Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats

Abstract: Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of the ME network structure to guide model selection for classification of electrocardiogram (ECG) beats. The expectation maximization algorithm is used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ECG signals were decomposed into time–frequency representations using discrete wavelet transforms and statistical features were calculated to depict their distribution. The ME network structure was implemented for ECG beats classification using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database were classified with an accuracy of 96.89% by the ME network structure. The ME network structure achieved accuracy rates which were higher than those of the stand-alone neural network models.     

Combining recurrent neural networks with eigenvector methods for classification of ECG beats

The purpose of this study is to evaluate the accuracy of the recurrent neural networks (RNNs) trained with Levenberg–Marquardt algorithm on the electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the RNN trained on the extracted features. The RNNs were implemented for classification of the ECG beats using the statistical features as inputs. The ability of designed and trained Elman RNNs, combined with eigenvector methods, were explored to classify the ECG beats. The classification results demonstrated that the combined eigenvector methods/RNN approach can be useful in analyzing the ECG beats.     

A modified mixture of experts network structure for ECG beats classification with diverse features

This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of electrocardiogram (ECG) beats with diverse features. The MME is a modular neural network architecture for supervised learning. Expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. The wavelet coefficients and Lyapunov exponents of the ECG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the ECG signals, were then input into the MME network structure for training and testing purposes. We explored the ability of designed and trained MME network structure, combined with wavelet preprocessing (computing wavelet coefficients) and nonlinear dynamics tools (computing Lyapunov exponents), to discriminate five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network—MLPNN). The proposed MME approach can be useful in classifying long-term ECG signals for early detection of heart diseases/abnormalities.     

Statistics over features of ECG signals

This paper presented the usage of statistics over the set of the features representing the electrocardiogram (ECG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of the ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the ECG signals were used as inputs of the MLPNN trained with Levenberg–Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the variabilities of the ECG signals.     

基于U-NET网络的心律失常信号识别算法研究

在心电信号心律失常自动识别系统中,针对心电信号形态复杂导致特征提取困难、自动分类模型准确度低、现实应用性差的问题,设计了一种基于U-NET全卷积神经网络的心电信号语义分割的识别分类方法。该方法通过全卷积神经网络的编码运算规则,将心电信号切片数据作为输入,标签地图作为输出,可划分出信号片段中的心拍位置与类别。仿真结果表明：该方法在正常窦性搏动、左束支传导阻滞、右束支传导阻滞、房性早搏和室性早搏五分类问题中取得较高准确率,实现了对心律失常信号的有效识别。     

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of electrocardiogram (ECG) beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. ECG signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in the ECG beats-classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features.     

Combined neural network model employing wavelet coefficients for EEG signals classification

This paper illustrates the use of combined neural network model to guide model selection for classification of electroencephalogram (EEG) signals. The EEG signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the EEG signals classification using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 94.83% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model.     

An improved procedure for detection of heart arrhythmias with novel pre‐processing techniques

The objective of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial pre‐mature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and pre‐mature ventricular contraction beats (PVC or V) using a neural network classifier. In order to prepare an appropriate input vector for the neural classifier several pre‐processing stages have been applied. Initially, a signal filtering method is used to remove the ECG signal baseline wandering. Continuous wavelet transform is then applied in order to extract features of the ECG signal. Next, principal component analysis is used to reduce the size of the data. A well‐known neural network architecture called the multi‐layered perceptron neural network is then utilized as the final classifier to classify each ECG beat as one of six groups of signals under study. Finally, the MIT‐BIH database is used to evaluate the proposed algorithm, resulting in 99.5% sensitivity, 99.66% positive predictive accuracy and 99.17% total accuracy.     

Detecting variabilities of ECG signals by Lyapunov exponents

An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg–Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of classification accuracies. The results confirmed that the MLPNN trained with the Levenberg–Marquardt algorithm has potential in detecting the variabilities of the ECG signals (total classification accuracy was 95.00%).     

Adaptive wavelet network for multiple cardiac arrhythmias recognition

This paper proposes a method for electrocardiogram (ECG) heartbeat detection and recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction from QRS complexes, and then according to characteristic features to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. The method of ECG beats is a two-subnetwork architecture, Morlet wavelets are used to enhance the features from each heartbeat, and probabilistic neural network (PNN) performs the recognition tasks. The AWN method is used for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results used from the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed non-invasive method. Compared with conventional multi-layer neural networks, the test results also show accurate discrimination, fast learning, good adaptability, and faster processing time for detection.     

Integration of independent component analysis and neural networks for ECG beat classification

In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG.     

ECG beat classification by a novel hybrid neural network

This paper presents a novel hybrid neural network structure for the classification of the electrocardiogram (ECG) beats. Two feature extraction methods: Fourier and wavelet analyses for ECG beat classification are comparatively investigated in eight-dimensional feature space. ECG features are determined by dynamic programming according to the divergence value. Classification performance, training time and the number of nodes of the multi-layer perceptron (MLP), restricted Coulomb energy (RCE) and a novel hybrid neural network are comparatively presented. In order to increase the classification performance and to decrease the number of nodes, the novel hybrid structure is trained by the genetic algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure.     

基于聚类分析的心电节拍分类算法

为提高计算机辅助心电节拍分类算法的准确率和普适性,提出一种基于聚类分析的心电节拍分类算法,该算法利用心电节拍个体内差异性较小的特性,采用两级聚类分析、抽样代表性心电节拍的方法,结合心电医师的辅助诊断,实现对心电节拍的准确分类。为了验证算法的准确性,采用国际公认的标准数据库--MIT-BIH心律失常数据库,AAMI/ANSI标准规定的心电节拍分类方法及准确率的计算方法进行仿真实验,最终总体分类准确率达到99.07%。与Kiranyaz等(KIRANYAZ S, INCE T,PULKKINEN J, et al. Personalized long-term ECG classification: A systematic approach［J］. Expert Systems with Applications, 2011, 38(4): 3220-3226.)的心电节拍分类算法相比,该算法无需进行设定的训练,且S类心电节拍分类灵敏度由40.15%提高到89.82%,显著提高了分类算法的普适性。     

Application of principal component analysis to ECG signals for automated diagnosis of cardiac health

Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. The subtle changes in amplitude and duration of ECG cannot be deciphered precisely by the naked eye, hence imposing the need for a computer assisted diagnosis tool. In this paper we have automatically classified five types of ECG beats of MIT-BIH arrhythmia database. The five types of beats are Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). In this work, we have compared the performances of three approaches. The first approach uses principal components of segmented ECG beats, the second approach uses principal components of error signals of linear prediction model, whereas the third approach uses principal components of Discrete Wavelet Transform (DWT) coefficients as features. These features from three approaches were independently classified using feed forward neural network (NN) and Least Square-Support Vector Machine (LS-SVM). We have obtained the highest accuracy using the first approach using principal components of segmented ECG beats with average sensitivity of 99.90%, specificity of 99.10%, PPV of 99.61% and classification accuracy of 98.11%. The system developed is clinically ready to deploy for mass screening programs.     

ECG signal classification via combining hand-engineered features with deep neural network features

The electrocardiogram (ECG) has been proven to be the most common and effective approach to investigate cardiovascular diseases because that it is simple, noninvasive and inexpensive. However, the differences among ECG signals are difficult to be distinguished. In this paper, hand-engineered ECG features and automatic ECG features extracted with deep neural networks are combined to generate high dimensional features. First, rich hand-engineered features were extracted using some extraction methods for common ECG features. Second, a convolutional neural network model was designed to extract the ECG features automatically. High dimensional feature set is obtained through combing hand-engineered features and automatic features. To get the most informative ECG feature combination, a feature selection method based on mutual information was proposed. An ensemble learning method was then used to build the classification model for abnormal ECG types. Six atrial arrhythmia subtypes’ ECG signals from the Chinese cardiovascular disease database dataset were analyzed through the proposed method. The precision of the classification results reaches 98.41%, which is higher than the results based on other current methods.

     

基于深度长短时记忆神经网络模型的心律失常检测算法

针对传统基于形态特征的心电检测算法存在特征提取不准确和高复杂性等问题,提出了一种多层的长短时记忆（LSTM）神经网络结构。结合传统LSTM模型在时序数据处理上的优势,该模型增加了反向和深度计算,避免了人工提取波形特征,提高了网络的学习能力。通过给定心拍序列和分类标签进行监督学习,然后实现对未知心拍的心律失常检测。通过对MIT-BIH数据库中的心律失常数据集进行实验验证,模型的总体准确率为98.34%。相比支持向量机（SVM）,该模型的准确率和F1值均有提高。     

Detection of electrocardiogram signals using an efficient method

Automatic detection of electrocardiogram (ECG) signals is very important for clinical diagnosis of heart disease. This paper investigates the design of a three-step system for recognition of the five types of ECG beat. In the first step, stationary wavelet transform (SWT) is used for noise reduction of the electrocardiogram (ECG) signals. Feature extraction module extracts higher order statistics of ECG signals in combination with three timing interval features. Then hybrid Bees algorithm-radial basis function (RBF_BA) technique is used to classify the five types of electrocardiogram (ECG) beat. The suggested method can accurately classify and discriminate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). Finally, the classification capability of five different classes of ECG signals is attained over eight files from the MIT/BIH arrhythmia database. Simulation results show that classification accuracy of 95.79% for the first dataset (4000 beats) and an overall accuracy of detection of 95.18% are achieved over eight files from the MIT/BIH arrhythmia database.     

类内类间距离在CNN识别心拍类中的应用研究

心脏疾病严重威胁人类身体健康，心电图（Electrocardiogram，ECG）心拍分类对心脏疾病的临床诊断和自动诊断具有重要意义。现有基于深度学习生成的ECG心拍特征虽然优于基于传统方法生成的心拍特征，但是因ECG中各类间存在着严重的数据不平衡问题，致使现有基于深度学习方法生成的心拍特征的性能仍不甚理想。针对这一问题，以卷积神经网络（Convolutional Neural Network，CNN）为基础，在各类心拍等量数据基础上构建能有效表达各类心拍共性信息的共性CNN模型，以共性CNN模型和最小化类内距离最大化类间距离模型为基础，分别在各类心拍数据上构建能有效反映相应心拍类别倾向性信息的类别CNN模型，综合各心拍类别CNN模型的输出进行识别与分类。在MIT-BIH数据库上的实验结果显示，该方法识别分类心拍的各项指标均达到100%，解决了MIT-BIH数据库中ECG四类心拍自动识别分类的问题。     

基于改进K均值聚类生成匹配模板的心搏分类方法

为提高分析含大量数据的动态心电时的准确性和分析效率,提出了一种基于改进的K均值聚类生成心搏模板的匹配方法.使用K均值聚类和波形反混淆技术进行循环纠错,生成可变宽心搏模板、并建立心搏模板库.利用可变宽心搏模板和相关系数相结合的策略,对动态心电中心搏进行快速准确分类.实验方法经心率失常数据库MIT-BIT和ANMA/ANSI标准验证,分类结果总体准确率达98.06%,达到了心搏分类目标.     

TSCNN:面向可穿戴心电信号监测与分析的卷积神经网络

目的 可穿戴设备能够长时间实时监测人体心脏状况,其在心电信号监测领域应用广泛。但目前仍没有公开的来自可穿戴设备的心电数据集,大部分心电信号分析算法都是针对医院设备所采集的心电数据。因此,本文使用IREALCARE 2.0柔性远程心电贴作为心电信号监测和采集设备制作了可穿戴设备的心电数据集。针对可穿戴心电数据干扰多、数据量大等特点,本文提出了一种针对可穿戴设备获得的心电信号进行自动分类的深层卷积神经网络,称之为时空卷积神经网络（time-spatial convolutional neural networks,TSCNN）。方法 将原始的长时间心电信号分割为单个的心搏并与滤波后不同频段的心搏数据组合成十通道的数据输入到TSCNN中。TSCNN对每个心搏使用时间卷积和空间滤波来提取丰富的特征。采用小卷积核级联卷积的方式提高分类性能,并降低网络的参数量和计算量。结果 在本文制作的心电数据集上进行了测试,并与其他4种心电分类算法：CNN（convolutional neural networks）、RNN（recurrent neural networks）、1-DCNN（1-dimensional convolution neural networks）和DCN（dense convolutional networks）进行了比较。实验结果显示,本文方法的分类准确率达到91.16%,优于其他4种方法。结论 本文方法面向可穿戴心电数据,获得了较好的分类性能,可以有效监控穿戴者是否出现了心电异常情况。