Weighted conditional random fields for supervised interpatient heartbeat classification

This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.     

ECG signal analysis through hidden Markov models

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.     

ECG beat detection using filter banks

The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters     

Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea

A method for the automatic processing of the electrocardiogram (ECG) for the detection of obstructive apnoea is presented. The method screens nighttime single-lead ECG recordings for the presence of major sleep apnoea and provides a minute-by-minute analysis of disordered breathing. A large independently validated database of 70 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. Thirty-five of these recordings were used for training and 35 retained for independent testing. A wide variety of features based on heartbeat intervals and an ECG-derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were compared. Feature selection and regularization of classifier parameters were used to optimize classifier performance. Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.     

Noninvasive ECG as a tool for predicting termination of paroxysmal atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and entails an increased risk of thromboembolic events. Prediction of the termination of an AF episode, based on noninvasive techniques, can benefit patients, doctors and health systems. The method described in this paper is based on two-lead surface electrocardiograms (ECGs): 1-min ECG recordings of AF episodes including N-type (not terminating within an hour after the end of the record), S-type (terminating 1 min after the end of the record) and T-type (terminating immediately after the end of the record). These records are organised into three learning sets (N, S and T) and two test sets (A and B). Starting from these ECGs, the atrial and ventricular activities were separated using beat classification and class averaged beat subtraction, followed by the evaluation of seven parameters representing atrial or ventricular activity. Stepwise discriminant analysis selected the set including dominant atrial frequency (DAF, index of atrial activity) and average HR (HRmean, index of ventricular activity) as optimal for discrimination between N/T-type episodes. The linear classifier, estimated on the 20 cases of the N and T learning sets, provided a performance of 90% on the 30 cases of a test set for the N/T-type discrimination. The same classifier led to correct classification in 89% of the 46 cases for N/S-type discrimination. The method has shown good results and seems to be suitable for clinical application, although a larger dataset would be very useful for improvement and validation of the algorithms and the development of an earlier predictor of paroxysmal AF spontaneous termination time.     

An association rule mining-based methodology for automated detection of ischemic ECG beats

Currently, an automated methodology based on association rules is presented for the detection of ischemic beats in long duration electrocardiographic (ECG) recordings. The proposed approach consists of three stages. 1) Preprocessing: Noise is removed and all the necessary ECG features are extracted. 2) Discretization: The continuous valued features are transformed to categorical. 3) Classification: An association rule extraction algorithm is utilized and a rule-based classification model is created. According to the proposed methodology, electrocardiogram (ECG) features extracted from the ST segment and the T-wave, as well as the patient's age, were used as inputs. The output was the classification of the beat as ischemic or not. Various algorithms were tested both for discretization and for classification using association rules. To evaluate the methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 93%, respectively. The proposed methodology combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.     

A stochastic characterization of chronic ventricular ectopicactivity

A novel approach for studying the generation of ventricular ectopic beats (VEBs) is reported. The approach is based on the hypotheses that VEBs are often generated by a mechanism (VEB mechanism) which is stable over time, but whose activity is modulated by a variety of physiologic influences. The hypothesis suggests that VEB generation should be predictable and should be related to other physiologic parameters. The relationship should be reproducible under similar conditions,. To pursue this hypothesis, an analytic approach was developed which represents VEB generation with a conditional probability distribution. The distribution gives the probability that the next beat is a VEB, given information about the preceding beats. Using this approach on a database of half-hour ECG (electrocardiogram) tapes, it is shown that VEB generation is usually related to the preceding beat type and timing, and that this relationship is often reproducible over time. It is concluded that, in a given patient, VEB generation often shows consistent patterns which might serve as a potentially valuable characterization of the patient's VEB mechanism     

Time-based compression and classification of heartbeats

Heart function measured by electrocardiograms (ECG) is crucial for patient care. ECG generated waveforms are used to find patterns of irregularities in cardiac cycles in patients. In many cases, irregularities evolve over an extended period of time that requires continuous monitoring. However, this requires wireless ECG recording devices. These devices consist of an enclosed system that includes electrodes, processing circuitry, and a wireless communication block imposing constraints on area, power, bandwidth, and resolution. In order to provide continuous monitoring of cardiac functions for real-time diagnostics, we propose a methodology that combines compression and analysis of heartbeats. The signal encoding scheme is the time-based integrate and fire sampler. The diagnostics can be performed directly on the samples avoiding reconstruction required by the competing finite rate of innovation and compressed sensing. As an added benefit, our scheme provides an efficient hardware implementation and a compressed representation for the ECG recordings, while still preserving discriminative features. We demonstrate the performance of our approach through a heartbeat classification application consisting of normal and irregular heartbeats known as arrhythmia. Our approach that uses simple features extracted from ECG signals is comparable to results in the published literature.     

Electrocardiogram based neonatal seizure detection

A method for the detection of seizures in the newborn using the electrocardiogram (ECG) signal is presented. Using a database of eight recordings, a method was developed for automatically annotating each 1-min epoch as "nonseizure" or "seizure". The system uses a linear discriminant classifier to process 41 heartbeat timing interval features. Performance assessment of the method showed that on a patient-specific basis an average accuracy of 70.5% was achieved in detecting seizures with associated sensitivity of 62.2% and specificity of 71.8%. On a patient-independent basis the average accuracy was 68.3% with sensitivity of 54.6% and specificity of 77.3%. Shifting the decision threshold for the patient-independent classifier allowed an increase in sensitivity to 78.4% at the expense of decreased specificity (51.6%), leading to increased false detections. The results of our ECG-based method are comparable with those reported for EEG-based neonatal seizure detection systems and offer the benefit of an easier acquisition methodology for seizure detection.     

ECG beat classification using GreyART network

The grey relational grade is a similarity measure. On the basis of the grey relational grade, an adaptive resonant theory (ART) type network, GreyART, has been developed. When the GreyART is used to classify a dataset with varying amount of data, the measurement between two specific data in the dataset may vary since the measurement is affected by new added data. In this case, the grey relational grade is not a global measure. As the measurement varies, in the GreyART, it is hard to use a fixed vigilance threshold value for determining whether the current input data belong to one of the existing clusters or become the template of a new online-created cluster. A method to solve this problem has been proposed and then applied to develop an electrocardiogram (ECG) beat classifier. The proposed ECG beat classification involves two phases. One is the off-line learning phase. With the proposed performance index, the product of the classification accuracy and the partition quality, an optimal value for the vigilance threshold and the corresponding cluster centres from the learning results can be determined. The other is the online examining phase, which classifies the input ECG beats. In this phase, the vigilance threshold value and the initial cluster centres are the optimal ones obtained in the learning phase. Under these conditions, the GreyART network enables real-time classification of ECG beats. Simulation results show that the proposed network achieves a good accuracy with a good computational efficiency for ECG beat classification problems     

HBS: a novel biometric feature based on heartbeat morphology

In this paper, a new feature named heartbeat shape (HBS) is proposed for ECG-based biometrics. HBS is computed from the morphology of segmented heartbeats. Computation of the feature involves three basic steps: 1) resampling and normalization of a heartbeat; 2) reduction of matching error; and 3) shift invariant transformation. In order to construct both gallery and probe templates, a few consecutive heartbeats which could be captured in a reasonably short period of time are required. Thus, the identification and verification methods become efficient. We have tested the proposed feature independently on two publicly available databases with 76 and 26 subjects, respectively, for identification and verification. The second database contains several subjects having clinically proven cardiac irregularities (atrial premature contraction arrhythmia). Experiments on these two databases yielded high identification accuracy (98% and 99.85%, respectively) and low verification equal error rate (1.88% and 0.38%, respectively). These results were obtained by using templates constructed from five consecutive heartbeats only. This feature compresses the original ECG signal significantly to be useful for efficient communication and access of information in telecardiology scenarios.     

基于粗糙集-支持向量机理论的过滤误报警方法

为过滤入侵检测系统报警数据中的误报警,根据报警的根源性和时间性总结出了区分真报警和误报警的19个相关属性,并提出了一种基于粗糙集-支持向量机理论的过滤误报警的方法。该方法首先采用粗糙集理论去除相关属性中的冗余属性,然后将具有约简后的10个属性的报警数据集上的误报警过滤问题转化为分类问题,采用支持向量机理论构造分类器以过滤误报警。实验采用由网络入侵检测器Snort监控美国国防部高级研究计划局1999年入侵评测数据(DARPA99)产生的报警数据,结果表明提出的方法在漏报警约增加1.6%的代价下,可过滤掉约98%的误报警。该结果优于文献中使用相同数据、相同入侵检测系统的其它方法的结果。     

基于滤波重构和卷积神经网络的心电信号分类

计算机自动分类心电信号能够减轻医生工作压力并大幅提高诊断速度和准确率。文中针对传统算法中特征提取过程复杂及抗干扰能力弱的问题,提出了一种结合滤波重构和卷积神经网络的心电信号分类算法。该算法首先通过传统信号滤波和心拍序列重构去除原始心电信号中的噪声干扰,然后构建卷积神经网络来自动学习心电信号特征并完成分类。在PhysioNet/CinC Challenge 2017数据集上的分类实验结果表明,该方法的平均F₁(查准率、召回率的调和平均)达到了0.8471,优于人工特征提取和常规卷积网络方法,且具有很强的抗干扰能力。     

Redefining performance evaluation tools for real-time QRS complex classification systems

In a heartbeat classification procedure, the detection of QRS complex waveforms is necessary. In many studies, this heartbeat extraction function is not considered: the inputs of the classifier are assumed to be correctly identified. This communication aims to redefine classical performance evaluation tools in entire QRS complex classification systems and to evaluate the effects induced by QRS detection errors on the performance of heartbeat classification processing (normal versus abnormal). Performance statistics are given and discussed considering the MIT/BIH database records that are replayed on a real-time classification system composed of the classical detector proposed by Hamilton and Tompkins, followed by a neural-network classifier. This study shows that a classification accuracy of 96.72% falls to 94.90% when a drop of 1.78% error rate is introduced in the detector quality. This corresponds to an increase of about 50% bad classifications.     

Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases

Current trends in clinical applications demand automation in electrocardiogram (ECG) signal processing and heart beat classification. This paper examines the design of an effective recognition method to diagnose heart diseases. The proposed method consists of three main modules: de-noising module, feature extraction module, and classifier module. In the de-noising module, multiscale principal component analysis (MSPCA) is used for noise reduction of the ECG signals. In the feature extraction module, autoregressive (AR) modeling is used for extracting features. In the classifier module, different classifiers are examined such as simple logistic, k-nearest neighbor, multilayer perceptron, radial basis function networks, and support vector machines. Different experiments are carried out using the MIT-BIH arrhythmia database to classify different ECG heart beats and the performance of the proposed method is evaluated in terms of several standard metrics. The experimental results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately in comparison to previous methods. The numerical results indicated that the proposed algorithm achieved 99.93 % of the classification accuracy using MSPCA de-noising and AR modeling.     

基于压电陶瓷传感器的非接触式精准逐拍心率提取方法研究

心冲击图(BCG)可用于无接触式地监测生命体征。在BCG的逐拍心率提取中,较低的平均绝对误差对于精确地获取用户的心率变异性(HRV)指标具有重要意义。为解决目前大多数方法在逐拍心率计算精度方面的不足,该文设计了一种基于压电陶瓷的心冲击信号采集系统。通过采用合适的传感器外壳结构和采样频率,增加传感器的灵敏度和BCG的时间分辨率;通过对比不同的BCG处理方法并找到BCG中最适合提取精准逐拍心动周期的成分;同时该文提出一种采用AP聚类的自适应模板匹配算法,以准确提取心动周期信息。对15名受试者共5741次心跳数据进行分析,结果显示逐拍心动周期的平均误差为0.48%,平均绝对误差为3.78 ms,心跳覆盖率在97%以上,优于其他同类工作。     

基于压电陶瓷传感器的非接触式精准逐拍心率提取方法研究

心冲击图(BCG)可用于无接触式地监测生命体征。在BCG的逐拍心率提取中,较低的平均绝对误差对于精确地获取用户的心率变异性(HRV)指标具有重要意义。为解决目前大多数方法在逐拍心率计算精度方面的不足,该文设计了一种基于压电陶瓷的心冲击信号采集系统。通过采用合适的传感器外壳结构和采样频率,增加传感器的灵敏度和BCG的时间分辨率;通过对比不同的BCG处理方法并找到BCG中最适合提取精准逐拍心动周期的成分;同时该文提出一种采用AP聚类的自适应模板匹配算法,以准确提取心动周期信息。对15名受试者共5741次心跳数据进行分析,结果显示逐拍心动周期的平均误差为0.48%,平均绝对误差为3.78 ms,心跳覆盖率在97%以上,优于其他同类工作。     

Real-time sleep apnea detection by classifier combination

To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It is shown that our proposed SpO (2) features outperform the ECG features in terms of diagnostic ability. More importantly, we propose classifier combination to further enhance the classification performance by harnessing the complementary information provided by individual classifiers. With our selected SpO(2) and ECG features, the classifier combination using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity, specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-breathing suspects' full overnight recordings.     

A Minicomputer System for Direct High Speed Analysis of Cardiac Arrhythmia in 24 h Ambulatory ECG Tape Recordings

A minicomputer system for the analysis of 24 h ambulatory ECG tapes at 60 times real time is presented. The unique aspect of the system is its implementation entirely in software on a single standard minicomputer system. The high scanning speed is achieved by a very efficient ectopic beat detection algorithm and a partition of the software into two phases. An on-line phase builds a reduced file of ectopic beats, which is then submitted to an off-line phase for beat classification and report generation. The entire analysis of a 24 h tape is completed in less than 30 min and requires only initialization by an operator.