Assessing a Myocardial Area at Risk in Non-ST Elevation Acute Myocardial Infarction Without Wall Motion Abnormalities Using Cardiac Magnetic Resonance and Radionuclide Imaging.

Evaluation of a myocardial area at risk is clinically important because it contributes to clinical decision-making and management of patients with acute myocardial infarction (AMI). Herein, we reported a case of non-ST-elevation AMI (non-STEMI) without wall motion abnormalities on echocardiography, in which the myocardial area at risk was evaluated by two modalities; cardiac magnetic resonance (CMR) and radionuclide imaging. Coronary angiography revealed significant luminal stenosis in the diagonal branch and the obtuse marginal branch. It remained unclear which branch was the culprit. T2-weighted CMR revealed myocardial edema in the left ventricular anterolateral area. Based on the extent of myocardial edema, the patient was diagnosed with non-STEMI in the area corresponding to the diagonal branch. The area exhibiting impaired fatty acid metabolism on iodine-123-beta-methyl-p-iodophenyl penta-decanoic acid (123I-BMIPP) imaging matched well with the area showing myocardial edema on T2-weighted CMR. In conclusion, both CMR and BMIPP imaging are powerful tools in identifying a myocardial area at risk even in non-STEMI without wall motion abnormalities. This should contribute to clinical decision-making and management of patients with AMI.

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data.

The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician's expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

Influence of idiopathic epilepsy on blood pressure and electrocardiography in dogs treated with phenobarbital.

Background and Aim: Dogs with idiopathic epilepsy (IE) experience a shortened lifespan, neurobehavioral changes, and an increased risk of comorbidities during the interictal period. There have been several reports of sudden death in humans with epilepsy, suggesting changes in cardiac rhythm secondary to seizures. In veterinary medicine, there are still no such conclusive studies. The present study aimed to evaluate blood pressure values, electrocardiographic findings, and laboratory parameters in dogs with IE treated with phenobarbital and to correlate these findings with possible cardiac alterations. Materials and Methods: Twenty-one dogs were divided into 11 healthy dogs and 10 idiopathic epileptic dogs for blood analysis, computerized electrocardiogram, and oscillometer-based blood pressure measurement. Results: QRS complex and S-T interval values differed significantly between groups, but blood pressure values were not significantly different. Conclusion: IE can occur with alterations in cardiac conduction and is a pathological condition.

Impact of an online course on enhancing the diagnosis of Chagas disease in Latin America.

Objective: Chagas disease poses a public health problem in Latin America, and the electrocardiogram is a crucial tool in the diagnosis and monitoring of this pathology. In this context, the aim of this study was to quantify the change in the ability to detect electrocardiographic patterns among healthcare professionals after completing a virtual course. Materials and Methods: An asynchronous virtual course with seven pre-recorded classes was conducted. Participants answered the same questionnaire at the beginning and end of the training. Based on these responses, pre and post-test results for each participant were compared. Results: The study included 1656 participants from 21 countries; 87.9% were physicians, 5.2% nurses, 4.1% technicians, and 2.8% medical students. Initially, 3.1% answered at least 50% of the pre-test questions correctly, a proportion that increased to 50.4% after the course (p=0.001). Regardless of their baseline characteristics, 82.1% of course attendees improved their answers after completing the course. Conclusions: The implementation of an asynchronous online course on electrocardiography in Chagas disease enhanced the skills of both medical and non-medical personnel to recognize this condition.

Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging.

OBJECTIVE: In cardiovascular magnetic resonance (MR) imaging, synchronization of image acquisition with heart motion (called gating) is performed by detecting R-peaks in electrocardiogram (ECG) signals. Effective gating is challenging with 3T and 7T scanners, due to severe distortion of ECG signals caused by magnetohydrodynamic effects associated with intense magnetic fields. This work proposes an efficient retrospective gating strategy that requires no prior training outside the scanner and investigates the optimal number of leads in the ECG acquisition set. APPROACH: The proposed method was developed on a data set of 12-lead ECG signals acquired within 3T and 7T scanners. Independent component analysis (ICA) is employed to effectively separate components related with cardiac activity from those associated to noise. Subsequently, an automatic selection process identifies the components best suited for accurate R peak detection, based on heart rate estimation metrics and frequency content quality indexes. MAIN RESULTS: The proposed method is robust to different B0 field strengths, as evidenced by R-peak detection errors of 2.4 ± 3.1 ms and 10.6 ± 15.4 ms for data acquired with 3T and 7T scanners, respectively. Its effectiveness was verified with various subject orientations, showcasing applicability in diverse clinical scenarios. The work reveals that ECG leads can be limited in number to three, or at most five for 7T field strengths, without significant degradation in R-peak detection accuracy. SIGNIFICANCE: The approach requires no preliminary ECG acquisition for R-peak detector training, reducing overall examination time. The gating process is designed to be adaptable, completely blind and independent of patient characteristics, allowing wide and rapid deployment in clinical practice. The potential to employ a significantly limited set of leads enhances patient comfort.

Interpreting Wide-Complex Tachycardia using Artificial Intelligence.

BACKGROUND: Adopting artificial intelligence in medicine may improve speed and accuracy in patient diagnosis. We sought to develop an artificial intelligence (AI) algorithm to interpret wide complex tachycardia (WCT) electrocardiograms (ECG) and compare its diagnostic accuracy to cardiologists. METHODS: Using 3330 WCT ECGs (2906 SVT and 424 VT), we created a training/validation (3131) and test set (199 ECGs). A convolutional neural network (CNN) structure using a modification of differentiable architecture search (DARTS), ZeroLess-DARTS, was developed to differentiate between SVT and VT. RESULTS: The mean accuracy of electrophysiology (EP) cardiologists was 92.5% with a sensitivity of 91.7%, specificity of 93.4%, positive predictive value of 93.7%, negative predictive value of 91.7%. NonEP cardiologists had an accuracy of 73.2 ± 14.4% with a sensitivity, specificity, positive and negative predictive value of 59.8 ± 18.2%, 93.8 ± 3.7%, 93.6 ± 2.3%, and 73.2 ± 14.4%, respectively. AI had superior sensitivity and accuracy (91.9% and 93.0%, respectively) than NonEP cardiologists, and had similar performance of EP cardiologists. Mean time to interpret each ECG varied between 10.1-13.8 seconds for EP cardiologists and 3.1 -16.6 seconds for NonEP cardiologists. Conversely AI required a mean of 0.0092 ± 0.0035 seconds for each ECG interpretation. CONCLUSIONS: AI appears to diagnose WCT with superior accuracy than Cardiologists and similar to those of Electrophysiologists. Using AI to assist with ECG interpretations may improve patient care.

Comparison of electrographic changes, clinical features and outcomes in different variants of Takotsubo syndrom.

BACKGROUND: Dysfunction of the left ventricular (LV) apex (apical variant) is the most common form in Takotsubo syndrome (TS). Several less common non-apical variants have been described - mid-ventricular, basal and focal. We hypothesised that the clinical presentation, and electrocardiographic (ECG) findings may vary between apical and non-apical TS. METHODS: We prospectively identified 194 consecutive patients with TS presenting to Middlemore Hospital, Auckland and obtained clinical, echocardiography, coronary angiography, and long-term follow-up data. ECGs at admission and Day 1 were compared. RESULTS: Of 194 patients with TS, 168 (86.6%) had apical TS, and 26 (13.4%) non-apical TS (11 mid-ventricular TS, 5 basal TS, 10 focal TS). Apical TS patients had more significant LV systolic impairment (p = 0.001) and longer length of stay (p = 0.001). The extent of T-wave inversion (TWI) was similar for both groups on admission (p = 0.88). By Day 1 the extent of TWI was greater in apical TS group (median number of leads 5 vs. 1, p = 0.02). The change in QTc interval between admission and Day 1 was greater in apical TS group (29.7 ms vs. 2.77 ms, p < 0.001). Composite in-hospital complication rate was similar for both groups (13.7% vs. 15.4%, p = 0.77). CONCLUSIONS: Compared with non-apical variants, apical TS patients develop more extensive TWI and greater QT prolongation on ECG, and more significant LV systolic impairment, but in-hospital complications were similar. Clinicians should be aware that there is a sub-group of TS patients who have non-apical regional wall motion abnormalities and who don't develop ECG changes typical of the more common apical variant.

Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram.

BACKGROUND: Convolutional neural networks (CNNs) have emerged as a novel method for evaluating heart failure (HF) in adult electrocardiograms (ECGs). However, such CNNs are not applicable to pediatric HF, where abnormal anatomy of congenital heart defects plays an important role. ECG-based CNNs reflecting neurohormonal activation (NHA) may be a useful marker of pediatric HF. This study aimed to develop and validate an ECG-derived marker of pediatric HF that reflects the risk of future cardiovascular events. METHODS: Based on 21,378 ECGs from 8324 children, a CNN was trained using B-type natriuretic peptide (BNP) and the occurrence of major adverse cardiovascular events (MACEs). The output of the model, or the electrical heart failure indicator (EHFI), was compared with the BNP regarding its ability to predict MACEs in 813 ECGs from 295 children. RESULTS: EHFI achieved a better area under the curve than BNP in predicting MACEs within 180 days (0.826 versus 0.691, p = 0.03). On Cox univariable analyses, both EHFI and BNP were significantly associated with MACE (log10 EHFI: hazard ratio [HR] = 16.5, p < 0.005 and log10 BNP: HR = 4.4, p < 0.005). The time-dependent average precisions of EHFI in predicting MACEs were 32.4%-67.9% and 1.6-7.5-fold higher than those of BNP in the early period. Additionally, the MACE rate increased monotonically with EHFI, whereas the rate peaked at approximately 100 pg/mL of BNP and decreased in the higher range. CONCLUSIONS: ECG-derived CNN is a novel marker of HF with different prognostic potential from BNP. CNN-based ECG analysis may provide a new guide for assessing pediatric HF.

Machine learning based detection of T-wave alternans in real ambulatory conditions.

BACKGROUND AND OBJECTIVE: T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. METHODS: In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K-nearest-neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper-parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. RESULTS: We train ML methods to detect a wide variety of alternant voltage from 20 to 100 µV, i.e., ranging from non-visible micro-alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. CONCLUSIONS: We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.

Characteristics of Patients With Acute Coronary Syndrome and Normal Electrocardiogram.

Background: Patients with acute coronary syndrome (ACS) and normal electrocardiogram (ECG) may have an increased risk of late diagnosis and complications of the disease. Objective: To study the demographic, angiographic and echocardiographic characteristics of patients hospitalized for ACS in whom the ECG was normal on admission to the hospital. Methods: This retrospective study included patients who were hospitalized for ACS without ST-elevation between 2015 and 2023 and who had coronary artery disease (CAD) confirmed by coronary angiography. By further inspection of the electronic databases, patients with ACS who had a normal ECG on admission were filtered out and analyzed separately. Results: Of the total 3137 patients with suspected ACS without ST-elevation, 129 patients (4.1%) were diagnosed as having ACS with a normal ECG. In three patients a non-atherosclerotic cause for the ACS was found. A significantly higher proportion of patients had single-vessel (54.3%) compared to two-vessel (29.5%) and three-vessel (14%) CAD. In addition to a normal ECG, 5.7% of patients with single-vessel CAD and 3.5% of patients with multi-vessel CAD had normal troponin levels and normal regional LV systolic function on echocardiography. Conclusion: Less than 5% of hospitalized patients with ACS without ST-elevation had a normal ECG on admission. The majority of these patients have single-vessel CAD. In about 5% of patients with single-vessel CAD, neither elevated troponin levels nor LV asynergy are detected.

Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network.

Accurately detecting the depolarization QRS complex in the ventricles is a fundamental requirement for cardiovascular disease detection using electrocardiography (ECG). In contrast to traditional signal enhancement algorithms, emerging neural network approaches have shown promise for QRS detection because of their generalizability on complex data. However, the inevitable noise present during ECG recording leads to a decrease in the performance of neural networks. To enhance the robustness and performance of neural network-based QRS detectors, we propose a simulated degeneration unit (SDU)-assisted convolutional neural network (CNN). An SDU simulates the physical degeneration process of interfering optical pulses, which can effectively suppress in-band noise. Through comprehensive performance evaluations on three open-source databases, the SDU-enhanced CNN-based approach demonstrated better performance in detecting QRS complexes than other recently reported QRS detectors. Furthermore, real-world noise injection tests indicate that the optimal noise robustness boundary for the CNN equipped with SDU is 167-300% higher than that for the CNN without SDU.

Fundamental and Practical Feasibility of Electrocardiogram Reconstruction from Photoplethysmogram.

Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned-the ECG R peak and PPG systolic peak are aligned-before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG.

Diagnostic Performance of an Automated Blood Pressure Monitor With an Irregular Heartbeat Algorithm Designed to Detect Atrial Fibrillation.

Background: Early detection of atrial fibrillation (AF) remains an unsolved challenge and because the greatest risk factor for AF is hypertension, blood pressure (BP) monitors with AF detectors have been developed. We evaluated the clinical performance of an irregular heartbeat (IHB) algorithm built into an A&D automated BP monitor for AF diagnosis. Methods and Results: Each of the 239 enrolled patients underwent BP measurement 3 times using the A&D UM-212 with the IHB algorithm. Real-time 3-lead ECG was recorded using automated ECG analysis software. Independent of the ECG analysis software results, 2 cardiologists interpreted the ECG and made the final diagnosis. Of the 239 patients, 135 were in sinus rhythm, 31 had AF, and 73 had non-AF arrhythmias. The respective sensitivity, specificity, and accuracy of the IHB algorithm for AF diagnosis were 98.9%, 91.2%, and 92.2% for the per-measurement evaluation, and 96.8%, 95.7%, and 95.8% for the per-patient evaluation (3/3 positive measurements). The respective sensitivity, specificity, and accuracy of the ECG analysis software for AF diagnosis were 91.4%, 97.9%, and 97.1% for the per-measurement evaluation, and 77.4%, 99.5%, and 96.7% for the per-patient evaluation (3/3 positive measurements). Conclusions: The IHB algorithm built into an A&D automated BP monitor had high diagnostic performance for AF in general cardiology patients, especially when multiple measurements were obtained.

Is Reiki effective in reducing heart rhythm, cortisol and anxiety and improving biochemical parameters in individuals with cardiac disesase? Randomized placebo-controlled trial.

AIMS: The aim of this study was to examine the effect of Reiki in patients with cardiac disease. METHODS AND RESULTS: This study was a single-blind, pre-post-test, randomized, placebo-controlled study. Patients from the cardiology outpatient clinic of a training and research hospital were randomized into three groups: Reiki (n = 22), sham (placebo) (n = 21), and control (no treatment) (n = 22). Data were collected using a personal information form, biochemical parameters, cortisol levels, Beck Anxiety Inventory, and electrocardiography analysis. The Reiki group received Reiki to nine main points for 30 min, while the sham Reiki group received the same points during the same period without starting energy flow. On day two, performed Distance Reiki for 30 minutes. After one week, the researchers administered the Beck Anxiety Inventory, assessed the biochemical parameters and cortisol levels, and analyzed the electrocardiography again. Of the patients, 52.3% were male and 47.7% were female, and the mean age (years) is 60.45 ± 9.67 years. The control group had a significantly higher posttest cortisol level than the other groups (p = 0.002). According to the post-hoc analysis, there was a significant difference between the Reiki versus control groups and sham versus control groups (p = 0.002). The control group had a significantly higher post-test cortisol level than the pre-test cortisol level (p = 0.008). Reiki group had a significantly lower mean posttest Beck Anxiety Inventory score than the other groups (p < 0.001). There was no difference between the electrocardiography results of the groups (p > 0.05). CONCLUSION: Reiki reduces blood cortisol levels and anxiety levels in patient with cardiac diseases.

Electrocardiographic Abnormalities in Patients with Metabolic Dysfunction-Associated Steatotic Liver Disease: A Systematic Review and Meta-Analysis.

INTRODUCTION: This study review aimed to consolidate current knowledge on the electrocardiographic abnormalities observed in patients with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD). METHODS: This was a systematic review of studies on the association between MASLD and electrocardiographic abnormalities, published between January 1, 1946, and October 31, 2023. Data from eligible studies were extracted, analyzed, synthesized, and summarized. RESULTS: We evaluated a total of 27 studies with 8,607,500 participants overall and 1,005,101 participants with MASLD. There was a statistically significant association between MASLD and prevalent atrial fibrillation (pooled OR: 1.34 95% CI: 1.20 - 1.49, p<0.001, n=12), shorter QRS duration (pooled SMD: -0.073, 95% CI: -0.144 - -0.001, n = 2, p=0.048, n=2), QTc prolongation (p<0.001, n=2), LVH (pooled OR: 1.48, 95% CI: 1.25 - 1.75, p<0.001, n=3), low voltage (P<0.001, n=1), ST changes (OR: 1.41, 95% CI: 1.04 - 1.91, p=0.027, n=1), T wave inversion (p<0.001, n=1), axis deviation (OR: 3.21, 95% CI: 1.99 - 5.17, p<0.001, n=1), conduction defect (OR: 2.79, 95% CI: 1.83 - 4.26, p<0.001, n=1) and bundle branch block (OR: 2.90, 95% CI: 1.82 - 4.61, p<0.001, n=1), any persistent heart block (p<0.001, n=1), fragmented QRS (p<0.001, n=1), and P wave diversion (p = 0.0001, n=1) CONCLUSION: MASLD is associated with multiple ECG abnormalities which are potential markers of early cardiac involvement, highlighting the multisystemic nature of MASLD. These specific ECG abnormalities could be used in screening and management algorithms to improve cardiac risk stratification in MASLD patients. PROSPERO REGISTRATION: CRD42023477501.

Functional substrate mapping of atrium in patients with atrial scar: A novel method to predict critical isthmus of atrial tachycardia.

Atrial tachycardia (AT) is a common rhythm disorder, especially in patients with atrial structural abnormalities. Although voltage mapping can provide a general picture of structural alterations which are mainly secondary to prior ablations, surgery or pressure/volume overload, data is scarce regarding the functional characteristics of low voltage regions in the atrium to predict critical isthmus of ATs. Recently, functional substrate mapping (FSM) emerged as a potential tool to evaluate the functionality of structurally altered regions in the atrium to predict critical sites of reentry. Current evidence suggested a clear association between deceleration zones of isochronal late activation mapping (ILAM) during sinus/paced rhythm and critical isthmus of reentry in patients with left AT. Therefore, these areas seem to be potential ablation targets even not detected during AT. Furthermore, abnormal conduction detected by ILAM may also have a role to identify the potential substrate and predict atrial fibrillation outcome after pulmonary vein isolation. Despite these promising findings, the utility of such an approach needs to be evaluated in large-scale comparative studies. In this review, we aimed to share our experience and review the current literature regarding the use of FSM during sinus/paced rhythm in the prediction of re-entrant ATs and discuss future implications and potential use in patients with atrial low-voltage areas.

Estimating Ejection Fraction from the 12 Lead ECG among Patients with Acute Heart Failure.

Background: Identifying patients with low left ventricular ejection fraction (LVEF) in the emergency department using an electrocardiogram (ECG) may optimize acute heart failure (AHF) management. We aimed to assess the efficacy of 527 automated 12-lead ECG features for estimating LVEF among patients with AHF. Method: Medical records of patients >18 years old and AHF-related ICD codes, demographics, LVEF %, comorbidities, and medication were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) identified important ECG features and evaluated performance. Results: Among 851 patients, the mean age was 74 years (IQR:11), male 56% (n=478), and the median body mass index was 29 kg/m2 (IQR:1.8). A total of 914 echocardiograms and ECGs were matched; the time between ECG-Echocardiogram was 9 hours (IQR of 9 hours); ≤30% LVEF (16.45%, n=140). Lasso demonstrated 42 ECG features important for estimating LVEF ≤30%. The predictive model of LVEF ≤30% demonstrated an area under the curve (AUC) of 0.86, a 95% confidence interval (CI) of 0.83 to 0.89, a specificity of 54% (50% to 57%), and a sensitivity of 91 (95% CI: 88% to 96%), accuracy 60% (95% CI:60 % to 63%) and, negative predictive value of 95%. Conclusions: An explainable machine learning model with physiologically feasible predictors may be useful in screening patients with low LVEF in AHF.

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach.

Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.

An interesting electrocardiogram caused by lead reversal.

BACKGROUND: During normal sinus rhythm, atrial depolarization is conducted from right atrium to left atrium through Bachmann's bundle, and a normal P wave axis which is measured on the frontal plane is between 0º and + 75º. The change of P wave polarity is helpful for the analysis of origin point. CASE PRESENTATION: We report a patient with negative P wave in lead I. The characteristics of QRS complex in leads V1 to V6 are helpful to preliminarily differential diagnosis. The 12-lead electrocardiogram (ECG) with correct limb leads (right arm-left arm) placement shows sinus rhythm with complete right bundle branch block (RBBB). CONCLUSIONS: The change of P wave polarity as well as characteristics of QRS complex can help identify limb-lead reversals.