A lead dependent ischemic episodes detection strategy using Hermite functions

In this work a new strategy for automatic detection of ischemic episodes is proposed. A new measure for ST deviation based on the time–frequency analysis of the ECG and the use of a reduced set of Hermite basis functions for T wave and QRS complex morphology characterization, are the key points of the proposed methodology.Usually, ischemia manifests itself in the ECG signal by ST segment deviation or by QRS complex and T wave changes in morphology. These effects might occur simultaneously. Time–frequency methods are especially adequate for the detection of small transient characteristics hidden in the ECG, such as ST segment alterations. A Wigner–Ville transform-based approach is proposed to estimate the ST shift. To characterize the alterations in the T wave and the QRS morphologies, each cardiac beat is described by expansions in Hermite functions. These demonstrated to be suitable to capture the most relevant morphologic characteristics of the signal. A lead dependent neural network classifier considers, as inputs, the ST segment deviation and the Hermite expansion coefficients. The ability of the proposed method in ischemia episodes detection is evaluated using the European Society of Cardiology ST–T database. A sensitivity of 96.7% and a positive predictivity of 96.2% reveal the capacity of the proposed strategy to perform ischemic episodes identification.     

Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram

The automatic detection of electrocardiogram (ECG) waves namely P, QRS and T-wave is important to cardiac disease diagnosis. This paper presents an application of support vector machine (SVM) as a classifier for the delineation of ECG wave components in the 12-lead ECG signal. Digital filtering techniques are used to remove power line interference and baseline wander present in the ECG signal. Gradient of the filtered ECG signal is used as a feature for the detection of QRS-complexes, P- and T-waves. The performance of the algorithm is validated using original 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate is achieved. The percentage of false positive and false negative detection is low. The method successfully detects all kind of morphologies of QRS-complexes, P- and T-waves. The onsets and offsets of the detected QRS-complexes, P- and T-waves are found to be within the tolerance limits given in CSE library.     

SVM-based algorithm for recognition of QRS complexes in electrocardiogram

Among all electrocardiogram (ECG) components, the QRS complex is the most significant feature. This paper presents a new algorithm for recognition of QRS complexes in the electrocardiogram (ECG) based on support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander in the ECG signal. SVM is used as a classifier to delineate QRS and non-QRS regions. Algorithm performance was evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.3% of the detection rate. The percentage of false positive and false negative was 12.4 and 0.7% respectively. It could function reliably even under the condition of poor signal quality of the ECG signal.     

Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features

This paper proposes a new power spectral-based hybrid genetic algorithm-support vector machines (SVMGA) technique to classify five types of electrocardiogram (ECG) beats, namely normal beats and four manifestations of heart arrhythmia. This method employs three modules: a feature extraction module, a classification module and an optimization module. Feature extraction module extracts electrocardiogram's spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. Support vector machine (SVM) is employed as a classifier to recognize the ECG beats. We investigate and compare two such classification approaches. First they are specified experimentally by the trial and error method. In the second technique the approach optimizes the relevant parameters through an intelligent algorithm. These parameters are: Gaussian radial basis function (GRBF) kernel parameter σ and C penalty parameter of SVM classifier. Then their performances in classification of ECG signals are evaluated for eight files obtained from the MIT–BIH arrhythmia database. Classification accuracy of the SVMGA approach proves superior to that of the SVM which has constant and manually extracted parameter.     

A mathematical algorithm for ECG signal denoising using window analysis

BACKGROUND: The presence of parasite interference signals could cause serious problems in the registration of ECG signals and many works have been done to suppress electromyogram (EMG) artifacts noises and disturbances from electrocardiogram (ECG). Recently, new developed techniques based on global and local transforms have become popular such as wavelet shrinkage approaches (1995) and time-frequency dependent threshold (1998). Moreover, other techniques such as artificial neural networks (2003), energy thresholding and Gaussian kernels (2006) are used to improve previous works. This review summarizes windowed techniques of the concerned issue. METHODS AND RESULTS: We conducted a mathematical method based on two sets of information, which are dominant scale of QRS complexes and their domain. The task is proposed by using a varying-length window that is moving over the whole signals. Both the high frequency (noise) and low frequency (base-line wandering) removal tasks are evaluated for manually corrupted ECG signals and are validated for actual recorded ECG signals. CONCLUSIONS: Although, the simplicity of the method, fast implementation, and preservation of characteristics of ECG waves represent it as a suitable algorithm, there may be some difficulties due to pre-stage detection of QRS complexes and specification of algorithm's parameters for varying morphology cases.     

24 Hour ST Segment Analysis in Transient Left Ventricular Apical Ballooning

Objective

The etiologic basis of transient left ventricular apical ballooning, a novel cardiac syndrome, is not clear. Among the proposed pathomechanisms is coronary vasospasm. Long-term ST segment analysis may detect vasospastic episodes but has not been reported.

Methods

30 consecutive patients with transient left ventricular apical ballooning, left ventricular dysfunction and normal or near-normal coronary arteries were investigated. A 24-hour Holter ECG was obtained after emergency admission. ST segment analysis was performed automatically in 2 leads and confirmed by visual inspection. Criteria for an ischemic event were: 1. ST elevation or 2. horizontal or down-sloping ST segments ≥1 min duration and ≥100 µV J+80 point deviation corrected for baseline ST-deviation.

Results

Patients presented with ST segment elevation (n = 19) and/or T wave inversion (n = 20) on admission ECG. Ejection fraction was 50±12%. No transient ST elevations were observed during Holter ECG analysis. In 3 patients, 8 transient episodes of ST depression were recorded. Durations of episodes varied between 75s and 790s (mean 229s). Maximal ST deviation averaged −191±71 µV. Ischemic burden was −1 to −22 mVs (mean −8 mVs). 27 patients showed no ischemic events.

Conclusions

ST segment analysis of 24 h Holter recordings revealed minor ischemic events in only 10% of patients with transient left ventricular apical ballooning. Overall, ST segment changes were not indicative of recurrent coronary spasm playing a major role in the genesis of transient left ventricular apical ballooning.     

A structural alignment kernel for protein structures

MOTIVATION: This work aims to develop computational methods to annotate protein structures in an automated fashion. We employ a support vector machine (SVM) classifier to map from a given class of structures to their corresponding structural (SCOP) or functional (Gene Ontology) annotation. In particular, we build upon recent work describing various kernels for protein structures, where a kernel is a similarity function that the classifier uses to compare pairs of structures. RESULTS: We describe a kernel that is derived in a straightforward fashion from an existing structural alignment program, MAMMOTH. We find in our benchmark experiments that this kernel significantly out-performs a variety of other kernels, including several previously described kernels. Furthermore, in both benchmarks, classifying structures using MAMMOTH alone does not work as well as using an SVM with the MAMMOTH kernel. AVAILABILITY: http://noble.gs.washington.edu/proj/3dkernel     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

Nowadays, doctors use electrocardiogram (ECG) to diagnose heart diseases commonly. However, some nonideal effects are often distributed in ECG. Discrete wavelet transform (DWT) is efficient for nonstationary signal analysis. In this paper, the Symlets sym5 is chosen as the wavelet function to decompose recorded ECG signals for noise removal. Soft-thresholding method is then applied for feature detection. To detect ECG features, R peak of each heart beat is first detected, and the onset and offset of the QRS complex are then detected. Finally, the signal is reconstructed to remove high frequency interferences and applied with adaptive searching window and threshold to detect P and T waves. We use the MIT-BIH arrhythmia database for algorithm verification. For noise reduction, the SNR improvement is achieved at least 10 dB at SNR 5 dB, and most of the improvement SNR are better than other methods at least 1 dB at different SNR. When applying to the real portable ECG device, all R peaks can be detected when patients walk, run, or move at the speed below 9 km/h. The performance of delineation on database shows in our algorithm can achieve high sensitivity in detecting ECG features. The QRS detector attains a sensitivity over 99.94%, while detectors of P and T waves achieve 99.75% and 99.7%, respectively.     

A wavelet optimization approach for ECG signal classification

Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accuracy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimination capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experimental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accuracy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet wavelets).     

基于小波变换的QRS综合波的检测

本文描述了基于二进制小波变换（DyWT),ECG信号中QRS综合波的检测。设计－小波它适合于QRS检测,将基于心电信号的特殊的特征的特征为小波的尺度。DyWT较之其它方法最基本的优点为强有力的抑制噪声检测以及在分析随时间变化ECG波形时的灵活性。     

Sleep apnoea detection from ECG using features extracted from reconstructed phase space and frequency domain

Sleep apnoea is a very common sleep disorder which is able to cause symptoms such as daytime sleepiness, irritability and poor concentration. This paper presents a combinational feature extraction approach based on some nonlinear features extracted from Electro Cardio Graph (ECG) Reconstructed Phase Space (RPS) and usually used frequency domain features for detection of sleep apnoea. Here 6 nonlinear features extracted from ECG RPS are combined with 3 frequency based features to reconstruct final feature set. The nonlinear features consist of Detrended Fluctuation Analysis (DFA), Correlation Dimensions (CD), 3 Large Lyapunov Exponents (LLEs) and Spectral Entropy (SE). The final proposed feature set show about 94.8% accuracy over the Physionet sleep apnoea dataset using a kernel based SVM classifier. This research also proves that using non-linear analysis to detect sleep apnoea can potentially improve the classification accuracy of apnoea detection system.     

低风险胸痛急性冠状动脉综合征患者心电图特征及其对诊断的价值研究

摘要 目的：分析低风险胸痛急性冠状动脉综合征(acute coronary syndrome,ACS)患者心电图特征及其对诊断的价值。方法：选择我院自2017年1月至2019年8月接诊的194例疑似低风险胸痛ACS患者,均采取心电图检查和冠状动脉造影检查;分析低风险胸痛ACS患者的心电图特征,观察心电图结果与冠状动脉病变支数、狭窄程度的关系,计算心电图诊断低风险胸痛ACS的特异性、敏感性等效能指标,使用受试者工作特征(receiver operating characteristic,ROC)曲线下面积(curve,AUC)定量分析ST段偏移值预测主要不良心血管事件的效能。结果：在194例疑似低风险胸痛ACS患者中,低风险胸痛ACS患者134例,低风险不稳定型心绞痛(UA)患者心电图表现以ST-T缺血性改变为主,发作时改变明显或呈现伪性改善;低风险非ST段抬高的心肌梗死(non-ST-segment elevation myocardial infarction,NSTEMI)患者心电图表现为肢体和胸导联ST段压低,T波低平、倒置,ST-T改变持续存在和呈动态衍变;低风险胸痛ACS患者心电图结果与冠状动脉病变支数无关(P＞0.05),与狭窄程度有关(P＜0.05);心电图诊断低风险胸痛ACS的特异性为71.67 %,敏感性为69.40 %,阳性预测值为84.55 %,阴性预测值为51.19 %,符合率为70.62 %;所有患者均获得随访,经ROC曲线分析,ST段偏移值预测低风险胸痛ACS患者发生主要不良心血管事件的最佳截值为1.85 mm,AUC为0.695,对比全球急性冠状动脉事件注册(GRACE)风险评分的0.675,差异无统计学意义(P＞0.05)。结论：低风险胸痛ACS患者心电图具有多样化,与冠状动脉狭窄程度有关,有助于初步诊断和风险评估,且ST段偏移值预测主要不良心血管事件的效能较好,值得进一步研究应用。     

Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination

Background

Elevated transient ischemic ST segment episodes in the ambulatory electrocardiographic (AECG) records appear generally in patients with transmural ischemia (e. g. Prinzmetal's angina) while depressed ischemic episodes appear in patients with subendocardial ischemia (e. g. unstable or stable angina). Huge amount of AECG data necessitates automatic methods for analysis. We present an algorithm which determines type of transient ischemic episodes in the leads of records (elevations/depressions) and classifies AECG records according to type of ischemic heart disease (Prinzmetal's angina; coronary artery diseases excluding patients with Prinzmetal's angina; other heart diseases).

Methods

The algorithm was developed using 24-hour AECG records of the Long Term ST Database (LTST DB). The algorithm robustly generates ST segment level function in each AECG lead of the records, and tracks time varying non-ischemic ST segment changes such as slow drifts and axis shifts to construct the ST segment reference function. The ST segment reference function is then subtracted from the ST segment level function to obtain the ST segment deviation function. Using the third statistical moment of the histogram of the ST segment deviation function, the algorithm determines deflections of leads according to type of ischemic episodes present (elevations, depressions), and then classifies records according to type of ischemic heart disease.

Results

Using 74 records of the LTST DB (containing elevated or depressed ischemic episodes, mixed ischemic episodes, or no episodes), the algorithm correctly determined deflections of the majority of the leads of the records and correctly classified majority of the records with Prinzmetal's angina into the Prinzmetal's angina category (7 out of 8); majority of the records with other coronary artery diseases into the coronary artery diseases excluding patients with Prinzmetal's angina category (47 out of 55); and correctly classified one out of 11 records with other heart diseases into the other heart diseases category.

Conclusions

The developed algorithm is suitable for processing long AECG data, efficient, and correctly classified the majority of records of the LTST DB according to type of transient ischemic heart disease.     

Cardiac dynamics during daily torpor in the Djungarian hamster (Phodopus sungorus)

Djungarian or Siberian hamsters (Phodopus sungorus) acclimated to short photoperiod display episodes of spontaneous daily torpor with metabolic rate depressed by approximately 70% and body temperature (T(b)) reduced by approximately 20 degrees C. To study the cardiovascular adjustment to daily torpor in Phodopus, electrocardiogram (ECG) and T(b) were continuously recorded by telemetry during entrance into torpor, in deep torpor, and during arousal from torpor. Minimum T(b) during torpor bouts was approximately 21 degrees C, and heart rate, approximately 349 beats/min at euthermy, displayed marked sinus bradyarrhythmia at approximately 70 beats/min. Arousal was typically completed within approximately 40 min, followed by a sustained post-torpor inactivity tachycardia ( approximately 540 beats/min). The absence of episodes of conduction block, tachyarrhythmia, or other forms of ectopy throughout the torpor cycle demonstrates a remarkable resistance to arrhythmogenesis. The ECG morphology lacks a distinct isoelectric interval following the QRS complex, and the ST segment resembles the ECG pattern in mice, with a prominent fast transient outward K(+) current (I(to,f)) determining the early phase of ventricular repolarization. During low-temperature torpor, the amplitudes of the QRS complex substantially increased, suggesting that in the euthermic state the terminal portion of ventricular depolarization is fused with the beginning of repolarization, low T(b) acting to decorrelate the superposition between depolarization and repolarization by delaying the repolarization onset. Atrioventricular and ventricular conduction times were prolonged as function of T(b). In contrast, the QT vs. T(b) relationship showed marked hysteresis indicating the operation of nonlinear control mechanisms whereby the rapid QT shortening during arousal results from additional mechanisms (probably sympathetic stimulation) other than temperature alone.     

Remote homology detection: a motif based approach

MOTIVATION: Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases. RESULTS: We present a method for detecting remote homology that is based on the presence of discrete sequence motifs. The motif content of a pair of sequences is used to define a similarity that is used as a kernel for a Support Vector Machine (SVM) classifier. We test the method on two remote homology detection tasks: prediction of a previously unseen SCOP family and prediction of an enzyme class given other enzymes that have a similar function on other substrates. We find that it performs significantly better than an SVM method that uses BLAST or Smith-Waterman similarity scores as features.     

Paroxysmal atrial fibrillation recognition based on multi-scale wavelet <Emphasis Type="Italic">α</Emphasis>-entropy

Background

This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction.

Results

The performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively.

Conclusions

Our proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment.

     

Decreases in Electrocardiographic R-Wave Amplitude and QT Interval Predict Myocardial Ischemic Infarction in Rhesus Monkeys with Left Anterior Descending Artery Ligation

Clinical studies have demonstrated the predictive values of changes in electrocardiographic (ECG) parameters for the preexisting myocardial ischemic infarction. However, a simple and early predictor for the subsequent development of myocardial infarction during the ischemic phase is of significant value for the identification of ischemic patients at high risk. The present study was undertaken by using non-human primate model of myocardial ischemic infarction to fulfill this gap. Twenty male Rhesus monkeys at age of 2–3 years old were subjected to left anterior descending artery ligation. This ligation was performed at varying position along the artery so that it produced varying sizes of myocardial infarction at the late stage. The ECG recording was undertaken before the surgical procedure, at 2 h after the ligation, and 8 weeks after the surgery for each animal. The correlation of the changes in the ECG waves in the early or the late stage with the myocardial infarction size was analyzed. The R wave depression and the QT shortening in the early ischemic stage were found to have an inverse correlation with the myocardial infarction size. At the late stage, the R wave depression, the QT prolongation, the QRS score, and the ST segment elevation were all closely correlated with the developed infarction size. The poor R wave progression was identified at both the early ischemic and the late infarction stages. Therefore, the present study using non-human primate model of myocardial ischemic infarction identified the decreases in the R wave and the QT interval as early predictors of myocardial infarction. Validation of these parameters in clinical studies would greatly help identifying patients with myocardial ischemia at high risk for the subsequent development of myocardial infarction.     

Predicting co-complexed protein pairs from heterogeneous data

Proteins do not carry out their functions alone. Instead, they often act by participating in macromolecular complexes and play different functional roles depending on the other members of the complex. It is therefore interesting to identify co-complex relationships. Although protein complexes can be identified in a high-throughput manner by experimental technologies such as affinity purification coupled with mass spectrometry (APMS), these large-scale datasets often suffer from high false positive and false negative rates. Here, we present a computational method that predicts co-complexed protein pair (CCPP) relationships using kernel methods from heterogeneous data sources. We show that a diffusion kernel based on random walks on the full network topology yields good performance in predicting CCPPs from protein interaction networks. In the setting of direct ranking, a diffusion kernel performs much better than the mutual clustering coefficient. In the setting of SVM classifiers, a diffusion kernel performs much better than a linear kernel. We also show that combination of complementary information improves the performance of our CCPP recognizer. A summation of three diffusion kernels based on two-hybrid, APMS, and genetic interaction networks and three sequence kernels achieves better performance than the sequence kernels or diffusion kernels alone. Inclusion of additional features achieves a still better ROC(50) of 0.937. Assuming a negative-to-positive ratio of 600ratio1, the final classifier achieves 89.3% coverage at an estimated false discovery rate of 10%. Finally, we applied our prediction method to two recently described APMS datasets. We find that our predicted positives are highly enriched with CCPPs that are identified by both datasets, suggesting that our method successfully identifies true CCPPs. An SVM classifier trained from heterogeneous data sources provides accurate predictions of CCPPs in yeast. This computational method thereby provides an inexpensive method for identifying protein complexes that extends and complements high-throughput experimental data.     

Coronary spasm reflects inputs from adjacent esophageal system

Mechanisms underlying coronary spasm are still poorly understood. The aim of the study was to assess the hypothesis that fluctuations in the development of coronary spasm might reflect inputs from the adjacent esophageal system. We enrolled patients admitted to the coronary care unit for episodes of nocturnal angina. Seven patients with variant angina and five with coronary artery disease (CAD) had concurrent ECG and esophageal manometric monitoring. ECG monitoring documented 28 episodes of ST elevation in variant angina patients and 16 episodes of ST depression in CAD patients. Manometric analysis showed that esophageal spasms resulted remarkably more frequently in variant angina patients (143 total spasms; individual range 9-31) than in CAD patients (20 total spasms; individual range 0-9; P < 0.01). Time series analysis was used to assess fluctuations in the occurrence of abnormal esophageal waves and its relationship with spontaneous episodes of ST shift. Episodes of esophageal spasm in CAD were sporadic (<1 in 30 min) and not related to ECG-recorded ischemia. In the variant angina group, esophageal spasms were time related to ischemia (>1 into 5 min before ECG-recorded ischemia) (P < 0.05). A bidirectional analysis of causal effects showed that the influence processes between esophageal and coronary spasms were mutual and reciprocal (transfer function model, P < 0.05) in variant angina. We concluded that in variant angina patients, episodes of esophageal spasms and myocardial ischemia influenced each other. Mechanisms that cause esophageal spasm can feed back to produce coronary spasm. Coronary spasm may feed forward to produce additional episodes of esophageal spasm.