Traditional Electrocardiographic Research Articles

ECG classification using 1-D convolutional deep residual neural network.

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier's performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.

Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method.

Cardiovascular disease is a serious health problem. Continuous Electrocardiograph (ECG) monitoring plays a vital role in the early detection of cardiovascular disease. As the Internet of Things technology continues to mature, wearable ECG signal monitors have been widely used. However, dynamic ECG signals are extremely susceptible to contamination. Therefore, it is necessary to evaluate the quality of wearable dynamic ECG signals. The topological data analysis method (TDA) with persistent homology, which can effectively capture the topological information of high-dimensional data space, has been widely studied. In this study, a brand-new quality assessment method of wearable dynamic ECG signals was proposed based on the TDA with persistent homology method. The point cloud of an ECG signal was constructed, and then the complex sequence was generated and displayed as a persistent barcode. Finally, GoogLeNet based on the transfer learning model with a 10-fold cross-validation method was used to train the classification model. A total of 12-leads ECGs Dataset and single-lead ECGs Dataset, established based on the 2011 PhysioNet/CinC challenge dataset, were both used to verify the performance of this method. In the study, 773 "acceptable" and 225 "unacceptable" signals were used as 12-leads ECGs Dataset. We relabeled 12,000 ECG signals in the challenge dataset, and treated them as single-lead ECGs Dataset after empty lead detection and balance datasets. Compared with the traditional ECG signal quality assessment method mainly based on waveform characteristics and time-frequency characteristics, the performance of the quality assessment method proposed. In this study, the classification performance of the proposed method are fairly great, mAcc = 98.04%, F1 = 98.40%, Se = 97.15%, Sp = 98.93% for 12-leads ECGs Dataset and mAcc = 98.55%, F1 = 98.62%, Se = 98.37%, Sp = 98.85% for single-lead ECGs Dataset.

Design and Application of Home Wireless Electrocardiograph Machine Based on Internet

This study introduces the design and application of home wireless electrocardiograph(ECG) machine based on Internet. The world's first three-lead dry electrode mobile electrocardiograph machine has been developed, on the basis of the successful development of dry electrodes. Moreover, it is not only chips filtering, but also wireless, as a result it is applied to ECG monitoring and diagnosis of patients. Compared with traditional electrocardiograph machine, the machine is very convenient and comes into the home, ECG Machines is connected to mobile phones by Bluetooth, wireless upload, therefore we recommend to achieve remote monitoring and early warning and reduce sudden death, to achieve Internet medical by using Internet technology, people can be self-test. It is playing an increasingly important role and it is an inevitable machine to improve the success rate of diagnosis, monitoring and first aid.

Tip 1 Diabetes Mellitusta Kardiyak Elektrofizyolojik Denge İndeksinin Değerlendirilmesi

Background: Type 1 diabetes mellitus (T1DM) has been suggested to carry the risk of developing cardiac arrhythmias. The risk of cardiovascular events and sudden death is higher in patients with T1DM. The index of cardiac electrophysiological balance (iCEB), which shows the balance between ventricular depolarization and repolarization, is a new electrocardiographic (ECG) parameter when compared to other ECG parameters. iCEB provides more information on ventricular arrhythmo-genesis than the other traditional ECG parameters such as QT and corrected QT (QTc) intervals. The aim of this study is to evaluate the relationship between T1DM and iCEB.Materials and Methods: This study was conducted retrospectively. The patient group consisted of 50 patients (58% male, mean age 32.9 ± 8.7 years) diagnosed with T1DM, while the control group consisted of 50 healthy individuals (58% male, mean age 33.9 ± 6.8 years). The clinical, demo-graphic and ECG records of the patients were obtained from our hospital's medical documentation system. iCEB and corrected iCEB (iCEBc) values of all participants were calculated by dividing the relevant QT and QTc intervals by the QRS duration.Results: Mean PR, QRS, and QT intervals were similar between groups, while mean heart rate and QTc interval were found to be higher in the T1DM group than in healthy subjects [(83.1 ± 9.8 vs 78.9 ± 13.3 bpm, respectively, p = 0.019) and (425 ± 20.4 vs 397.6 ± 10.6 ms, respectively, p < 0.001)]. The iCEBc of the patient group with T1DM was higher than the control group (5.1 ± 0.5 vs 4.7 ± 0.4 ms, p <0.001, respectively). However, there was no significant difference in iCEB be-tween the patient and control groups (4.3 ± 0.4 and 4.3 ± 0.4 ms, respectively, p = 0.402).Conclusions: In our study, iCEBc was found to be significantly increased in T1DM patients com-pared to healthy subjects. Consequently; it is important to evaluate the risk of ventricular ar-rhythmia with iCEBc in T1DM patients.Key Words: Electrocardiography, Index of cardiac electrophysiological balance, Type 1 diabetes mellitus, Ventricular arrhythmia

Survey on atrial fibrillation detection from a single-lead ECG wave for Internet of Medical Things

Recent advances of Internet of Medical Things have allowed for continuous heart rhythm monitoring in a comfortable fashion. Single lead Electrocardiograph (ECG) is first collected by the wearable devices, and then some intelligent approaches are employed for automatic recognition of heart rhythms. Because the single lead ECG wave is different from traditional 12-leads Holter-based ECG signal in terms of high noise/artifact and the missing of other channels, specific algorithms for pattern recognition of the single lead ECG waves have been proposed in recent years. This paper systematically surveys state-of-the-art methods for screening atrial fibrillation from a single lead ECG wave. The database and performance metrics for this problem are demonstrated, the data preprocessing and feature extraction techniques are collected, and then the learning methods in terms of machine learning and deep learning are comparatively summarized. Specifically, the techniques for data preprocessing are reviewed and the most common and powerful features are listed, which are capable of providing a guideline for researchers aiming at developing AF detection algorithms. Finally, we discuss the potential contributors that are probably helpful for screening the atrial fibrillation from a single lead ECG wave.

Abstract P181: Prediction Of Left Ventricular Diastolic Dysfunction Using Electrocardiographic Machine Learning In Asian Young Male Adults

Left ventricular diastolic dysfunction (LVDD) occurs at initial stage of heart failure. Electrocardiographic (ECG) criteria and machine learning for ECG features with or wihtout biological features have been applied successfully to predict LVDD in middle- and old-aged individuals. The purpose of this study is to clarify the performance of machine learning in young adults. In a large sample of 2,206 military males in Hualien, Taiwan, aged 17-43 years, the prevalence of LVDD is 4.26%. Five machine learning classifiers including random forest (RF), support vector machine (SVM), gradient boosting decision tree (GBDT), multi-layer perceptron (MLP) and logistic regression (LR) for the input of 26 ECG features with or without other 6 biological features (age, anthropometrics, and blood pressures) to link the output of LVDD are compared with the corrected QT interval (QTc) calculated by the Bazett’s formula, a traditional ECG criterion for LVDD. The definition of LVDD is based on either one of the echocardiographic criteria: (1) the E/A ratio of the mitral inflow <0.8; (2) the lateral mitral annulus velocity, e’ <10 cm/s; and (3) the E/e’ ratio >14. The area under the receiver operating characteristic (ROC) curve in machine learning of the RF, SVM, GBDT, MLP and LR for ECG only are 84.1%, 78.7%, 77.9%, 77.6% and 75.4%, which are all superior to 64.6% in the QTc interval. If the specificity is fixed around 70-80%, the sensitivity of these mahine learning classifiers for ECG only are 81.0%, 76.2%, 71.4%, 71.4% and 71.4%, which are all higher than 47.6% in the QTc interval. This study suggests that using machine learning for ECG features only to predict LVDD in Asian young adults is reliable and thereby it is possible for people to take an early preventive action on heart failure.

Electrocardiographic Machine Learning to Predict Left Ventricular Diastolic Dysfunction in Asian Young Male Adults

Left ventricular diastolic dysfunction (LVDD) occurs at the initial stage of heart failure. Electrocardiographic (ECG) criteria and machine learning for ECG features have been applied to predict LVDD in middle- and old-aged individuals. The purpose of this study is to clarify the performance of machine learning in young adults. Three machine learning classifiers including random forest (RF), support vector machine (SVM) and gradient boosting decision tree (GBDT) for the input of 26 ECG features with or without 6 other biological features (age, anthropometrics and blood pressures) are compared with the corrected QT (QTc) interval, a traditional ECG criterion for LVDD. The definition of LVDD is based on either one of the following echocardiographic criteria: (1) an E/A ratio of mitral inflow 14. The best areas under the receiver operating characteristic curve were observed in machine learning of the RF for ECG only (84.1%) and of the SVM for all ECG and biological features (82.1%), both of which were superior to the QTc interval (64.6%). If the specificity is chosen to be approximately 75.0%, the sensitivity of the RF for ECG only reaches 81.0% and that of the SVM for all ECG and biological features is raised to 85.7%, both of which are higher than 47.6% by the QTc interval. This study suggests that using machine learning for ECG features only or with other biological features to predict LVDD in young Asian adults is reliable. The proposed methods provide for the early detection of LVDD for young adults and are helpful for taking preventive action on heart failure.

Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults

Mitral valve prolapse (MVP), known as balloon mitral valve, accounts for 2-4% of cases in the general population and is associated with several cardiac sequelae. A few studies have shown suboptimal results using electrocardiographic (ECG) machine learning to identify MVP in middle- or old-aged individuals; however, no studies have focused on young adults. The aim of this study is to develop an ECG-based system through machine learning to predict MVP in young adults. In a large military population of 2,206 males, aged 17-43 years, support vector machine (SVM), logistic regression (LR) and multilayer perceptron (MLP) classifiers are used as machine learning techniques for 26 ECG features and additional 6 simple biological parameters to link the output of MVP compared with a traditional ECG criterion of a negative T-axis in inferior limb leads. In the parasternal long-axis view of echocardiography, MVP is defined as a displacement of the anterior or posterior leaflet of the mitral valve to the mid portions of the annular hinge point >2 mm. The values of the area under the receiver operating characteristic curve are 74.59%, 74.16% and 73.02% in the proposed SVM, LR and MLP classifiers, respectively, which are better than 38.13% in the traditional ECG criterion for MVP. Our machine learning system provides a novel tool for screening MVP among young male adults. The proposed method can be an adjuvant to the physical findings for early detection of MVP prior to a confirmation by echocardiography for young male adults.

P5645Lifetime sex-specific sudden cardiac death prediction using ECG global electrical heterogeneity: the atherosclerosis risk in communities (ARIC) study

Abstract Background Sex-based differences in sudden cardiac death (SCD) exist and screening methods for SCD are inadequate. Purpose To develop sex-specific lifetime risk prediction models using electrocardiographic (ECG) global electrical heterogeneity (GEH) and clinical characteristics. Methods Participants from the Atherosclerosis Risk in Communities study with analyzable ECGs (n=14,725; age, 54.2±5.8 yrs; 55% female, 74% white) were followed up for 24.4 years (median). Traditional ECG and GEH variables were measured on 12-lead ECGs. A Cox regression model was used to develop a prediction model. In women, the final model included race, age, coronary heart disease (CHD), stroke, hypertension, diabetes, smoking, high-density lipoprotein, albumin, uric acid, education level, heart rate, QTc, sum absolute QRST integral, spatial peak QRS-T angle. In men, the final prediction model included age, race, CHD, stroke, hypertension, diabetes, total cholesterol, physical activity, smoking, serum phosphorus, albumin, chronic kidney disease, spatial area QRS-T angle, area spatial ventricular gradient (SVG) elevation and magnitude, and peak SVG magnitude. Results There were a total of 530 SCDs. Our prediction models showed robust prediction of SCD in both sexes [(Harrell's C-statistic women 0.863 (95% CI 0.845–0.882), men 0.786 (95% CI 0.786–0.803)]. In women when ECG and GEH variables were added to clinical variables, the net reclassification improved by 9% (P=0.001) (Table). In men there was no significant reclassification improvement. Net reclassification Lifetime SCD Risk: Clinical + ECG + GEH Variables Women Men <5% 5–15% >15% Total <5% 5–15% >15% Total SCD Cases <5% 82 14 0 96 103 16 0 119 5–15% 7 59 10 76 12 116 12 140 >15% 0 0 20 20 0 5 74 79 Lifetime SCD Risk: Total 89 73 30 192 115 137 86 338 Clinical Variables Only Non-Cases <5% 6,956 131 2 7,089 4,411 264 0 4,675 5–15% 180 509 42 731 210 1,059 48 1,317 >15% 0 28 84 112 0 56 214 270 Total 7,136 668 128 7,932 4,621 1,379 262 6,262 Conclusions We were the first to develop sex-specific lifetime SCD prediction models. The addition of ECG GEH to clinical variables improved SCD risk reclassification in women, but not in men. Prediction of SCD was more accurate in women as compared to men.

Research and Implementation of ECG-Based Biological Recognition Parallelization

Nowadays, biological recognition technologies attract more attention in electrocardiograph (ECG) signals, which vary among different people and are difficult to counterfeit. However, the robustness of recognition cannot be well sustained in the case of diversified application scenarios and huge human crowds. In order to tackle this problem, this paper puts forward a fiducial and non-fiducial mixed feature extraction method, which can effectively complete the multidimensional feature modeling of ECG signal. In addition, this paper proposes a linear discriminant analysis (LDA) based on multiple features (LOMF) algorithm based on ECG mixed feature to solve time-overhead problem of big data training. LOMF includes ECG signal preprocessing, sub-block division, and block training. By combining the MapReduce distributed computing framework and the secondary retrieval method based on the multi-dimensional feature space, LOMF is parallelized to improve recognition rate and computing efficiency at the same time. The experiment results show that, in the diversified scenarios, utilizing ECG mixed feature can return a higher recognition rate than the traditional ECG 1-D feature. Moreover, compared with the traditional LDA and support vector machine algorithms, the precision of LOMF increases by 7%–8%, which depends on the most competitive advantage of using LOMF. LOMF fits MapReduce parallel framework well so it is more effective than traditional algorithms, especially on diversified application scenarios (such as Internet) where the amount of data grows rapidly.

Mobile Cardiac Health-care Monitoring and Notification with Real Time Tachycardia and Bradycardia Arrhythmia Detection

The increasing trend of heart disease has turned the attention of researchers toward the use of portable connected technologies. The necessity of continuous special care for cardiovascular patients is an inevitable fact. In this research, a new wireless electrocardiographic (ECG) signal-monitoring system based on smartphone is presented. This system has two main sections. The first section consists of a sensor which receives ECG signals via an amplifier, then filters and digitizes the signal, and prepares it to be transmitted. The signals are stored, processed, and then displayed in a mobile application. The application alarms in dangerous situations and sends the location of the cardiac patient to family or health-care staff. The results obtained from the analysis of the electrocardiogram signals on 20 different people have been compared with the traditional ECG in hospital by a cardiologist. The signal is instantly transmitted by 200 sample per second to mobile phone. The raw data are processed, the anomaly is detected, and the signal is drawn on the interface in about 70 s. Therefore, the delay is not noticeable by the patient. With respect to rate of data transmission to hospital, different internet connections such as 2G, 3G, 4G, WiFi, WiMax, or Long-Term Evolution (LTE) could be used. Data transmission ranges from 9.6 kbps to 20 Mbps. Therefore, the physician could receive data with no delay. A performance accuracy of 91.62% is obtained from the wireless ECG system. It conforms to the hospital's diagnostic standard system while providing a portable monitoring anywhere at anytime.

Patient-Specific Deep Architectural Model for ECG Classification.

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.

Association of Traditional Cardiovascular Risk Factors With Development of Major and Minor Electrocardiographic Abnormalities: A Systematic Review.

Electrocardiographic (ECG) abnormalities are prevalent in middle aged and are associated with risk of adverse cardiovascular events. It is unclear whether and to what extent traditional risk factors are associated with the development of ECG abnormalities. To determine whether traditional cardiovascular risk factors are associated with the presence or development of ECG abnormalities, we performed a systematic review of the English-language literature for cross-sectional and prospective studies examining associations between traditional cardiovascular risk factors and ECG abnormalities, including major and minor ECG abnormalities, isolated nonspecific ST-segment and T-wave abnormalities, other ST-segment and T-wave abnormalities, QT interval, Q waves, and QRS duration. Of the 202 papers initially identified, 19 were eligible for inclusion. We examined data analyzing risk factor associations with ECG abnormalities in individuals free of cardiovascular disease. For composite major or minor ECG abnormalities, black race, older age, higher blood pressure, use of antihypertensive medications, higher body mass index, diabetes, smoking, and evidence of left ventricular hypertrophy or higher left ventricular mass are the factors most commonly associated with prevalence and incidence. Risk factor associations differ somewhat according to types of specific ECG abnormalities. Because major and minor ECG abnormalities have important and independent prognostic significance, understanding the groups at risk for their development may inform prevention strategies focused on modifiable risk factors to reduce the burden of ECG abnormalities, which may in turn promote CVD prevention.

Effect of Different Pranayamas on Respiratory Sinus Arrhythmia.

Respiratory Sinus Arrhythmia (RSA) is the differential change of Heart Rate (HR) in response to inspiration and expiration. This is a noninvasive sensitive index of parasympathetic cardiac control. To evaluate changes in RSA by utilizing a simple and cost-effective analysis of electrocardiographic (ECG) tracings obtained during performance of four pranayama techniques. Fifty two trained volunteers performed the following pranayamas with different ratios for inspiration and expiration: sukha (1:1), traditional (1:2), pranava (1:3) and savitri (2:1:2:1) and ECG was recorded while performing the techniques with rest period of 5 minutes in-between. HR was calculated and maximum HR during inspiration (Imax), minimum HR during expiration (Emin), differences between Imax and Emin (Δ), percentage differences between I(max) and Emin (Δ%) and expiration: inspiration ratio (E:I) calculated by respective formulae. Statistical analysis was carried out using repeated measures of ANOVA with Tukey-Kramer multiple comparisons test. There were significant differences between groups in all five aspects namely: p= 0.0093 for mean Imax, p = 0.0009 for mean Emin, and p < 0.0001 for Δ HR (I-E), Δ% HR (I-E) and E:I ratio. Pranava pranayama produced the greatest changes in all five comparisons. We suggest that further short and long term studies be undertaken with pranava pranayama in patients to further qualitatively and quantitatively evaluate inherent mechanisms of this simple technique. Addition of these cost-effective techniques to the medical armory will help patients of rhythm disorders and other cardiovascular conditions.

Echocardiographic Assessment of Cardiac Dyssynchrony. Where do We Stand?

During the last two decades there has been an increasing interest in cardiac resynchronization therapy (CRT) in patients with heart failure. Major randomized clinical trials which tested the effectiveness of CRT included patients based on electrocardiographic (ECG) criteria amongst other features. However, there is an assumption that mechanical synchrony, as may be assessed by echocardiography, is the underlying mechanism that improves left ventricular function and finally the clinical outcome. Large multi-centre trials have failed to prove the usefulness of echocardiography in both selection of patients for CRT and identification of non-responders. On the contrary smaller trials from centres where there is high expertise in echo techniques have demonstrated that mechanical dyssynchrony can be assessed by echocardiography and can actually predict the response to CRT in a better way than traditional ECG criteria. In this manuscript, we have analysed current evidence from published data and attempted to provide insight to the field of cardiac dyssynchrony as assessed with echocardiography.

Usefulness of QRS Voltage Correction by Body Mass Index to Improve Electrocardiographic Detection of Left Ventricular Hypertrophy in Patients With Systemic Hypertension

Obesity reduces the accuracy of voltage-based electrocardiographic (ECG) criteria for diagnosis of left ventricular (LV) hypertrophy. We developed a new ECG score for diagnosis of LV hypertrophy, defined either by a typical strain pattern or a product of the Cornell voltage (R wave height in lead aVL plus S wave depth in lead V3) by body mass index >604mm∙kg/m(2). We examined a population of 2,747 untreated hypertensive subjects (mean age 49 ± 11 years) with good quality ECG and echocardiographic tracings. Several traditional ECG criteria for LV hypertrophy were compared with the new score, with echocardiographic LV mass taken as reference. Among the tested criteria, the highest sensitivity combined with specificity was yielded by the new score (sensitivity 36.1%, 95% confidence interval [CI] 32.9 to 39.4; specificity 90.5%, 95% CI 89.1 to 91.8; and accuracy 73.1%, 95% CI 71.5 to 74.8). Prevalence of ECG LV hypertrophy with the new score was 18%. On the basis of comparisons between areas under the receiver operating characteristic curves, the best performance was achieved by the new score with respect to other ECG criteria for LV hypertrophy (all p <0.0001). In conclusion, correction of Cornell voltage by body mass index as a marker of obesity improves the performance of traditional electrocardiography for diagnosis of LV hypertrophy in patients with hypertension.

An effective way of J wave separation based on multilayer NMF.

J wave is getting more and more important in the clinical diagnosis as a new index of the electrocardiogram (ECG) of ventricular bipolar, but its signal often mixed in normal ST segment, using the traditional electrocardiograph, and diagnosed by experience cannot meet the practical requirements. Therefore, a new method of multilayer nonnegative matrix factorization (NMF) in this paper is put forward, taking the hump shape J wave, for example, which can extract the original J wave signal from the ST segment and analyze the accuracy of extraction, showing the characteristics of hump shape J wave from the aspects of frequency domain, power spectrum, and spectral type, providing the basis for clinical diagnosis and increasing the reliability of the diagnosis of J wave.

Validity of the Surface Electrocardiogram Criteria for Right Ventricular Hypertrophy: The MESA-RV Study (Multi-Ethnic Study of Atherosclerosis-Right Ventricle)

The study aimed to assess the diagnostic properties of electrocardiographic (ECG) criteria for right ventricular hypertrophy (RVH) measured by cardiac magnetic resonance imaging (cMRI) in adults without clinical cardiovascular disease. Current ECG criteria for RVH were based on cadaveric dissection in small studies. MESA (Multi-Ethnic Study of Atherosclerosis) performed cMRIs with complete right ventricle (RV) interpretation on 4,062 participants without clinical cardiovascular disease. Endocardial margins of the RV were manually contoured on diastolic and systolic images. The ECG screening criteria for RVH from the 2009 American Heart Association Recommendations for Standardization and Interpretation of the ECG were examined in participants with and without left ventricular (LV) hypertrophy or reduced ejection fraction. RVH was defined using sex-specific normative equations based on age, height, and weight. The study sample with normal LV morphology and function (n = 3,719) was age 61.3 ± 10.0 years, 53.5% female, 39.6% Caucasian, 25.5% African American, 21.9% Hispanic, and 13.0% Asian. The mean body mass index was 27.9 ± 5.0 kg/m2. A total of 6% had RVH, which was generally mild. Traditional ECG criteria were specific (many >95%) but had low sensitivity for RVH by cMRI. The positive predictive values were not sufficiently high as to be clinically useful (maximum 12%). The results did not differ based on age, sex, race, or smoking status, or with the inclusion of participants with abnormal LV mass or function. Classification and regression tree analysis revealed that no combination of ECG variables was better than the criteria used singly. The recommended ECG screening criteria for RVH are not sufficiently sensitive or specific for screening for mild RVH in adults without clinical cardiovascular disease.

Relation of Electrocardiographic Changes in Pulmonary Embolism to Right Ventricular Enlargement

The electrocardiographic (ECG) findings in patients with pulmonary embolism (PE) and no previous cardiopulmonary disease are well documented; however, investigation of the relation of ECG abnormalities to right ventricular (RV) enlargement has been limited. The purpose of the present investigation was to assess further the relation of ECG changes in acute PE to RV cavity enlargement (dilation). The records of patients hospitalized from January 2009 to December 2012 with acute PE and no previous cardiopulmonary disease were reviewed. A total of 289 patients were included. RV cavity enlargement was present in 141 patients (49%). Normal ECG findings were less prevalent in patients with PE and RV enlargement than those with PE and no RV enlargement (35 of 141 [25%] vs 56 of 148 [38%]; p= 0.02). One or more of the traditional ECG manifestations of acute cor pulmonale (S1Q3T3, complete right bundle branch block, P pulmonale, or right axis deviation) was found in 18 of 141 patients (13%) with RV enlargement and 13 of 148 (8.8%) with a normal size RV (p= NS). None of the ECG abnormalities was sensitive for RV enlargement. The specificity of P and QRS abnormalities was high. The positive predictive values were ≤83% or had wide 95% confidence intervals. The negative predictive values ranged from 50% to 61%. In conclusion, ECG findings were not useful for the detection or exclusion of RV cavity enlargement in patients with acute PE.

Coronary CT Angiography Incorporating Doppler-Guided Prospective ECG Gating in Patients with High Heart Rate: Comparison with Results of Traditional Prospective ECG Gating

PurposeAs Doppler ultrasound has been proven to be an effective tool to predict and compress the optimal pulsing windows, we evaluated the effective dose and diagnostic accuracy of coronary CT angiography (CTA) incorporating Doppler-guided prospective electrocardiograph (ECG) gating, which presets pulsing windows according to Doppler analysis, in patients with a heart rate >65 bpm.Materials and Methods119 patients with a heart rate >65 bpm who were scheduled for invasive coronary angiography were prospectively studied, and patients were randomly divided into traditional prospective (n = 61) and Doppler-guided prospective (n = 58) ECG gating groups. The exposure window of traditional prospective ECG gating was set at 30%–80% of the cardiac cycle. For the Doppler group, the length of diastasis was analyzed by Doppler. For lengths greater than 90 ms, the pulsing window was preset during diastole (during 60%–80%); otherwise, the optimal pulsing intervals were moved from diastole to systole (during 30%–50%).ResultsThe mean heart rates of the traditional ECG and the Doppler-guided group during CT scanning were 75.0±7.7 bpm (range, 66–96 bpm) and 76.5±5.4 bpm (range: 66–105 bpm), respectively. The results indicated that whereas the image quality showed no significant difference between the traditional and Doppler groups (P = 0.42), the radiation dose of the Doppler group was significantly lower than that of the traditional group (5.2±3.4mSv vs. 9.3±4.5mSv, P<0.001). The sensitivities of CTA applying traditional and Doppler-guided prospective ECG gating to diagnose stenosis on a segment level were 95.5% and 94.3%, respectively; specificities 98.0% and 97.1%, respectively; positive predictive values 90.7% and 88.2%, respectively; negative predictive values 99.0% and 98.7%, respectively. There was no statistical difference in concordance between the traditional and Doppler groups (P = 0.22).ConclusionDoppler-guided prospective ECG gating represents an improved method in patients with a high heart rate to reduce effective radiation doses, while maintaining high diagnostic accuracy.