Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

Individual Identification Using ECG SignalsW

The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerged as a biometric recognition tool. Indeed, ECG signal changes from one person to another according to health status, heart geometry and anatomy among other factors. This paper forms a comparative study between different identification techniques and their performances. Previous works in this field referred to methodologies implementing either set of fiducial or set non-fiducial features. In this study we show a comparison of the same data using a fiducial feature set and a non-fiducial feature set based on statistical calculation of wavelet coefficient. High identification rates were measured in both cases, non-fiducial using Discrete Meyer (dmey) wavelet outperformed the rest at 98.65.

ANALYSIS OF AFFECTIVE ECG SIGNALS TOWARD EMOTION RECOGNITION

Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.

Efficient ECG classification based on Chi-square distance for arrhythmia detection

This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.

Progress on Fabric Electrodes Used in ECG Signals Monitoring

Wearable monitoring system is designed for skin stimulation of conductive adhesive, prolonged physiological monitoring and biocompatibility, whose core is fabric electrodes and it can feedback physiological status by analysis of abnormal electrocardiogram (ECG). Fabric electrode is a sensor to collect biological signals based on textile materials including signals acquisition, processing systems and information feedback platform and so on. In this paper, the design methods and classification of medical electrodes would be introduced. It also sorted out the principle of biological electrical signals, the design methods and characteristics of different material and different structure electrodes from the point of dry electrodes and wet electrodes. There are many methods that can be used to prepare fabric electrodes. They are mainly metal plating, conductive polymer coating, magnetron sputtering, gas phase deposition and impregnation. Besides, they select the appropriate substrate, conductive medium and composite way to get light fabric electrodes which have high conductivity, good conformability. From the perspective of biological signal acquisition by fabric electrodes, this paper also sorted out the influence and approaches of biological signals and the way to feedback the physiological condition of human. As a new generation of bio-signal acquisition material, fabric electrode has met the requirements of the development of modern medicine. Fabric electrode is different from traditional conductive materials in the characteristics of comfort, intelligence, convenience, accuracy and so on.

Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

This paper presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code Modulation (DPCM), and run-length coding techniques for the compression of different parts of the signal;where lossless compression is adopted in clinically relevant parts and lossy compression is used in those parts that are not clinically relevant. The proposed compression algorithm begins by segmenting the ECG signal into its main components (P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The resulting waves are grouped into Region of Interest (RoI) and Non Region of Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes are applied to the RoI and NonRoI parts respectively. Ideally we would like to compress the signal losslessly, but in many applications this is not an option. Thus, given a fixed bit budget, it makes sense to spend more bits to represent those parts of the signal that belong to a specific RoI and, thus, reconstruct them with higher fidelity, while allowing other parts to suffer larger distortion. For this purpose, the correlation between the successive samples of the RoI part is utilized by adopting DPCM approach. However the NonRoI part is compressed using DWT, thresholding and coding techniques. The wavelet transformation is used for concentrating the signal energy into a small number of transform coefficients. Compression is then achieved by selecting a subset of the most relevant coefficients which afterwards are efficiently coded. Illustrative examples are given to demonstrate thresholding based on energy packing efficiency strategy, coding of DWT coefficients and data packetizing. The performance of the proposed algorithm is tested in terms of the compression ratio and the PRD distortion metrics for the compression of 10 seconds of data extracted from records 100 and 117 of MIT-BIH database. The obtained results revealed that the proposed technique possesses higher compression ratios and lower PRD compared to the other wavelet transformation techniques. The principal advantages of the proposed approach are: 1) the deployment of different compression schemes to compress different ECG parts to reduce the correlation between consecutive signal samples;and 2) getting high compression ratios with acceptable reconstruction signal quality compared to the recently published results.

基于卷积神经网络的ECG心律失常分类研究

基于心电信号进行心律失常自动检测和分类识别研究,辅助临床医生进行心血管相关疾病诊断。采用MIT-BIH数据库作为数据源,对该数据库心电数据进行小波分解与重构去噪后,构建卷积神经网络模型,结合Adam优化器,并优化丢弃值、训练步数和批大小三个超参数来优化模型,使用准确率、灵敏性和正预测率三个指标评价模型性能。实验结果表明,模型实现心律失常五分类的整体准确率大于99%,与现有模型性能相比,准确率提升1.2%。

De-Noising of ECG Signals by Design of an Optimized Wavelet

In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.

以BP神经网络为工具的短时ECG信号情感分类

针对目前生理信号情感识别领域采用的生理信号种类太多或使用的生信号长度较长的问题,本文使用BP神经网络对单一、短时ECG信号进行情感识别分类,并对识别时间进行了估计。通过诱发被试喜、怒、哀、惧和平静5种基本情感状态,采集到ECG生理信号,处理后利用神经网络建立模型。实验结果表明,本文方法得到的情感分类的平均识别率为89.14%,且生理信号进行特征提取和识别分类的时间总和小于0.15s,有效地降低了对生理信号种类和窗口长度的依赖。

A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal

In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.

Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features

Diagnoses of heart diseases can be done effectively on long term recordings of ECG signals that preserve the signals’ morphologies. In these cases, the volume of the ECG data produced by the monitoring systems grows significantly. To make the mobile healthcare possible, the need for efficient ECG signal compression algorithms to store and/or transmit the signal efficiently has been rising exponentially. Currently, ECG signal is acquired at Nyquist rate or higher, thus introducing redundancies between adjacent heartbeats due to its quasi-periodic structure. Existing compression methods remove these redundancies by achieving compression and facilitate transmission of the patient’s imperative information. Based on the fact that these signals can be approximated by a linear combination of a few coefficients taken from different basis, an alternative new compression scheme based on Compressive Sensing (CS) has been proposed. CS provides a new approach concerned with signal compression and recovery by exploiting the fact that ECG signal can be reconstructed by acquiring a relatively small number of samples in the “sparse” domains through well-developed optimization procedures. In this paper, a single-lead ECG compression method has been proposed based on improving the signal sparisty through the extraction of the signal significant features. The proposed method starts with a preprocessing stage that detects the peaks and periods of the Q, R and S waves of each beat. Then, the QRS-complex for each signal beat is estimated. The estimated QRS-complexes are subtracted from the original ECG signal and the resulting error signal is compressed using the CS technique. Throughout this process, DWT sparsifying dictionaries have been adopted. The performance of the proposed algorithm, in terms of the reconstructed signal quality and compression ratio, is evaluated by adopting DWT spatial domain basis applied to ECG records extracted from the MIT-BIH Arrhythmia Database. The results indicate that average compression ratio of 11:1 with PRD1 = 1.2% are obtained. Moreover, the quality of the retrieved signal is guaranteed and the compression ratio achieved is an improvement over those obtained by previously reported algorithms. Simulation results suggest that CS should be considered as an acceptable methodology for ECG compression.

Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network

The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.

Visualization of the Machine Learning Process Using J48 Decision Tree for Biometrics through ECG Signal

The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.

Multimodal Emotion Recognition Based on Facial Expression and ECG Signal

As a key link in human-computer interaction,emotion recognition can enable robots to correctly perceive user emotions and provide dynamic and adjustable services according to the emotional needs of different users,which is the key to improve the cognitive level of robot service.Emotion recognition based on facial expression and electrocardiogram has numerous industrial applications.First,three-dimensional convolutional neural network deep learning architecture is utilized to extract the spatial and temporal features from facial expression video data and electrocardiogram(ECG)data,and emotion classification is carried out.Then two modalities are fused in the data level and the decision level,respectively,and the emotion recognition results are then given.Finally,the emotion recognition results of single-modality and multi-modality are compared and analyzed.Through the comparative analysis of the experimental results of single-modality and multi-modality under the two fusion methods,it is concluded that the accuracy rate of multi-modal emotion recognition is greatly improved compared with that of single-modal emotion recognition,and decision-level fusion is easier to operate and more effective than data-level fusion.

Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, networkconnectivity is facilitated between smart devices from anyplace and anytime.IoT-based health monitoring systems are gaining popularity and acceptance forcontinuous monitoring and detect health abnormalities from the data collected.Electrocardiographic (ECG) signals are widely used for heart diseases detection.A novel method has been proposed in this work for ECG monitoring using IoTtechniques. In this work, a two-stage approach is employed. In the first stage, arouting protocol based on Dynamic Source Routing (DSR) and Routing byEnergy and Link quality (REL) for IoT healthcare platform is proposed for effi-cient data collection, and in the second stage, classification of ECG for Arrhythmia. Furthermore, this work has evaluated Support Vector Machine (SVM),Artificial Neural Network (ANN), and Convolution Neural Networks (CNNs)-based approach for ECG signals classification. Deep-ECG will use a deep CNNto extract critical features and then compare through evaluation of simple and fastdistance functions in order to obtain an efficient classification of heart abnormalities. For the identification of abnormal data, this work has proposed techniquesfor the classification of ECG data, which has been obtained from mobile watchusers. For experimental verification of the proposed methods, the Beth Israel Hospital (MIT/BIH) Arrhythmia and Massachusetts Institute of Technology (MIT)Database was used for evaluation. Results confirm the presented method’s superior performance with regards to the accuracy of classification. The CNN achievedan accuracy of 91.92% and has a higher accuracy of 4.98% for the SVM and2.68% for the ANN.

Modeling of Biological Tissues Response to Radio Frequency （RF）： Towards Remote Sensing of Electrocardiography Signal

Premature newborns are at high risk of developing infections, so they require continuous monitoring of vital parameters for long periods of time, until they approximately reach the pregnancy due date. ECG （electrocardiography） is one of the most widely used method for evaluating the structure-function relationship of the heart in health and in sickness. Due to incomplete skin development, premature newborns have some special requirements to the ECG monitoring electrodes. Contact ECG monitoring adversely affects the health and comfort of the newborns. The goal of this study is to determine the feasibility of using RF （radio frequency） in ECG signal remote sensing. This requires studying the interaction mechanisms between RF fields and biological tissues The ECG current propagated from the heart through the skin has an effect on the permittivity of the skin which is frequency dependent. Thus, the feasibility of detecting the change of the relative permittivity in the presence of ECG signal is also discussed. The RF biological tissues response is simulated using MATLAB software in preparation for experimental validation.

模拟ECG信号在320排CT冠脉成像中的应用价值

目的探讨模拟ECG信号在320排CT冠脉成像中的应用价值。方法收集和分析我院2015-01-01至2021-10-01期间,使用模拟ECG信号成像的20例患者(A组),与同时期,随机抽取的,常规技术成像的20例患者(B组)的冠脉成像结果,实行对照研究。结果A、B两组图像质量主观法评价,图像质量无显著差异(P=0.3758>0.05);A、B两组图像质量客观法评价,升主动脉根部,右冠状动脉近端,左前降支近端,左旋支近端CT值以及升主动脉根部层面噪声均无明显差异(P>0.05);A、B两组辐射剂量对比有显著差异(P<0.01)。结论模拟ECG信号在320排CT冠脉成像中的应用是可行的,值得推广。

多模型投票的深度学习ECG分类方法设计与研究

由于经典机器学习算法在心电信号(Recording of electrocardiograms,ECG)分析中存在特征表征能力不足等原因,基于深度学习投票机制,提出了一种基于多模型投票的深度学习ECG波形分类方法。利用多个具有不同网络参数的深度神经网络对ECG信号进行分类,并通过加权投票来提高ECG信号的分类准确率。实验的平均分类准确率为98%,与传统方法以及其它深度学习方法比如支持向量机,卷积神经网络,深度神经网络以及长短期记忆网络的结果比较,验证了上述方法在分类精度上有显著提高。

基于VMD算法的ECG信号基线漂移校正研究

针对现有方法校正ECG信号基线漂移的缺陷,提出基于VMD算法校正ECG信号的基线漂移。首先获取含真实基线漂移的ECG信号;然后基于最佳参数的VMD将含噪ECG信号分解为多个IMF分量,利用各IMF分量频谱图的峰值频率判断基线漂移;最后将含基线漂移的IMF分量舍弃,将其他IMF分量叠加得到去除基线漂移的ECG信号。同时将EMD算法和该方法对相同含基线漂移的ECG信号进行去噪,结果表明,该方法能更好地校正ECG信号基线漂移,且去噪后与原信号的相关系数更大。