The use of face masks and the impact on heart rate variability during baseline ECG

We compared electrocardiograms (ECGs) findings with one year difference between each other with and without use of face mask at the moment to be tested. The first ECG was done one year before without face mask, and the second ECG with a mask one year later after 3 months of mandatory use for epidemiological COVID-19 pandemic justifications in healthy youth elite athletes.ResultsRegarding heart rate variability (HRV), an increase in RMSSD was recorded when the test was performed with a mask (M): 108.5 ± 90 ms vs. No mask (NM): 72.9 ± 54.2 ms (p <0.002). And also an increase in SDNN, when the test was done with a M: 86.2 ± 47.2 ms vs. NM: 65.9 ± 43.5 ms (p <0.036).ConclusionsThe results on ECG are consistent with the increasing predominance of parasympathetic regulation, which is responsible for regulation of the autonomic loop when the subject is using face mask. (AU)

Intelligent Algorithm-Based Electrocardiography to Predict Atrial Fibrillation after Coronary Artery Bypass Grafting in the Elderly.

The objective of this study was to explore the predictive value of electrocardiogram (ECG) based on intelligent analysis algorithm for atrial fibrillation (AF) in elderly patients undergoing coronary artery bypass grafting (CABG). Specifically, 106 elderly patients with coronary heart disease who underwent CABG in the hospital were selected, including 52 patients with postoperative AF (AF group) and 54 patients without arrhythmia (control group). Within 1-3 weeks after operation, the dynamic ECG monitoring system based on Gentle AdaBoost algorithm constructed in this study was adopted. After the measurement of the 12-lead P wave duration, the maximum P wave duration (Pmax) and minimum P wave duration (Pmin) were recorded. As for simulation experiments, the same data was used as the back-propagation algorithm. The results showed that for the detection accuracy of the test samples, the Gentle AdaBoost algorithm showed 93.7% accuracy after the first iteration, and the Gentle AdaBoost algorithm was 16.1% higher than the back-propagation algorithm. Compared with the control group, the detection rate of arrhythmia in patients after CABG was significantly lower (P < 0.05). Bivariate logistic regression analysis on Pmax and Pmin showed as follows: Pmax: 95% confidential interval (CI): 1.024-1.081, P < 0.05; Pmin: 95% CI: 1.036-1.117, P < 0.05. The sensitivity of Pmax and Pmin in predicting paroxysmal AF was 78.2% and 73.4%, respectively; the specificity of them was 80.1% and 85.6%, respectively; the positive predictive value was 81.2% and 83.4%, respectively; and the negative predictive value was 79.5% and 75.3%, respectively. In conclusion, the generalization ability of Gentle AdaBoost algorithm was better than that of back-propagation algorithm, and it can identify arrhythmia better. Pmax and Pmin were important indicators of AF after CABG.

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, K-means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

Emergent cardiac outcomes in patients with normal electrocardiograms in the emergency department.

BACKGROUND: Emergency physicians (EP) are frequently interrupted to screen electrocardiograms (ECG) from Emergency Department (ED) patients undergoing triage. Our objective was to identify discrepancies between the computer ECG interpretation and the cardiologist ECG interpretation and if any patients with normal ECGs underwent emergent cardiac intervention. We hypothesized that computer-interpreted normal ECGs do not require immediate review by an EP. METHODS: This was a retrospective study of adult (≥ 18 years old) ED patients with computer-interpreted normal ECGs. Laboratory, diagnostic testing and clinical outcomes were abstracted following accepted methodologic guidelines. The primary outcome was emergent cardiac catheterization (within four hours of ED arrival). All ECGs underwent final cardiologist interpretation. When cardiology interpretation differed from the computer (discrepant ECG interpretation), the difference was classified as potentially clinically significant or not clinically significant. Data was described with simple descriptive statistics. MAIN FINDINGS: 989 ECGs interpreted as normal by the computer were analyzed with a mean age of 50.4 ± 16.8 years (range 18-96 years) and 527 (53%) female. Discrepant ECG interpretations were identified in 184 cases including 124 (12.5%, 95% CI 10.4, 14.7%) not clinically significant and 60 (6.1%, 95% CI 4.6, 7.7%) potentially clinically significant. The 60 potentially clinically significant changes included: ST/T wave changes 45 (75%), T wave inversions 6 (10%), prolonged QT 3 (5%), and possible ischemia 10 (17%). Of these 60, 21 (35%) patients were admitted. Six patients had potassium levels >6.0 mEq/L, with one having a potentially clinically significant ECG change. No patient (0%, 95% CI 0, 0.3%) underwent immediate (within four hours) cardiac catherization whereas two underwent delayed cardiac interventions. CONCLUSIONS: Cardiologists frequently disagree with a computer-interpreted normal ECG. Patients with computer-interpreted normal ECGs, however, rarely had significant ischemic events. A rare number of patients will have important cardiac outcomes regardless of the computer-generated normal ECG interpretation. Immediate EP review of the ECG, however, would not have changed these patients' ED courses.

Relationship between impaired repolarization parameters and poor cardiovascular clinical outcomes in patients with potentially serious coronary artery anomalies.

BACKGROUND: Congenital coronary artery anomalies (CCAAs) have the potential for life-threatening complications, including malignant ventricular arrhythmias and sudden cardiac death (SCD). In this study, we aimed to evaluate the relationship between impaired repolarization parameters and poor cardiovascular clinical outcomes in patients with potentially serious CCAAs. METHODS: This retrospective study included 85 potentially serious CCAA patients (mean age: 54.7 ± 13.6 years; male:44) who were diagnosed with conventional and coronary computed tomography angiography (CCTA). All patients underwent transthoracic echocardiography and 12-lead surface electrocardiography. Cardiac events were defined as sustained ventricular tachycardia or fibrillation, syncope, cardiac arrest and SCD. RESULTS: The presence of interarterial course (IAC) was confirmed by CCTA in 37 (43.5%) patients. During a median follow-up time of 24 (18-50) months, a total of 11 (12.9%) patients experienced cardiac events. The presence of IAC was significantly more frequent and Tp-e interval, Tp-e/QTc ratio and frontal QRS/T angle (fQRSTa) were significantly greater in patients with poor clinical outcomes. Moreover, the presence of IAC, high Tp-e/QTc ratio and high fQRSTa were found to be independent predictors of poor clinical outcomes and decreased long-term cardiac event-free survival in these patients. A net reclassification index was +1.0 for the Tp-e/QTc ratio and +1.3 for fQRSTa which were confirmable for additional predictability of these repolarization abnormalities. CONCLUSION: Impaired repolarization parameters, including wider fQRSTa, prolonged Tp-e interval, and increased Tp-e/QTc ratio, and IAC may be associated with poor cardiovascular clinical outcomes in potentially serious CCAA patients.

Utility of using electrocardiogram measures of heart rate variability as a measure of cardiovascular autonomic neuropathy in type 1 diabetes patients.

AIMS/INTRODUCTION: Cardiovascular autonomic neuropathy (CAN) is a predictor of cardiovascular disease and mortality. Cardiovascular reflex tests (CARTs) are the gold standard for the diagnosis of CAN, but might not be feasible in large research cohorts or in clinical care. We investigated whether measures of heart rate variability obtained from standard electrocardiogram (ECG) recordings provide a reliable measure of CAN. MATERIALS AND METHODS: Standardized CARTs (R-R response to paced breathing, Valsalva, postural changes) and digitized 12-lead resting ECGs were obtained concomitantly in Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications participants (n = 311). Standard deviation of normally conducted R-R intervals (SDNN) and the root mean square of successive differences between normal-to-normal R-R intervals (rMSSD) were measured from ECG. Sensitivity, specificity, probability of correct classification and Kappa statistics evaluated the agreement between ECG-derived CAN and CARTs-defined CAN. RESULTS: Participants with CARTs-defined CAN had significantly lower SDNN and rMSSD compared with those without CAN (P < 0.001). The optimal cut-off points of ECG-derived CAN were <17.13 and <24.94 ms for SDNN and rMSSD, respectively. SDNN plays a dominant role in defining CAN, with an area under the curve of 0.73, indicating fair test performance. The Kappa statistic for SDNN was 0.41 (95% confidence interval 0.30-0.51) for the optimal cut-off point, showing fair agreement with CARTs-defined CAN. Combining SDNN and rMSSD optimal cut-off points does not provide additional predictive power for CAN. CONCLUSIONS: These analyses are the first to show the agreement between indices of heart rate variability derived from ECGs and the gold standard CARTs, thus supporting potential use as a measure of CAN in clinical research and clinical care.

Frontal plane QRS-T angle predicts early recurrence of acute atrial fibrillation after successful pharmacological cardioversion with intravenous amiodarone.

WHAT IS KNOWN AND OBJECTIVE: Although restoration of sinus rhythm is the integral part of the atrial fibrillation (Af) management, recurrence frequency of Af is high after cardioversion. However, little is known about the association of electrocardiography (ECG) parameters with Af recurrence after restoration of sinus rhythm. The present study aimed to investigate whether frontal plane QRS-T (fQRST) angle, as a marker of ventricular repolarization heterogeneity, predicts Af recurrence after successful pharmacological cardioversion. METHODS: One hundred and sixty-five paroxysmal Af patients with an acute Af episode who underwent successful pharmacological cardioversion with intravenous amiodarone infusion were included into the study. Patients were divided into two groups according to presence or absence of in-hospital Af recurrence. The association between fQRST angle and Af recurrence was investigated. RESULTS AND DISCUSSION: Af recurrence was observed in 42 (25.4%) patients. The mean fQRST angle was significantly higher in patients with Af recurrence compared to those without Af recurrence (90 ± 45.8 vs. 51 ± 38.2, p < 0.001). Also, Af recurrence was more frequent in patients who had fQRST angle >90˚, compared to patients with fQRST angle ≤90˚ (54.1% vs. 13.7%, p < 0.001). Moreover, ROC curve analysis demonstrated that an increased fQRST angle >92.5˚ predicted in-hospital Af recurrence with a sensitivity of 76.2% and a specificity of 81.4% (AUC:0.728, p < 0.001). Furthermore, multivariate analysis demonstrated that fQRST angle was an independent predictor of in-hospital Af recurrence after successful pharmacological cardioversion (OR: 1.892, 95% CI: 1.361-2.917, p < 0.001). WHAT IS NEW AND CONCLUSION: As a parameter that can be easily calculated from automated ECG recordings, fQRST angle may be useful in the prediction of early Af recurrence after successful pharmacological cardioversion with amiodarone.

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.

Predictors for the prolonged R wave peak time among patients with arterial hypertension.

OBJECTIVE: Hypertension (HT) is prevalent in the general population and is associated with significant cardiovascular adverse events. Major structural and electrical remodeling occurs in the ventricular myocardium in response to the pressure overload. Increased left ventricular mass (LVM) and myocardial fibrosis contribute to the prolongation of the R wave peak time (RWPT), which may indicate electrical remodeling in patients with HT. We evaluated predictors for prolonged RWPT among patients with a previous diagnosis of HT. METHODS: Consecutive patients who had a previous diagnosis of arterial HT and presented to the cardiology clinic for routine visit were included in the study. The standard 12-lead surface electrocardiography (ECG) and transthoracic echocardiography (TTE) was performed on all the patients included in the study for evaluating RWPT and the epicardial fat tissue (EFT). The upper limit for the RWPT was accepted as 40 milliseconds (ms). RESULTS: Between February 2019 and February 2020, 453 patients were screened; and of these, 237 were included in the study. The mean age was 62.1±11.2 years, and 41.8% of the included patients were men. The mean RWPT of the study population was 41.9±10.8. The RWPT was prolonged in 55 patients, and the remaining 172 patients had normal RWPT. In the univariate analysis, EFT (Odds ratio [OR] 1.222; 95% confidence interval [CI] 1.077-1386; p=0.002), the left ventricular mass index (LVMI) (OR 1.011; 95% CI 1.001-1.021; p=0.026), and fragmented QRS (fQRS) (OR 2.679; 95% CI 1.433-5.004; p=0.002) were associated with a prolonged RWPT. Multivariate analysis revealed that only EFT (OR 1.211; 95% CI 1.061-1.383; p=0.005) and fQRS (OR 2.796; 95% CI 1.459-5.359; p=0.002) predicted prolonged RWPT. CONCLUSION: Among the patients with HT, EFT and fQRS predicted prolonged RWPT. These findings may suggest that compared with increased LVM, myocardial fibrosis had a more significant impact on ventricular activation time.

Electrocardiogram as a Lyme Disease Screening Test.

OBJECTIVE: To examine the association between electrocardiographic (ECG) evidence of carditis at the time of Lyme disease evaluation and a diagnosis of Lyme disease. STUDY DESIGN: We performed an 8-center prospective cohort study of children undergoing emergency department evaluation for Lyme disease limited to those who had an ECG obtained by their treating clinicians. The study cardiologist reviewed all ECGs flagged as abnormal by the study sites to assess for ECG evidence of carditis. We defined Lyme disease as the presence of an erythema migrans lesion or a positive 2-tier Lyme disease serology. We used logistic regression to measure the association between Lyme disease and atrioventricular (AV) block or any ECG evidence of carditis. RESULTS: Of the 546 children who had an ECG obtained, 214 (39%) had Lyme disease. Overall, 42 children had ECG evidence of carditis, of whom 24 had AV block (20 first-degree). Of the patients with ECG evidence of carditis, only 21 (50%) had any cardiac symptoms. The presence of AV block (OR 4.7, 95% CI 1.8-12.1) and any ECG evidence of carditis (OR 2.3, 95% CI 1.2-4.3) were both associated with diagnosis of Lyme disease. CONCLUSIONS: ECG evidence of carditis, especially AV block, was associated with a diagnosis of Lyme disease. ECG evidence of carditis can be used as a diagnostic biomarker for Lyme disease to guide initial management while awaiting Lyme disease test results.

Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model.

In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians' confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.

Acute myocardial infarction: Development and application of an ICD-10-CM-based algorithm to a large U.S. healthcare claims-based database.

BACKGROUND: Healthcare administrative claims data hold value for monitoring drug safety and assessing drug effectiveness. The U.S. Food and Drug Administration Biologics Effectiveness and Safety Initiative (BEST) is expanding its analytical capacity by developing claims-based definitions-referred to as algorithms-for populations and outcomes of interest. Acute myocardial infarction (AMI) was of interest due to its potential association with select biologics and the lack of an externally validated International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) algorithm. OBJECTIVE: Develop and apply an ICD-10-CM-based algorithm in a U.S. administrative claims database to identify and characterize AMI populations. METHODS: A comprehensive literature review was conducted to identify validated AMI algorithms. Building on prior published methodology and consistent application of ICD-9-CM codes, an ICD-10-CM algorithm was developed via forward-backward mapping using General Equivalence Mappings and refined with clinical input. An AMI population was then identified in the IBM® MarketScan® Research Databases and characterized using descriptive statistics. RESULTS AND DISCUSSION: Between 2014-2017, 2.83-3.16 individuals/1,000 enrollees/year received ≥1 AMI diagnosis in any healthcare setting. The 2015 transition to ICD-10-CM did not result in a substantial change in the proportion of patients identified. Average patient age at first AMI diagnosis was 64.9 years, and 61.4% of individuals were male. Unspecified chest pain, hypertension, and coronary atherosclerosis of native coronary vessel/artery were most commonly reported within one day of AMI diagnosis. Electrocardiograms were the most common medical procedure and beta-blockers were the most commonly ordered cardiac medication in the one day before to 14 days following AMI diagnosis. The mean length of inpatient stay was 5.6 days (median 3 days; standard deviation 7.9 days). Findings from this ICD-10-CM-based AMI study were internally consistent with ICD-9-CM-based findings and externally consistent with ICD-9-CM-based studies, suggesting that this algorithm is ready for validation in future studies.

Relationship of electrocardiographic changes and severity of acute cerebral ischemic stroke in old patients: A clinical observational study.

ABSTRACT: There was a controversy for the electrocardiogram (ECG) changes and their relationship with disease severity in old patients with acute cerebral ischemic stroke (CIS). This study was aim to provide referential data for this topic.Totally 200 old patients with acute CIS in our hospital from January 2017 to December 2019 were included into this study. According to the ST-T segment changes in ECG, these patients were divided into 3 groups: persistent ischemic group (n = 38), transient ischemic group (n = 106) and non-ischemic group (n = 56). The characteristics and incidence of abnormal ECG and their relationship with disease severity, infarct size and prognosis were respectively analyzed under the severe, moderate and mild type of disease.The ECG changes of patients were mainly characterized by myocardial ischemic ST-T segment changes with a abnormal ECG incidence of 72.00%, the arrhythmia with a abnormal ECG incidence of 9.50%, which were the second most common in clinical features. There were statistically significant differences of myocardial ischemic ST-T segment changes among different disease severity, infarct size and prognosis of acute CIS patients (P < .05). The ischemic ST-T segment changes of ECG reflected that the disease severity, and more ECG abnormalities indicated more severe pathological conditions in CIS patients.The characteristics of ischemic ST-T segment changes have important reference value in the evaluation of severity and prognosis of acute CIS in old patients.

Comparative analysis of ischemic changes in electrocardiogram and coronary angiography results: A retrospective study.

ABSTRACT: To improve the correct diagnosis rate of coronary heart disease and to explore the guiding value of electrocardiogram (ECG) ST-T ischemic changes in the clinical diagnosis of coronary heart disease.A retrospective analysis was conducted on a total of 310 cases who underwent a conventional 12-lead ECG, 12-lead dynamic ECG (DECG, Holter) with ST-T ischemic changes, and then coronary angiography (CA) within 1 week in Qingdao Sttarr Heart Hospital from June 2015 to April 2020 in the study. Ischemic ST-T changes were evaluated using conventional diagnostic criteria, and Judkins diagnostic criteria were used in CA. The sensitivity and specificity of ECG were analyzed.The specificity of ST-T changes in conventional ECG for the diagnosis of coronary heart disease is 33.7% and the sensitivity is 66.0%. The specificity of ST-T changes in Holter in the diagnosis of coronary heart disease is 55.6% and the sensitivity is 32.2%. The sensitivity of conventional ECG for the diagnosis of coronary heart disease is better than Holter, but its specificity is inferior to Holter. The negative likelihood ratios of the 2 ECGs for the diagnosis of coronary heart disease were 1.0 and 1.22, both >0.1, and the positive likelihood ratios were 0.99 and 0.73, both <10. The positive results of ST-T in conventional ECG were 128 males (65.7%), 77 females (66.9%), (P < .05), 148 cases (74.7%) in the group ≥60 years old, and 75 cases in the group less than 60 years (67%), (P > .05). The positive results of ST-T change of DECG were 135 males (69.2%), 69 females (60.0%), (P < .05), 152 cases (78.7%) in the group ≥60 years, and 83 cases (70.9%) in the group less than 60 years, (P > .05). Coronary heart disease-related factors: symptoms, hypertension, diabetes, cancer, family history, smoking history as independent variables, and a binary multivariate logistic regression analysis was performed.The sensitivity of DECG in the diagnosis of myocardial ischemia in women and the elderly was slightly higher than that in men and young cases. ST-T ischemic changes in ECG are more significant for the diagnosis of coronary heart disease in male patients. Smoking, hypertension, diabetes, and family history are all high-risk factors for coronary heart disease.

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.

INTRODUCTION: The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG). METHOD: This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia. RESULTS: Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection. CONCLUSIONS: When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.

The effect of colour on reading performance in children, measured by a sensor hub: From the perspective of gender.

In recent decades reported findings regarding gender differences in reading achievement, cognitive abilities and maturation process in boys and girls are conflicting. As reading is one of the most important processes in the maturation of an individual, the aim of the study was to better understand gender differences between primary school students. The study evaluates differences in Heart Rate Variability (HRV), Electroencephalography (EEG), Electrodermal Activities (EDA) and eye movement of participants during the reading task. Taking into account that colour may affect reading skills, in that it affects the emotional and physiological state of the body, the research attempts to provide a better understanding of gender differences in reading through examining the effect of colour, as applied to reading content. The physiological responses of 50 children (25 boys and 25 girls) to 12 different background and overlay colours of reading content were measured and summarised during the reading process. Our findings show that boys have shorter reading duration scores and a longer Saccade Count, Saccade Duration Total, and Saccade Duration Average when reading on a coloured background, especially purple, which could be caused by their motivation and by the type of reading task. Also, the boys had higher values for the Delta band and the Whole Range of EEG measurements in comparison to the girls when reading on coloured backgrounds, which could reflect the faster maturation of the girls. Regarding EDA measurements we did not find systematic differences between groups either on white or on coloured/overlay background. We found the most significant differences arose in the HRV parameters, namely (SDNN (ms), STD HR (beats/min), RMSSD (ms), NN50 (beats), pNN50 (%), CVRR) when children read the text on coloured/overlay backgrounds, where the girls showed systematically higher values on HRV measurements in comparison to the boys, mostly with yellow, red, and orange overlay colours.

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

Specific Electrocardiograph Intervals Predict Hospitalization with Atrial Fibrillation in Those with Chronic Kidney Disease.

INTRODUCTION: Atrial fibrillation (AF) is common in patients with chronic kidney disease (CKD) and is associated with higher rates of hospitalization compared to those without AF. Whether routine electrocardiographic parameters are predictive of future hospitalizations with AF is not clear. METHODS: The present study is an analysis of a prospective cohort of 2,759 patients without baseline AF from the Chronic Renal Insufficiency Cohort, a large prospective multicenter study of patients with nondialysis-dependent CKD. Unadjusted and adjusted Cox regression models were fit to examine the association of baseline categories of QTc, QRS, and PR intervals with time to first hospitalization with AF. Restricted cubic splines were used to display nonlinear associ-ations. RESULTS: The mean age of subjects at baseline was 58 ± 11 years, 55% were male, and 44% were Black. The mean follow-up was 6.6 years during which 224 participants experienced a hospitalization with AF. The association of baseline QTc interval with risk of AF hospitalization was nonlinear, such that the lowest and highest quartiles of QTc (<407 and >431 ms, respectively) had higher adjusted risk of AF hospitalization, compared with the second quartile (407-416 ms) (aHR Q1:Q2 1.58, 95% CI 1.03-2.41; p = 0.03; aHR Q4:Q2 1.84, 95% CI 1.22-2.78; p < 0.01). Longer QRS was associated with a higher risk of hospitalization with AF among the subgroup of patients with a history of heart failure (HF). PR interval was not associated with AF hospitalization. DISCUSSION/CONCLUSION: The association of QTc with risk for hospitalization with AF among patients with CKD is nonlinear, while the association of longer QRS with AF hospitalization is restricted to patients with baseline HF. Electrocardiography may represent a simple and widely accessible method for risk stratification of future AF in patients with CKD.

Assessment of the causal relevance of ECG parameters for risk of atrial fibrillation: A mendelian randomisation study.

BACKGROUND: Atrial electrical and structural remodelling in older individuals with cardiovascular risk factors has been associated with changes in surface electrocardiographic (ECG) parameters (e.g., prolongation of the PR interval) and higher risks of atrial fibrillation (AF). However, it has been difficult to establish whether altered ECG parameters are the cause or a consequence of the myocardial substrate leading to AF. This study aimed to examine the potential causal relevance of ECG parameters on risk of AF using mendelian randomisation (MR). METHODS AND FINDINGS: Weighted genetic scores explaining lifelong differences in P-wave duration, PR interval, and QT interval were constructed, and associations between these ECG scores and risk of AF were estimated among 278,792 UK Biobank participants (mean age: 57 years at recruitment; 19,132 AF cases). The independent genetic variants contributing to each of the separate ECG scores, and their corresponding weights, were based on published genome-wide association studies. In UK Biobank, genetic scores representing a 5 ms longer P-wave duration or PR interval were significantly associated with lower risks of AF (odds ratio [OR] 0.91; 95% confidence interval [CI]: 0.87-0.96, P = 2 × 10-4 and OR 0.94; 95% CI: 0.93-0.96, P = 2 × 10-19, respectively), while longer QT interval was not significantly associated with AF. These effects were independently replicated among a further 17,931 AF cases from the AFGen Consortium. Investigation of potential mechanistic pathways showed that differences in ECG parameters associated with specific ion channel genes had effects on risk of AF consistent with the overall scores, while the overall scores were not associated with changes in left atrial size. Limitations of the study included the inherent assumptions of MR, restriction to individuals of European ancestry, and possible restriction of results to the normal ECG ranges represented in UK Biobank. CONCLUSIONS: In UK Biobank, we observed evidence suggesting a causal relationship between lifelong differences in ECG parameters (particularly PR interval) that reflect longer atrial conduction times and a lower risk of AF. These findings, which appear to be independent of atrial size and concomitant cardiovascular comorbidity, support the relevance of varying mechanisms underpinning AF and indicate that more individualised treatment strategies warrant consideration.