking how trained clinicians identify PVCs. Using three different encoder types, we evaluated 1D U-Net with DCAM on six external test datasets. The results showed that DCAM significantly improved the F1-score of PVC detection performance on all six external datasets and enhanced the performance of balancing both the sensitivity and precision of the models, demonstrating its robustness and generalization ability regardless of the encoder type. This demonstrates the need for a trainable denoising process before applying the attention mechanism. Our DCAM could contribute to the development of a reliable algorithm for cardiac arrhythmia detection under real clinical electrocardiograms.

Design of hardware-efficient PVC recognition and classification system for early detection of sudden cardiac arrests

Sudden cardiac arrest (SCA) is a critical cardiovascular condition that needs greater emphasis on its early detection because it occurs unexpectedly and is fatal within minutes. Nowadays, wearable healthcare technologies are preferred for continuous monitoring of a wide range of cardiovascular diseases. Additionally, the recognition of cardiac arrhythmias such as premature ventricular contraction (PVC) and non-sustained ventricular tachycardia (NSVT) by wearable healthcare devices can help in the early detection of SCAs. The biggest challenge in this regard for compactly sized wearable healthcare devices is to design a hardware-efficient yet highly accurate PVC detection and classification system. In this work, a novel and hardware-efficient PVC recognition and classification system capable of categorizing PVCs into five types of ventricular arrhythmias, including NSVT, for the early detection of SCAs is proposed. For PVC detection, lifting-based discrete wavelet transform (LDWT) is used to perform pre-processing and extract morphological electrocardiogram (ECG) features and the Teager energy operator. When compared to the state-of-the-art ventricular arrhythmias and PVC detection systems, the proposed system performs PVC beat recognition and classification with an improved accuracy and sensitivity of 98.29% and 98.64%, respectively, while utilizing only 4.36% of the total resources when implemented on the Nexys 4 DDR FPGA board.

Premature Ventricular Contraction Detection Based on Chebyshev Polynomials and K Nearest Neighbours Classifier

Premature Ventricular Contraction Detection Based on Chebyshev Polynomials and K Nearest Neighbours Classifier

An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal

The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.

Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques.

Detection of Premature Ventricular Contractions (PVC) is of crucial importance in the cardiology field, not only to improve the health system but also to reduce the workload of experts who analyze electrocardiograms (ECG) manually. PVC is a non-harmful common occurrence represented by extra heartbeats, whose diagnosis is not always easily identifiable, especially when done by long-term manual ECG analysis. In some cases, it may lead to disastrous consequences when associated with other pathologies. This work introduces an approach to identify PVCs using machine learning techniques without feature extraction and cross-validation techniques. In particular, a group of six classifiers has been used: Decision Tree, Random Forest, Long-Short Term Memory (LSTM), Bidirectional LSTM, ResNet-18, MobileNetv2, and ShuffleNet. Two types of experiments have been performed on data extracted from the MIT-BIH Arrhythmia database: (i) the original dataset and (ii) the balanced dataset. MobileNetv2 came in first in both experiments with high performance and promising results for PVCs’ final diagnosis. The final results showed 99.90% of accuracy in the first experiment and 99.00% in the second one, despite no feature detection techniques were used. The approach we used, which was focused on classification without using feature extraction and cross-validation techniques, allowed us to provide excellent performance and obtain better results. Finally, this research defines as first step toward understanding the explanations for deep learning models’ incorrect classifications.

A clinical study on Atrial Fibrillation, Premature Ventricular Contraction, and Premature Atrial Contraction screening based on an ECG deep learning model

It is still a challenge to develop an electrocardiography (ECG) interpreter based on ECG basic characteristics because of the uncertainty of ECG delineation. Based on the clinical investigation in this study, ECG devices generated interpretations of Atrial Fibrillation (AF), Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC) have high ratios of false-positive errors. An ECG interpretation gap exists between ECG devices and cardiologists. This study aimed to develop an ECG interpreter to improve the performance of AF, PVC, and PAC based on clinical ECGs. This study first adopted a deep learning model to delineate ECG features such as P, QRS, and T waves based on 1160 8–10-s lead I or lead II ECG signals from a clinically-used 12-lead ECG device whose ECG device interpretation is AF as a training dataset. Second, a sliding window with 3-RR intervals in length is applied to the raw ECG to examine the delineated features in the window, and the ECG interpretation is then determined based on the experiences of cardiologists. The results indicate the following: (1) This delineator achieves good performance on P-, QRS-, and T- wave delineation with a sensitivity/specificity of 0.94/0.98, 1.00/0.99, and 0.97/0.98, respectively, in 48 10-s test ECGs mixed with true-positive AF and false-positive AF ECGs. (2) As compared to ECG-device generated interpretations, the precision of the detection of AF, PVC, and PAC in this study was increased from 0.77 to 0.86, 0.76 to 0.84, and 0.82 to 0.87 in 188 10-s test ECGs. Finally, (3) the F1 measure, which is a measure of the accuracy of test data but takes false-positive and false-negative into account, on the detection of AF, PVC, and PAC were 0.92, 0.91, and 0.83, respectively. In conclusion, this study overcomes the difficulties of ECG P-wave discrimination between true-positive AF and false-positive AF which are not documented well in previous research and improves the precision of ECG devices’ interpretation. We believe that this study can facilitate clinical applications of ECG, and bridge the gap between machines’ ECG interpretation and cardiologists.

Effortless detection of premature ventricular contraction using computerized analysis of photoplethysmography signal

Effortless detection of premature ventricular contraction using computerized analysis of photoplethysmography signal

Detection of Premature Ventricular Contractions using 12-lead Dynamic ECG based on Squeeze-Excitation Residual Network

Premature ventricular contraction (PVC) is a very common arrhythmia that can originate in any part of the ventricle and is one of the important causes of sudden cardiac death. Timely and rapid detection of PVC on dynamic electrocardiogram (ECG) recording for patients with cardiovascular diseases is of great significance for clinical diagnosis. Furthermore, it can facilitate the planning and execution of radiofrequency ablation. But the dynamic ECGs can be easily contaminated by various noises and its morphological characteristics show significant variations for different patients. Though the deep learning methods achieved outstanding performance in ECG automatic recognition, there are still some limitations, such as overfitting, gradient disappearance or gradient explosion in deep networks. Therefore, a residual module is constructed using the squeeze-excitation method to alleviate the problems. A 20-layer squeeze-extraction residual network (SE-ResNet) containing multiple squeeze-extraction modules was designed for real-time PVC detection on 12-lead dynamic ECG. The algorithm was evaluated using the dynamic 12-lead ECGs in INCART database (168,379 heartbeats in total). The experimental results show that the test accuracy of the method proposed in this paper is 98.71%, and the specificity and sensitivity of PVC are 99.12% and 99.59%, respectively. Under the same dataset and experimental platform, the average recognition accuracy of our proposed method is increased by 0.73%, 1.55%, 2.9% and 1.65% compared with the results obtained by CNN, Inception, AlexNet and deep multilayer perceptron, respectively. The proposed scheme provides a new method for real-time detection of PVC on dynamic 12-lead ECGs. The experiment results show that the proposed method outperforms state-of-the-art methods, and has good potential for clinical applications.

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove.

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

Very Large-Scale Integration for Premature Ventricular Contraction Detection Using a Convolutional Neural Network

We propose a very large-scale integration (VLSI) chip for premature ventricular contraction (PVC) detection. The chip contains a convolutional neural network (CNN) for detecting the abnormal heartbeats associated with PVCs in 12-lead electrocardiogram signals. The proposed CNN comprises two convolutional layers and a fully connected layer; in testing, it achieved a high PVC detection accuracy of [Formula: see text]. Created by using a [Formula: see text]-[Formula: see text]m CMOS process, the developed chip consumes [Formula: see text] mW with a clock frequency of 50 MHz and gate count of [Formula: see text] K. Compared with the previously designed VLSI chips, the proposed CNN chip achieves higher accuracy in abnormal heartbeat detection.

Automated detection of premature ventricular contraction based on the improved gated recurrent unit network

Background and Objective: Premature ventricular contraction (PVC) is the common arrhythmia disease, affecting thousands of individuals worldwide. However, the traditional PVC detection is cumbersome by visually inspecting electrocardiogram (ECG) signals.Methods: In this work, we specially propose an improved gated recurrent unit (IGRU) by setting a scale parameter into existing bidirectional GRU (BGRU) model for PVC signals recognition, which is used to alleviate the problem of information redundancy in BGRU. To verify the effectiveness, IGRU model will be embedded into a convolutional network frame and existing GRU and BGRU models are employed as control groups for a fair comparison.Results: The results exhibit that the model attains better model performance than control groups and several state-of-the-art algorithms with the accuracy of 98.3% and 97.9% with the MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Besides, motivated from the waveform characteristics of ECG signals in PVC, the proposed model can provide certain physiological interpretability for physicians and researchers.Conclusions: To our knowledge, this is the first attempt to re-design the existing GRU network for ECG signals classification, thus exhibiting great application potentials especially in lightweight equipment such as mobile phone and camera.

Premature beats detection based on a novel convolutional neural network

Objective. Automatic detection of premature beats on long electrocardiogram (ECG) recordings is of great significance for clinical diagnosis. In this paper, we propose a novel deep learning model, the ECGDet, to detect premature beats, including premature ventricular contractions (PVCs) and supraventricular premature beats (SPBs) on single-lead long-term ECGs. Approach. The ECGDet is proposed based on a convolutional neural network and squeeze-and-excitation network. It outputs the probabilities that the ECG samples belong to a premature contraction. Non-max suppression was used to select the most appropriate locations for the premature beats. The ECGDet was trained and tested on the MIT-BIH arrhythmia database (MITDB) using a five-fold cross-validation approach. A novel loss calculation method was introduced in the model training process. Then it was tuned and further tested on the China Physiological Signal Challenge (2020) database (CPSCDB). Main results. The results showed that the average F1 value of PVC detection was 92.6%, while that of SPB detection was 72.2% on MITDB. The ECGDet bagged the 2nd place for PVC detection and ranked 7th place of SPB detection in the China Physiological Signal Challenge (2020). Significance. The proposed ECGDet can automatically detect premature heartbeats without manually extracting the features. This technique can be used for long-term ECG signal analysis and has potential for clinical applications.

Machine learning-data mining integrated approach for premature ventricular contraction prediction

Cardiac arrhythmias impose a significant burden on the healthcare environment due to the increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is a common form of cardiac arrhythmia which begins from the lower chamber of the heart, and frequent occurrence of PVC beat might lead to mortality. ECG signals are the noninvasive and primary tool used to identify the actual life threat related to the heart. Nowadays, in society, the computer-assisted technique reduces doctors' burden to evaluate heart disease and heart arrhythmia automatically. Regardless of well-equipped and well-developed health facilities that are available for monitoring the cardiac condition, the success stories are yet unsatisfactorily due to the complexity of the cardiac disorder. The most challenging part in ECG signal analysis is to extract the accurate features relevant to the arrhythmia for classification due to the inter-patient variation. There are many morphological changes present in the ECG signals. Hence, there is a gap in the usage of appropriate methods for the extraction of features and classification models, which reduce the biased diagnosis of PVC arrhythmia. To predict PVC arrhythmia accurately is a quite challenging task owing to (a) QRS negative (b) long compensatory pause (c) p-wave (d) biased diagnosis of PVC detection due to the small feature set. This study presents a new approach for PVC prediction using derived predictor variables from the electrocardiograph (ECG-MLII) signals: R–R wave interval, previous R–R wave interval, QRS duration, and verification of P-wave whether it is present or absent using threshold technique. We propose the machine learning-data mining MACDM integrated approach using five different models of multiple logistic regression and four classifiers, namely, Random Forest (RF), K-Nearest Neighbor (KNN), Support vector machine (SVM), and Naive Bayes (NB). The experiment was conducted on the public benchmark MIT-BIH-AR to evaluate the performance of our proposed MACDM technique. The multiple logistic regression models constructed as a function of all independent variables achieved an accuracy of 99.96%, sensitivity 98.9%, specificity 99.20%, PPV 99.25%, and Youden's index parameter 98.24%. Thus, it is proved that this computer-aided method helps our medical practitioners improve the efficiency of their services.