Can the introduction of a 12-lead ECG help reduce mortality in those presenting with foot ulceration to multidisciplinary diabetic foot clinics? An observational evaluation of a real-world implementation pilot in England.

AIMS/HYPOTHESIS: The risk of dying within 2 years of presentation with diabetic foot ulceration is over six times the risk of amputation, with CVD the major contributor. Using an observational evaluation of a real-world implementation pilot, we aimed to assess whether for those presenting with diabetic foot ulceration in England, introducing a 12-lead ECG into routine care followed by appropriate clinical action was associated with reduced mortality. METHODS: Between July 2014 and December 2017, ten multidisciplinary diabetic foot services in England participated in a pilot project introducing 12-lead ECGs for new attendees with foot ulceration. Inception coincided with launch of the National Diabetes Footcare Audit (NDFA), whereby all diabetic footcare services in England were invited to enter data on new attendees with foot ulceration. Poisson regression models assessed the mortality RR at 2 and 5 years following first assessment of those receiving care in a participating pilot unit vs those receiving care in any other unit in England, adjusting for age, sex, ethnicity, deprivation, type and duration of diabetes, ulcer severity, and morbidity in the year prior to first assessment. RESULTS: Of the 3110 people recorded in the NDFA at a participating unit during the pilot, 33% (1015) were recorded as having received an ECG. A further 25,195 people recorded in the NDFA had attended another English footcare service. Unadjusted mortality in the pilot units was 16.3% (165) at 2 years and 37.4% (380) at 5 years for those who received an ECG, and 20.5% (430) and 45.2% (950), respectively, for those who did not receive an ECG. For people included in the NDFA at other units, unadjusted mortality was 20.1% (5075) and 42.6% (10,745), respectively. In the fully adjusted model, mortality was not significantly lower for those attending participating units at 2 (RR 0.93 [95% CI 0.85, 1.01]) or 5 years (RR 0.95 [95% CI 0.90, 1.01]). At participating units, mortality in those who received an ECG vs those who did not was lower at 5 years (RR 0.86 [95% CI 0.76, 0.97]), but not at 2 years (RR 0.87 [95% CI 0.72, 1.04]). Comparing just those that received an ECG with attendees at all other centres in England, mortality was lower at 5 years (RR 0.87 [95% CI 0.78, 0.96]), but not at 2 years (RR 0.86 [95% CI 0.74, 1.01]). CONCLUSIONS/INTERPRETATION: The evaluation confirms the high mortality seen in those presenting with diabetic foot ulceration. Overall mortality at the participating units was not significantly reduced at 2 or 5 years, with confidence intervals just crossing parity. Implementation of the 12-lead ECG into the routine care pathway proved challenging for clinical teams-overall a third of attendees had one, although some units delivered the intervention to over 60% of attendees-and the evaluation was therefore underpowered. Nonetheless, the signals of potential mortality benefit among those who had an ECG suggest that units in a position to operationalise implementation may wish to consider this. DATA AVAILABILITY: Data from the National Diabetes Audit can be requested through the National Health Service Digital Data Access Request Service process at: https://digital.nhs.uk/services/data-access-request-service-dars/dars-products-and-services/data-set-catalogue/national-diabetes-audit-nda.

A computational study on the influence of antegrade accessory pathway location on the 12-lead electrocardiogram in Wolff-Parkinson-White syndrome.

Wolff-Parkinson-White syndrome is a cardiovascular disease characterized by abnormal atrio-ventricular conduction facilitated by accessory pathways (APs). Invasive catheter ablation of the AP represents the primary treatment modality. Accurate localization of APs is crucial for successful ablation outcomes, but current diagnostic algorithms based on the 12 lead electrocardiogram (ECG) often struggle with precise determination of AP locations. In order to gain insight into the mechanisms underlying localization failures observed in current diagnostic algorithms, we employ a virtual cardiac model to elucidate the relationship between AP location and ECG morphology. We first introduce a cardiac model of electrophysiology that was specifically tailored to represent antegrade APs in the form of a short atrio-ventricular bypass tract. Locations of antegrade APs were then automatically swept across both ventricles in the virtual model to generate a synthetic ECG database consisting of 9271 signals. Regional grouping of antegrade APs revealed overarching morphological patterns originating from diverse cardiac regions. We then applied variance-based sensitivity analysis relying on polynomial chaos expansion on the ECG database to mathematically quantify how variation in AP location and timing relates to morphological variation in the 12 lead ECG. We utilized our mechanistic virtual model to showcase limitations of AP localization using standard ECG-based algorithms and provide mechanistic explanations through exemplary simulations. Our findings highlight the potential of virtual models of cardiac electrophysiology not only to deepen our understanding of the underlying mechanisms of Wolff-Parkinson-White syndrome but also to potentially enhance the diagnostic accuracy of ECG-based algorithms and facilitate personalized treatment planning.

DAMS-Net: Dual attention and multi-scale information fusion network for 12-lead ECG classification.

Automated 12-lead electrocardiographic (ECG) classification algorithms play an important role in the diagnosis of clinical arrhythmias. Current methods that perform well in the field of automatic ECG classification are usually based on Convolutional Neural Networks (CNN) or Transformer. However, due to the intrinsic locality of convolution operations, CNN can't extract long-dependence between series. On the other side, the Transformer design includes a built-in global self-attention mechanism, but it doesn't pay enough attention to local features. In this paper, we propose DAMS-Net, which combines the advantages of Transformer and CNN, introducing a spatial attention module and a channel attention module using a CNN-Transformer hybrid encoder to adaptively focus on the significant features of global and local parts between space and channels. In addition, our proposal fuses multi-scale information to capture high and low-level semantic information by skip-connections. We evaluate our method on the 2018 Physiological Electrical Signaling Challenge dataset, and our proposal achieves a precision rate of 83.6%, a recall rate of 84.7%, and an F1-score of 0.839. The classification performance is superior to all current single-model methods evaluated in this dataset. The experimental results demonstrate the promising application of our proposed method in 12-lead ECG automatic classification tasks.

Fasciculoventricular accessory pathway masked extensive atrioventricular conduction system disease in a patient with PRKAG2 syndrome.

A 23-year-old male with a history of ventricular pre-excitation and atrial flutter presented for evaluation after recurrent syncope. The possible mechanism of syncope erroneously attributed to pre-excited atrial flutter with fast heart rates in the first hospitalization. The patient was found to have advanced heart block and PRKAG2 genetic mutation in the second hospitalization. The genetic findings and clinical features are consistent with PRKAG2 syndrome (PS). PS is a rare, autosomal dominant inherited disease, characterized by ventricular pre-excitation, supraventricular tachycardia, and cardiac hypertrophy. It is frequently followed by atrial-fibrillation-induced ventricular fibrillation and advanced heart blocks. An accurate differential diagnosis of syncope is important because of the different arrhythmic features and clinical course of PS.

Novel application of convolutional neural networks for artificial intelligence-enabled modified moving average analysis of P-, R-, and T-wave alternans for detection of risk for atrial and ventricular arrhythmias.

BACKGROUND: T-wave alternans (TWA) analysis was shown in >14,000 individuals studied worldwide over the past two decades to be a useful tool to assess risk for cardiovascular mortality and sudden arrhythmic death. TWA analysis by the modified moving average (MMA) method is FDA-cleared and CMS-reimbursed (CAG-00293R2). OBJECTIVE: Because the MMA technique is inherently suitable for dynamic tracking of alternans levels, it was selected for development of artificial intelligence (AI)-enabled algorithms using convolutional neural networks (CNN) to achieve rapid, efficient, and accurate assessment of P-wave alternans (PWA), R-wave alternans (RWA), and TWA. METHODS: The novel application of CNN algorithms to enhance MMA analysis generated efficient and powerful pattern-recognition algorithms for highly accurate alternans quantification. Algorithm reliability and accuracy were verified using simulated ECGs achieving R2 ≥ 0.99 (p < 0.01) in response to noise inputs and artifacts that emulate real-life conditions. RESULTS: Accuracy of the new AI-MMA algorithms in TWA analysis (n = 5) was significantly improved over unsupervised, automated MMA output (p = 0.036) and did not differ from conventional MMA analysis with expert overreading (p = 0.21). Accuracy of AI-MMA in PWA analysis (n = 45) was significantly improved over unsupervised, automated MMA output (p < 0.005) and did not differ from conventional MMA analysis with expert overreading (p = 0.89). TWA and PWA by AI-MMA were correlated with conventional MMA output over-read by an expert reader (R2 = 0.7765, R2 = 0.9504, respectively). CONCLUSION: This novel technique for AI-MMA analysis could be suitable for use in diverse in-hospital and out-of-hospital monitoring systems, including cardiac implantable electronic devices and smartwatches, for tracking atrial and ventricular arrhythmia risk.

Anatomical considerations and clinical interpretation of the 12-lead ECG in the prone position: a prospective multicentre study.

AIMS: The aim of this study is to provide guidance for the clinical interpretation of electrocardiograms (ECGs) in prone position and to establish the electroanatomic explanations for the possible differences to supine position ECGs that may be observed. Additionally, to determine if prone back ECG can be used as an alternative to standard ECG in patients who may benefit from prone position. METHODS AND RESULTS: The ECG in supine (standard ECG), prone back (precordial leads placed on the patient's back), and prone anterior position (precordial leads placed in the standard position with the subjects in prone position) were prospectively examined on 85 subjects. Comparisons of ECG parameters between these positions were performed. Computed tomography (CT) scans were performed in both positions to determine possible electroanatomic aetiologies for prone-associated ECG changes. There were significant differences in QRS amplitude in Leads V1-V5 between supine and prone positions. Q waves were more frequently observed in prone back position vs. supine position (V1: 74.1 vs. 10.6%, P < 0.0001; V2: 23.5 vs. 0%, P < 0.0001, respectively). Flat and inverted T waves were more common in prone back leads (V1: 98 vs. 66%, P < 0.0001; V2: 96 vs. 8%, P < 0.0001; V3: 45 vs. 7%, P < 0.0001). The 3D-CT reconstructions measurements corroborated the significant inverse correlation between QRS amplitude and the distance from the centre of the heart to the estimated lead positions. CONCLUSION: In prone back position ECG, low QRS amplitude should not be misinterpreted as low voltage conditions, neither should Q waves and abnormal T waves are considered anteroseptal myocardial infarction. These changes can be explained by an increased impedance (due to interposing lung tissue) and by the increased distance between the electrodes to the centre of the heart.

A scalogram tensor decomposition based ECG quality assessment.

ECG quality assessment is crucial for reducing false alarms and physician strain in automated diagnosis of cardiovascular diseases. Recent researches have focused on constructing an automatic noisy ECG record rejection mechanism. This work develops a noisy ECG record rejection system using scalogram and Tucker tensor decomposition. The system can reject ECG records, which cannot be analyzed or diagnosed. Scalogram of all 12­lead ECG signals per subject are stacked to form a 3-way tensor. Tucker tensor decomposition is applied with empirical settings to obtain the core tensor. The core tensor is reshaped to form the latent features set. When tested using the PhysioNet challenge 2011 dataset in five-fold cross validation settings, the RusBoost ensemble classifier proved to be a very reliable option, producing an accuracy of 92.4% along with sensitivity of 87.1% and specificity of 93.5%. According to the experimental findings, combining the scalogram with Tucker tensor decomposition yields competitive performance and has the potential to be used in actual evaluation of ECG quality.

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

Steep repolarization time gradients in pig hearts cause distinct changes in composite electrocardiographic T-wave parameters.

BACKGROUND: The T wave of the electrocardiogram (ECG) reflects ventricular repolarization. Repolarization heterogeneity is associated with reentrant arrhythmias. Several T-wave markers (including QT interval) have been associated with ventricular arrhythmias, but studies linking such markers to underlying local repolarization time (RT) inhomogeneities are lacking. We aimed to investigate the relation of several T-wave markers to controlled drug-induced regional RT gradients in intact pig hearts. METHODS: Repolarization time gradients were created by regional infusion of dofetilide and pinacidil in four atrially paced porcine Langendorff-perfused hearts placed inside a torso tank. From the 12-lead ECG on the torso tank, the mean, maximum, and dispersion (max-min) of QTtime , JTtime , Tpeak-end , Twidth , TQratio , dV/dtmax , Tarea , Tamp , and T-upslope duration were determined, as well as upslope end difference between leads V1 and V6 . RESULTS: Temporal T-wave parameters Tpeak-end , Twidth, and TQratio show a significant and high correlation with RT gradient, best reflected by mean value. Tarea (mean, max and dispersion) and dV/dtmax dispersion show only a moderate significant correlation. T-upslope duration shows a significant correlation in particular for mean values. Mean, maximum, or dispersion of QTtime and V1 -V6 upslope end difference were not significantly correlated with RT gradient. CONCLUSION: Composite 12-lead ECG T-wave parameters Tpeak-end , Twidth , TQratio , upslope duration, and Tarea show a good correlation with underlying RT heterogeneity, whereas standard clinical metrics such as QTtime do not reflect local RT heterogeneity. The composite T-wave metrics may thus provide better insights in arrhythmia susceptibility than traditional QTtime metrics.

QRS and cycle length alternans in a patient with idiopathic left ventricular tachycardia: Postulated mechanism and clinical implications.

Idiopathic left ventricular tachycardia is macro-reentrant tachycardia involving the fascicles in the left ventricle as a part of its reentrant circuit. The detailed circuit mechanisms somewhat remain unclear. We reported QRS and cycle length alternans confirmed after the first application of radiofrequency delivery for the distal site of left posterior fascicle potential (P2) in a patient with idiopathic left ventricular tachycardia.

Improving reliability for measuring ST deviation in suspected acute coronary syndrome.

BACKGROUND: Standard 12­lead ECG is used for diagnosis and risk stratification in suspected acute coronary syndrome (ACS) patients. Artifacts have significant impact on the measuring quality, which consequently affect the diagnostic decision. We used a signal quality indicator (SQI) to identify the ECG segments with lower artifact levels which we hypothesized would improve ST measurements. METHODS: The Staff III 12­lead ECG database was used with the ECG segments before balloon inflation (n = 185). SQI scores per second were calculated and a 10-s ECG segment with least noise and artifacts (Clean10) was identified for each minute of recording. The first 10 s of ECG recordings (First10) for each minute were selected as a reference. The Philips DXL™ algorithm was used to measure the ST levels at J-point, +20 ms, +40 ms, +60 ms, and + 80 ms after the J-point. Standard deviations (SDs) for the ST measurements for each of the 185 ECG records were calculated for the Clean10 and for the First10 across records. The resulting SDs for the Clean10 were compared with the SDs for the First10 using the Wilcoxon signed rank test. RESULTS: The results indicated that 1) The SDs for the Clean10 are lower than that of the First10; 2) The SDs for J+20 ms and J+40 ms are lowest among the 5 different measuring points although similar improvement for the Clean10 over the First10 is observed for J+60 ms and J+80 ms as well; 3) The improvement at the J-point was not as high as other ST measurements. CONCLUSIONS: The SQI is demonstrated as an efficient tool to identify the ECG segments with lower artifacts that produce more consistent and reliable ST measurement. The measurements at J+20 ms demonstrated the highest consistency among the five studied measuring points.

Assessment of a new KoMaWo electrode-patch configuration accuracy and review of the literature.

INTRODUCTION: Standard 12­lead electrocardiogram (ECG) is a basic element of routine everyday clinical practice. Traditional cardiac monitoring devices are associated with considerable limitations. Adhesive patches, novel digital solutions, may become a useful diagnostic tool for several cardiovascular diseases. MATERIALS AND METHODS: We propose a new variation of ECG electrodes positioning called KoMaWo. 15 consecutive patients presenting with ST segment deviations due to coronary artery disease were enrolled. The accuracy and utility of the new configuration was assessed and compared with the Mason-Likar configuration, as well as with a standard 12­lead ECG recording. The scans were blinded and interpreted by two independent cardiologists. RESULTS: There were no statistically significant differences in morphology, as well as in the duration of individual waves, complexes, segments, and intervals between the scans obtained using all three methods. In a subgroup analysis, with regard to age, body mass and left ventricle ejection fraction (LVEF), KoMaWo was non-inferior to standard ECG with a 0.2 mm margin. DISCUSSION: The role of traditional cardiac monitoring devices is recognized as the gold standard of patient management. However, certain limitations should be considered. Adhesive patches are light-weight, well-tolerated and do not interfere with daily activities of patients. These novel devices allow for extended monitoring, facilitating increased diagnostic accuracy, regarding cardiac arrhythmias. CONCLUSIONS: The KoMaWo configuration is not inferior to standard electrode placement, nor to Mason-Likar configuration, including its ability to capture ST segment deviations. Adhesive patches may become a valid alternative for traditional cardiac monitoring methods.

ECG reading differences demonstrated on two databases.

Many studies that rely on manual ECG interpretation as a reference use multiple ECG expert interpreters and a method to resolve differences between interpreters, reflecting the fact that experts sometimes use different criteria. The aim of this study was to show the effect of manual ECG interpretation style on training automated ECG interpretation. METHODS: The effect of ECG interpretation style or differing ECG criteria on algorithm training was shown in this study by careful analysis of the changes in algorithm performance when the algorithm was trained on one database and tested on a different database. Morphology related ECG interpretation was summarized in eleven abnormalities such as left bundle branch block (LBBB) and old anterior myocardial infarction (MI). Each of the two databases used in the study had a reference interpretation mapped to those eleven abnormalities. F1 algorithm performance scores across abnormalities were compared for four cases. First, the algorithm was trained and tested on randomly split database A and then trained on the training set of database A and tested on randomly chosen test set of database B. The previous two test cases were repeated for opposite databases, train and test on database B and then train on database B and test on the test set of database A. RESULTS: F1 scores across abnormalities were generally higher when training and testing on the same database. F1 scores were high for bundle branch blocks (BBB) no matter the training and testing database combination. Old anterior MI F1 score dropped for one cross-database comparison and not the other suggesting a difference in manual interpretation. CONCLUSION: For some abnormalities, human experts appear to have used different criteria for ECG interpretation, as evident by the difference between cross-database and within-database performance. Bundle branch blocks appear to be interpreted in a consistent manner.

12-Lead ECG arrhythmia classification using cascaded convolutional neural network and expert feature.

Owing to widely available digital ECG data and recent advances in deep learning techniques, automatic ECG arrhythmia classification based on deep neural network has gained growing attention. However, existing neural networks are mainly validated on single­lead ECG, not involving the correlation and difference between multiple leads, while multiple leads ECG provides more complete description of the cardiac activity in different directions. This paper proposes a 12­lead ECG arrhythmia classification method using a cascaded convolutional neural network (CCNN) and expert features. The one-dimensional (1-D) CNN is firstly designed to extract features from each single­lead signal. Subsequently, considering the temporal correlation and spatial variability between multiple leads, features are cascaded as input to two-dimensional (2-D) densely connected ResNet blocks to classify the arrhythmia. Furthermore, features based on expert knowledge are extracted and a random forest is applied to get a classification probability. Results from both CCNN and expert features are combined using the stacking technique as the final classification result. The method has been validated against the first China ECG Intelligence Challenge, obtaining a final score of 86.5% for classifying 12­lead ECG data with multiple labels into 9 categories.

Synthesis of standard 12­lead electrocardiograms using two-dimensional generative adversarial networks.

This paper proposes a two-dimensional (2D) bidirectional long short-term memory generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs corresponding to four types of signals-left ventricular hypertrophy (LVH), left branch bundle block (LBBB), acute myocardial infarction (ACUTMI), and Normal. It uses a fully automatic end-to-end process to generate and verify the synthetic ECGs that does not require any visual inspection. The proposed model is able to produce synthetic standard 12-lead ECG signals with success rates of 98% for LVH, 93% for LBBB, 79% for ACUTMI, and 59% for Normal. Statistical evaluation of the data confirms that the synthetic ECGs are not biased towards or overfitted to the training ECGs, and span a wide range of morphological features. This study demonstrates that it is feasible to use a 2D GAN to produce standard 12-lead ECGs suitable to augment artificially a diverse database of real ECGs, thus providing a possible solution to the demand for extensive ECG datasets.

The Reconstruction of a 12-Lead Electrocardiogram from a Reduced Lead Set Using a Focus Time-Delay Neural Network.

BACKGROUND: The 12-lead electrocardiogram (ECG) is the gold-standard ECG method used by cardiologists. However, accurate electrode placement is difficult and time consuming, and can lead to incorrect interpretation. OBJECTIVES: The objective of this study was to accurately reconstruct a full 12-lead ECG from a reduced lead set. METHODS: Five-electrode placement was used to generate leads I, II, III, aVL, aVR, aVF and V2. These seven leads served as inputs to the focus time-delay neural network (FTDNN) which derived the remaining five precordial leads (V1, V3-V6). An online archived medical database containing 549 cases of ECG recordings was used to train, validate and test the FTDNN. RESULTS: After removing outliers, the reconstructed leads exhibited correlation values of between 0.8609 and 0.9678 as well as low root mean square error values of between 123 µV and 245 µV across all cases, for both healthy controls and cardiovascular disease subgroups except the bundle branch block disease subgroup. The results of the FTDNN method compared favourably to those of prior lead reconstruction methods. CONCLUSIONS: A standard 12-lead ECG was successfully reconstructed with high quantitative correlations from a reduced lead set using only five electrodes, of which four were placed on the limbs. Less reliance on precordial leads will aid in the reduction of electrode placement errors, ultimately improving ECG lead accuracy and reduce the number of cases that are incorrectly diagnosed.

Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome.

BACKGROUND: Non-invasive screening tools of cardiac function can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with cardiac contractility in the general population. We sought to identify ECG features that are most predictive for real-time screening of reduced left ventricular ejection fraction (LVEF) in the acute care setting. METHODS: We performed a secondary analysis of a prospective, observational cohort study of patients evaluated for suspected acute coronary syndrome. We included consecutive patients in whom an echocardiogram was performed during indexed encounter. We evaluated 554 automated 12-lead ECG features in multivariate linear regression for predicting LVEF. We then used regression trees to identify the most important predictive ECG features. RESULTS: Our final sample included 297 patients (aged 63 ± 15, 45% females). The mean LVEF was 57% ± 13 (IQR 50%-65%). In multivariate analysis, depolarization dispersion in the horizontal plane; global repolarization dispersion; and abnormal temporal indices in inferolateral leads were all independent predictors of LVEF (R2 = 0.452, F = 6.679, p < 0.001). Horizontal QRS axis deviation and prolonged ventricular activation time in left ventricular apex were the most important determinants of reduced LVEF, while global QRS duration was of less importance. CONCLUSIONS: Poor R wave progression in precordial leads with dominant QS pattern in V3 is the most predictive feature of reduced LVEF in suspected ACS. This feature constitutes a simple visual marker to aid clinicians in identifying those with impaired cardiac function.

IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms.

The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model-referred to as IGRNet-is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

Optimal Lead Position in Patch-Type Monitoring Sensors for Reconstructing 12-Lead ECG Signals with Universal Transformation Coefficient.

The aim of this study was to reconstruct a 12-lead electrocardiograph (ECG) with a universal transformation coefficient and find the appropriate electrode position and shape for designing a patch-type ECG sensor. A 35-channel ECG monitoring system was developed, and 14 subjects were recruited for the experiment. A feedforward neural network with one hidden layer was applied to train the transformation coefficient. Three electrode shapes (5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle) were considered for the patch-type ECG sensor. The mean correlation coefficient (CC) and minimum CC methods were applied to evaluate the reconstruction performance. The average CCs between the standard 12-lead ECG and reconstructed 12-lead ECG were 0.860, 0.893, and 0.893 for a 5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle shape. The right-angled triangle showed the highest performance among the considered shapes. The results also suggested that the bottom of the central area of the chest was the most suitable position for attaching the patch-type ECG sensor.

Outflow-tract ventricular tachycardia: Can 12 lead ECG during sinus rhythm identify underlying cardiac sarcoidosis?

BACKGROUND: Patients with outflow tract ventricular tachycardia (OTVT) with normal echocardiogram are labeled as idiopathic VT (IVT). However, a subset of these patients is subsequently diagnosed with underlying cardiac sarcoidosis (CS). OBJECTIVE: Whether electrocardiogram (ECG) abnormalities in sinus rhythm (SR) can differentiate underlying CS from IVT. METHODS: We retrospectively analyzed the SR-ECGs of 42 patients with OTVT/premature ventricular complexes (PVC) and normal echocardiography. All underwent advanced imaging with cardiac magnetic resonance (CMR)/18FDG PET-CT for screening of CS. Twenty-two patients had significant abnormalities in cardiac imaging and subsequently had biopsy-proven CS (Cases). Twenty patients had normal imaging and were categorized as IVT (Controls). SR-ECGs of all patients were analyzed by 2 independent, blinded observers. RESULTS: Baseline characteristics were comparable. Among the ECG features analyzed - fascicular (FB) or bundle branch block (BBB) was seen in 9/22 Cases vs. 1/20 controls (p = 0.01). Among patients without FB or BBB, fragmented QRS (fQRS) was present in 9/13 cases but in none of the controls (p < 0.001). Low voltage QRS was more often seen among cases as compared to controls (10/22 vs. 3/20 p = 0.03). A stepwise algorithm based on these 3 sets of ECG findings helped to diagnose CS among patients presenting with OTVT/PVC with sensitivity of 91%, specificity of 75%, a PPV of 80%, and a NPV of 88%. CONCLUSIONS: In patients presenting with OTVT/PVC: FB/BBB, fQRS, and low QRS voltage on the baseline ECG were more often observed among patients with underlying CS as compared to true IVT. These findings may help to distinguish underlying CS among Cases presenting with OTVT/PVC.