A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN)that automatically learns the best features from the raw ECG signals. After that, the support vector machine (SVM) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the SVM classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.

A heart disease called cardiovascular diseases (CVD) is another leading cause for the death. There are several reasons that lead the CVD in human beings. The early detect of CVD helps to take necessary medical attentions to prevent the harms. The conventional techniques for CVD detection were manual and expensive which often delivers the inaccurate diagnosis. Since from the last decade the other inexpensive Computer Aided Diagnosis (CAD) based methods gained significant medical attentions. The CAD based techniques mainly based on raw Electro Cardiogram (ECG) signals of patient for the accurate and economical detection of CVD at early stage. In recent past, there are several CAD systems designed for CVD diagnosis utilizing raw ECG signals, however accuracy of CVD detection utilizing ECG bothered through several issues of research like QRS beats extraction, artefacts, efficient features extraction. This research paper present CVD novel framework, utilizing raw ECG signals and designed hybrid pre-processing algorithm for extracting artefacts and noise through raw ECG signal. Further designed simple and efficient dynamic thresholding based technique to extract the beats such as Q, R, S, and ST segment through pre-processing ECG signal. Third step perform the fusion of extracted beats and apply the feature extraction method called Normalized Higher Order Statistic (NHOS). The normalized HOS techniques asses the complexity among all the QRS based beats and delivers the more unique features for the accuracy enhancement. The final step is the classification by using five different classifiers for the CVD detection. The simulation results presented in this paper demonstrate that proposed framework achieved the significant accuracy improvement.

Multi-stream Bi-GRU network to extract a comprehensive feature set for ECG signal classification

This paper presents the analysis of heart diseases that are categorized as arrhythmia based on Electrocardiogram (ECG). ECG database of different disease conditions was analyzed. The ECG signals are filtered to remove noise which is caused due to powerline interface or Electromyogram. This filtered signal is segmented to smaller pieces of ECG so that feature extraction is accurate. The features extracted are Peak to peak Interval (R-R Interval), BPM (Beats per minute), P wave to QRS peak. The data set is classified using an SVM classifier algorithm. This algorithm classifies the input ECG signal with varying feature parameters to two different types of arrhythmia. This approach has achieved an accuracy of 91% and the performance regarding other criteria such as precision, recall and F1 score were significantly better indicating the success of the proposed method.

Objective. Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias. Approach. In this study, we propose a multi-branch signal fusion network (MBSF-Net) for multi-label classification of arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include Inception with depthwise separable convolution (DWS-Inception), spatial pyramid pooling (SPP) Layer, and multi-scale fusion Resnet (MSF-Resnet). The proposed method can extract features from each lead of 12-lead ECG recordings separately and then effectively fuse the features of each lead by integrating multiple convolution kernels with different receptive fields, which can achieve the information of complementation between different angles of the ECG signal. In particular, our model can accept 12-lead ECG signals of arbitrary length. Main results. The experimental results show that our model achieved an overall classification F1 score of 83.8% in the 12-lead ECG data of CPSC-2018. In addition, the F1 score of the MBSF-Net performed best among the MBF-Nets which are removed the SPP layer from MBSF-Net. In comparison with the latest ECG classification algorithms, the proposed model can be applied in varied-length signals and has an excellent performance, which not only can fully retain the integrity of the original signals, but also eliminates the cropping/padding signal beforehand when dealing with varied-length signal database. Significance. MBSF-Net provides an end-to-end multi-label classification model with outperfom performance, which allows detection of disease in varied-length signals without any additional cropping/padding. Moreover, our research is beneficial to the development of computer-aided diagnosis.

Over the last few years, various researches have reached the conclusion that cosmic ray variations and geomagnetic disturbances are related to the condition of the human physiological state. In this study, medical data concerning the number of incidents of different types of cardiac arrhythmias for the time period 1983–1992, which refer to 1902 patients in Tbilisi, Georgia, were used. The smoothing method and the Pearson r-coefficients were used to examine the possible effect of different solar and geomagnetic activity parameters and cosmic ray intensity variations on the different types of arrhythmias. The time interval under examination was separated into two different time periods, which coincided with the polarity reversal of the solar magnetic field occurred in the years 1989–1990, and as a result, a different behavior of all the above-mentioned parameters as well as of the different types of arrhythmias was noticed during the two time intervals. In addition, changing of polarity sign of the solar magnetic field was found to affect the sign of correlation between the incidence of arrhythmias and the aforementioned parameters. The primary and secondary maxima observed in the solar parameters during the solar cycle 22, also appeared in several types of arrhythmias with a time lag of about 5 months.

Any significant alteration in the Electro-Cardio-Gram (ECG) signal wave components (P-QRS-T) for a time duration is detected as arrhythmia. In this paper, a novel fractional wavelet transform (FrWT) is used as a preprocessing tool. FrWT describes the given signal in time–frequency fractional domain using fractional Fourier transform and its denoising using wavelet transform. Because of this novel and intriguing property, it is broadly utilized as a noise removal tool in the fractional domain along with multiresolution analysis. Next, features are extracted using Yule–Walker autoregressive modeling. Dimensionality of the extracted features is to be reduced for proper detection of different types of arrhythmias. Principal component analysis has been applied for arrhythmia detection using variance estimation. The proposed method is evaluated on the basis of various performance parameters such as output SNR, mean squared error (MSE) and detection accuracy ( $$ {\text{DE}}_{\text{Acc}} $$ ). An output SNR of 33.41 dB, MSE of 0.1689% and Acc of 99.94% for real-time ECG database and output SNR of 25.25 dB, MSE of 0.1656%, $$ {\text{DE}}_{\text{Acc}} $$ of 99.89% for MIT-BIH Arrhythmia database are obtained.

Electrocardiogram (ECG)-based arrhythmia classification is vital for the early detection and diagnosis of heart diseases. However, raw ECG signals are often noisy, which poses challenges for accurate classification. In this paper, we propose a novel approach that integrates discrete wavelet transform (DWT) for signal denoising with an Attention-Enhanced Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for arrhythmia classification. Initially, DWT is applied to the ECG signal to remove noise while preserving important features. To address class imbalance, Borderline-SMOTE is employed to generate synthetic samples for the minority classes. The denoised and balanced signals are then processed through a CNN for feature extraction, followed by a BiGRU to capture temporal dependencies. By incorporating an attention mechanism, the model is able to concentrate on the most critical regions of the ECG signal. The proposed method is evaluated on the MIT-BIH arrhythmia database, achieving an accuracy of 99.22% in classifying five types of arrhythmias, outperforming previous models. This approach provides a promising solution for automatic arrhythmia detection in clinical practice.

Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQIp), skewness (SQIskew), signal-to-noise ratio (SQIsnr), higher order statistics SQI (SQIhos) and peakedness of kurtosis (SQIkur). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.

Detection and treatment of arrhythmias has become one of the main goals in cardiac care diagnosis provided by general practitioners. Electrocardiogram (ECG) analysis is one of the most commonly used tools to test and diagnose heart problems. Classification of ECG heartbeats enables the identification of specific arrhythmia or other heart conditions. This paper presents and contrasts the results from two effective ECG arrhythmia classification schemes. The first scheme consists of a principal component analysis (PCA) step for feature reduction at the input vector to the classifier, combined with soft independent modelling of class analogy (SIMCA). The second method uses a multi-class support vector machine (MSVM) classifier to differentiate between four different types of arrhythmia from ECG beats. The four types of beats include Normal (N), Premature Ventricular Contraction (PVC), and Atrial premature contraction (APC) and Right Bundle Branch Block Beat (RBBB). The time domain features were obtained from the St Petersburg INCART 12-lead Arrhythmia Database (incartdb). Between 10 and 30 Principal Components (PCs) were selected for reconstructing individual ECG beats and create the input vector to the classifier. The average classification accuracy of the proposed scheme is 76.83% and 98.33% using MSVM and SIMCA classifier respectively. The SIMCA classification algorithm provided better performance than the MSVM classifiers.

Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.

A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification

This paper presents a resource-saving system to extract a few important features of electrocardiogram (ECG) signals. In addition, real-time classifiers are proposed as well to classify different types of arrhythmias via these features. The proposed feature extraction system is based on two delta-sigma modulators adopting 250Hz sampling rate and three wave detection algorithms to analyze outputs of the modulators. It extracts essential details of each heartbeat, and the details are encoded into 68 bits data that is only 1.48% of the other comparable methods. To evaluate our classification, we use a novel patient-specific training protocol in conjunction with the MIT-BIH database and the recommendation of the AAMI to train the classifiers. The classifiers are random forests that are designed to recognize two major types of arrhythmias. They are supraventricular ectopic beats (SVEB) and ventricular ectopic beats (VEB). The performance of the arrhythmia classification reaches to the F1 scores of 81.05% for SVEB and 97.07% for VEB, which are also comparable to the state-of-the-art methods. The method provides a reliable and accurate approach to analyze ECG signals. Additionally, it also possesses time-efficient, low-complexity, and low-memory-usage advantages. Benefiting from these advantages, the method can be applied to practical ECG applications, especially wearable healthcare devices and implanted medical devices, for wave detection and arrhythmia classification.

This paper discusses the circuit comparison of the electrocardiogram (ECG) heart rate detector for wearable biomedical devices. In this work, the QRS complex is used to calculate heart rate, representing the main component of the ECG signal. In order to achieve a high level of accuracy by the detector, the measured ECG signal must be free of noise. Typically, such noise originates from power line interferences and baseline wandering. To eliminate these noises, the ECG signal must be processed using the signal processing method. The ECG detection process can be divided into a preprocessing stage, which eliminates noise from the raw ECG signal, and the feature extraction stage, which extracts the QRS complex for heart rate calculation. Based on our analysis, the trade-off between complexity and accuracy in ECG signal architecture is important when designing a high-efficiency system.

The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.