Electrocardiographic Analysis Research Articles

Abstract 4138269: The Association Between Automatically Assessed Broad P-wave and Cardiovascular Events in Patients with Cardiovascular Risks

Background: A broad P wave in electrocardiography reflects atrial remodeling and is associated with cardiovascular events. Although there are several reports on the association between a broad P wave and cardiovascular events, the association between automatically assessed P-wave duration and cardiovascular events in cardiovascular high-risk patients has not been clarified. Hypothesis: We hypothesized that a greater automatically assessed P-wave duration would be associated with cardiovascular events in patients with cardiovascular risk factors. Methods: We included 4,654 patients with access to automated electrocardiography (ECG) analysis of those in the Cardiovascular Prognostic Coupling Study in Japan (a registry of subjects with one or more cardiovascular risk factors, excluding patients with atrial fibrillation). Twelve-lead ECG was conducted, and the P-wave duration in each lead was analyzed automatically using the 12-lead ECG Analysis system (Fukuda Denshi, Tokyo). We selected the maximum P wave duration (Pmax) among the 12 leads. The primary endpoint was a composite endpoint (cardiovascular death, nonfatal myocardial infarction/stroke), and the secondary endpoint was heart failure hospitalization. We assessed two P-wave cut-offs (Pmax ≥ 140, 150 msec) in relation to cardiovascular events. Results: There were 218 patients with Pmax ≥140 ms and 123 patients with Pmax ≥150 ms. N-Terminal pro brain natriuretic peptide (NT-proBNP) was significantly higher in patients with Pmax ≥140 ms than in those with Pmax <140 ms (median 134.0 vs. 65.7 pg/mL, p<0.001). NT-ProBNP was also higher in patients with Pmax ≥150 ms than in those with Pmax <150 ms (median 192.0 vs. 66.9 pg/mL, p<0.001). The mean follow-up period was 53 ± 17 months. The primary endpoint occurred in 180 cases and the secondary endpoint in 73 cases. A broad P-wave defined as Pmax ≥140 ms was independently associated with the primary endpoint (hazard ratio [HR] 1.68, 95% confidence interval [CI] 1.04–2.70, p=0.034) but not with the secondary endpoint (HR 1.96, 95% CI 0.99–3.90, p=0.055) after adjustment for covariates including NT-proBNP. A broad P-wave defined as Pmax ≥150 ms was independently associated with both the primary endpoint (HR 2.08, 95% CI 1.06–4.09, p=0.034) and the secondary endpoint (HR 3.22, 95% CI 1.38–7.52, p=0.007). Conclusions: Automatically assessed P-wave duration of Pmax ≥ 150 ms was a significant predictor of cardiovascular events and heart failure.

Abstract 4137989: Notched P-wave by digital ECG analysis was associated with new-onset atrial fibrillation onset and ischemic stroke

Background: A bimodal P wave in lead II reflects left atrial remodeling and is described as a notched P wave. A notched P wave is usually defined as a dip over the smallest unit of electrocardiography (ECG) recording paper, 40 ms, but automated analysis of ECGs has shown that even a notch of 20 ms is associated with cardiovascular events. A notched P wave is also known to predict atrial fibrillation (AF) after catheter ablation. However, the relationship between automatically assessed notched P waves and new-onset AF and ischemic stroke in patients without documented AF has not been clarified. Hypothesis: A notched P-wave by digital ECG analysis was associated with both new-onset AF and ischemic stroke. Methods: We enrolled 4,216 subjects from the Cardiovascular Prognostic Coupling Study in Japan (Coupling Registry) who had one or more cardiovascular risk factors. Twelve-lead electrocardiography was conducted, and the peak-to-peak distance in the M shape was calculated automatically using a 12-lead ECG analysis system. We compared two definitions: P-waves defined as “notched” at the the peak-to-peak (“M shape”) distance in lead II of ≥20 ms or ≥40 ms. New-onset AF was confirmed through routine medical care as well as annual ECGs. We defined the primary endpoint as new-onset AF and the secondary endpoint as ischemic stroke. Results: The mean follow-up period was 53 ± 17 months, during which 17 AF cases developed. When a notched P-wave was defined as ≥20 ms (n = 319), it was a significant predictor of both new-onset AF (hazard ratio [HR] 2.91, 95% confidence interval [CI] 1.41–6.01, p=0.004) and ischemic stroke (HR 3.02, 95% CI 1.56–5.83, P=0.001). A notched P-wave defined as ≥40 ms (n = 63) was also a predictor of new-onset AF (HR 4.57; 95% CI:1.42–14.71, p = 0.011) and ischemic stroke (HR 3.84, 95% CI 1.20–12.29, P=0.024). Conclusions: A notched P-wave by digital ECG analysis was associated with ischemic stroke as well as the new-onset AF.

Association of Electrocardiographic Abnormalities With In-hospital Mortality in Adult Patients With COVID-19 Infection

OBJECTIVES: The study aimed to determine the association of electrocardiographic (ECG) abnormalities and in-hospital mortality of patients with coronavirus disease 2019 (COVID-19) infection admitted in a tertiary care hospital in the Philippines. METHODS: We conducted a retrospective study of confirmed COVID-19–infected patients. Demographic and clinical characteristics and clinical outcomes were extracted from the medical records. Electrocardiographic analysis was derived from the 12-lead electrocardiogram recorded upon admission. The frequencies and distributions of various clinical characteristics were described, and the ECG abnormalities associated with in-hospital mortality were investigated. RESULTS: A total of 163 patients were included in the study; most were female (52.7%) with a median age of 55 years. Sinus rhythm with any ECG abnormality (65%), nonspecific ST and T-wave changes (35%), and sinus tachycardia (22%) were the frequently reported ECG findings. The presence of any ECG abnormality was detected in 78.5% of patients, and it was significantly associated with in-hospital mortality (P = 0.038). The analysis revealed a statistically significant association between in-hospital mortality and having atrial fibrillation or flutter (P = 0.002), supraventricular tachycardia (P = 0.011), ventricular tachycardia (P = 0.011), third-degree atrioventricular block (P = 0.011), T-wave inversion (P = 0.005), and right ventricular hypertrophy (P = 0.011). CONCLUSION: The presence of any ECG abnormality in patients with COVID-19 infection was associated with in-hospital mortality. Electrocardiographic abnormalities that were associated with mortality were atrial fibrillation or flutter, supraventricular tachycardia, ventricular tachycardia, third-degree atrioventricular block, T-wave inversion, and right ventricular hypertrophy. KEYWORDS: COVID-19, electrocardiography, mortality, Philippines

Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection

Electrocardiographic (ECG) R-peak detection is essential for every sensor-based cardiovascular health monitoring system. To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. This comparison is typically performed using tools provided by the waveform database (WFDB) or custom methods. However, many studies fail to provide detailed information on the validation process. The literature also highlights inconsistencies in reporting window size, a crucial parameter used to compare predictions with expert annotations to distinguish false peaks from the true R-peak. Additionally, there is also a need for uniformity in reporting the total number of beats for individual or collective records of the widely used MIT-BIH arrhythmia database. Thus, we aim to review validation methods of various R-peak detection methodologies before their implementation in real time. This review discusses the impact of non-beat annotations when using a custom validation method, allowable window tolerance, the effects of window size deviations, and implications of varying numbers of beats and skipping segments on ECG testing, providing a comprehensive guide for researchers. Addressing these validation gaps is critical as they can significantly affect validatory outcomes. Finally, the conclusion section proposes a structured concept as a future approach, a guide to integrate WFDB R-peak validation tools for testing any QRS annotated ECG database. Overall, this review underscores the importance of complete transparency in reporting testing procedures, which prevents misleading assessments of R-peak detection algorithms and enables fair methodological comparison.

Mitochondrial Oxidative Stress and Cardiac Dysfunction in TERT Knockout Mice

Abstract Background Heart failure (HF) is a major cause of hospitalization in the elderly, with its incidence increasing due to aging and associated risk factors like hypertension, diabetes, and myocardial fibrosis. Aging-related myocardial fibrosis, characterized by alterations in the extracellular matrix and myocardial structure, impairs cardiac function. Mitochondrial oxidative stress plays a pivotal role in cardiac electrical and structural remodeling, necessitating a deeper understanding of its involvement in aging-related heart failure. Purpose The purpose of this study is to investigate the mechanisms of aging-related ventricular electrical and structural remodeling by constructing an aging mouse model. This research aims to provide potential therapeutic targets for heart failure associated with aging. Methods We developed a third-generation homozygous telomerase reverse transcriptase (TERT) knockout mouse model. Comparing third-generation homozygous TERT knockout mice (F3 group) with age-matched wild-type males (WT group). The study encompassed blood pressure measurement, electrocardiographic analysis, echocardiography, ELISA for cardiac biomarkers, ventricular tissue electrophysiology, fibrosis assessment through staining, transcriptomic profiling via RNA-seq, and electron microscopy of ventricular mitochondria. Results Compared to the WT group, the F3 group demonstrated increased P53 and P16 protein expression. Decreased LVEF and FS, along with increased interventricular septum thickness, left ventricular diameter, and left ventricular volume. Elevated serum NT-pro-BNP and troponin I (cTnI) levels, with prolonged QRS duration. Decreased left and right ventricular conduction velocity, accompanied by increased conduction dispersion. Notable elevation in COLI, TGFβ, and α-SMA gene transcription and protein expression in ventricular tissue. Significant differences in mitochondrial oxidative stress signal pathways via RNA-seq analysis. Elevated serum MDA levels and decreased SOD levels, with up-regulated Mn-SOD gene transcription and protein expression, and down-regulated MFN2 and Drp1 gene transcription and protein expression in ventricular tissue. Electron microscopy revealed mitochondrial abnormalities such as loose arrangement, decreased number, swelling, vacuolation, and myofilament disintegration or breakage in the F3 group. Conclusion Third-generation TERT-/- senescent mice exhibit notable cardiac impairments, including electrical remodeling, structural changes, and mitochondrial dysfunction. These findings suggest a potential link between mitochondrial oxidative stress and aging-related heart failure, indicating novel therapeutic opportunities targeting mitochondrial dysfunction for managing such conditions.

Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.

Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs). A cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network-long short-term memory (CNN-LSTM) DLM was employed. HFpEF risk was determined by left ventricular end-diastolic pressure (LVEDP) and clinical symptoms. The DLM was trained by ECGs. LVEDP for each patient was collected through invasive left ventricular catheterisation. Cohort A and B comprised data from individuals at high risk for HFpEF (LVEDP>12mmHg) and low risk for HFpEF (LVEDP≤12mmHg). The model was trained on Cohort A and prospectively validated on Cohort B. A total of 238 patients underwent ECG and left ventricular catheterisation for model training in Cohort A, and 117 patients for validation in Cohort B. The DLM achieved 78% accuracy in assessing HFpEF risk in Cohort A, while in Cohort B, it demonstrated 78% accuracy, 71.9% specificity, and 71.7% sensitivity. In the validation Cohort B, the DLM-identified high-risk HFpEF group exhibited significantly higher prevalence of diabetes (22.03%-11.86%, P<0.01), higher BMI indices (25.92-24.22kg/cm2, P<0.01), and lower usage history of calcium channel blockers (CCB) (11.76%-28.81%, P<0.01) compared with the DLM-identified low-risk HFpEF group. Traditional HFpEF indicators, including B-type natriuretic peptide (BNP) (22-20pg/mL, P=0.71) and E/E' (8.25-8.5, P=0.66), did not exhibit significant differences between the two groups. The DLM offers an accurate, cost-effective tool for HFpEF risk assessment, potentially facilitating early detection and improved clinical management.

A hybrid deep learning network for automatic diagnosis of cardiac arrhythmia based on 12-lead ECG

Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41%, a precision of 99.15%, a specificity of 99.68%, and an F1-Score of 99.21%, indicating its robust performance across different evaluation metrics. Additionally, the model’s performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings.

Myocardial Disarray and Fibrosis across Hypertrophic Cardiomyopathy Stages Associate with ECG Markers of Arrhythmic Risk.

Myocardial disarray, an early feature of hypertrophic cardiomyopathy (HCM) and a substrate for ventricular arrhythmia, is poorly characterised in prehypertrophic sarcomeric variant carriers (SARC+LVH-). Using diffusion tensor cardiac magnetic resonance (DT-CMR) we assessed myocardial disarray and fibrosis in both SARC+LVH- and HCM patients and evaluated the relationship between microstructural alterations and electrocardiographic (ECG) parameters associated with arrhythmic risk. Sixty-two individuals (24 SARC+LVH-, 24 HCM and 14 matched controls) were evaluated with multiparametric CMR including stimulated echo acquisition mode (STEAM) DT-CMR, and blinded quantitative 12-lead ECG analysis. Mean diastolic fractional anisotropy (FA) was reduced in HCM compared to SARC+LVH- and controls (0.49±0.05 vs 0.52±0.04 vs 0.53±0.04, p=0.009), even after adjustment for differences in extracellular volume (ECV) (p=0.038). Both HCM and SARC+LVH- had segments with significantly reduced FA relative to controls (54% vs 25% vs 0%, p=0.002). Multiple repolarization parameters were prolonged in HCM and SARC+LVH-, with corrected JT interval (JTc) being most significant (354±42ms vs 356±26ms vs 314±26ms, p=0.002). Among SARC+LVH-, JTc duration correlated negatively with mean FA (r=-0.6, p=0.002). In HCM, the JTc interval showed a stronger association with ECV (r=0.6 p=0.019) than FA (r=-0.1 p=0.72). JTc discriminated SARC+LVH- from controls (Area-under-the-receiver-operator-curve 0.88, CI 0.76-1.00, p<0.001), and in HCM correlated with the ESC HCM sudden cardiac death risk score (r=0.5, p=0.014). Low diastolic FA, suggestive of myocardial disarray, is present in both SARC+LVH- and HCM. Low FA and raised ECV were associated with repolarization prolongation. Myocardial disarray assessment using DT-CMR and repolarization parameters such as the JTc interval demonstrate significant potential as markers of disease activity in HCM.

Performance of electrocardiographic criteria for detecting LVH in hypertensive Africans.

To evaluate the performance of the currently accepted LVH electrocardiographic (ECG) criteria from ethnic-specific normal values. We included 309 hypertensive subjects with both ECG (12‑leads device) and echocardiographic analysis. The diagnosis of LVH was established by echocardiography from specific norms. Current ECG criteria for the diagnosis of LVH have low performances. Area Under Curve of Sokolow-Lyon, Peguero-Lo Presti, Cornell voltage and Cornell Product were respectively 0.61, 0.65, 0.72 and 0.71. In hypertensive African, the most accurate criterion for diagnosing LVH is Cornell voltage.

Abnormal QRS-T angles in 5796 women and men aged 50–64: an electrocardiographic analysis providing mechanistic insights

IntroductionAbnormal QRS-T angles are prognostic markers for cardiovascular deaths including sudden cardiac death. They occur in ∼5–6% of population-based cohorts but in ∼20% of patients with diabetes. The mechanistic background, electrical activation and/or recovery disturbances, is not known and the topic of this study. MethodsApplying Frank vectorcardiography (VCG) and simultaneously recorded scalar 12‑lead ECG, electrical activation and recovery of abnormal QRS-T angles were studied in 311 participants (5.4%) from a population-based cohort of 5796 women and men in the main Swedish CArdio-Pulmonary bio-Imaging Study (SCAPIS) in Gothenburg. Cut-off values for the peak and mean QRS-T angles were > 124° and > 119°, based on the >95th percentile among all 1080 participants in the pilot SCAPIS and reference values for normal directions (Q1-Q3) from 319 apparently healthy (30%) of them. ResultsOf 311 cases 17% had known cardiac disease. Deviations of QRS and QRSarea-vectors from reference limits (90%) were significantly more common than deviations of T- and Tarea-vectors (65%). Standard ECG signs suggested pathophysiology in 20%; left bundle branch block (LBBB) and voltage criteria for left ventricular hypertrophy being most frequent (9–10%) each. Sub-group analysis of the 30 with LBBB showed very large variability in vector directions. ConclusionsOur observations provide mechanistic insights about abnormal QRS-T angles of potential value for future prognostic and interventional studies. The results also have potential implications for LBB area pacing and the approach to left ventricular hypertrophy.

Cardiac alterations induced by Heloderma horridum horridum venom in rats: An experimental study with ECG analysis using a linear regression algorithm

Envenomation by reptile venom, particularly from lizards, poses significant health risks and can lead to physiological and cardiovascular changes. The venom of Heloderma horridum horridum, endemic to Colima, Mexico, was tested on Wistar rats. Electrocardiographic (ECG) data were collected pre-treatment and at 5-min intervals for 1 h post-envenomation. A specially designed computational linear regression algorithm (LRA) was used for the segmentation analysis of the ECG data to improve the detection of fiducial points (P, Q, R, S, and T) in ECG waves. Additionally, heart tissue was analyzed for macroscopic and microscopic changes. The results revealed significant electrocardiographic alterations, including pacemaker migration, junctional extrasystoles, and intraventricular conduction aberrations. By applying a linear regression algorithm, the study compensated for noise and anomalies in the isoelectric line in an ECG signal, improving the detection of P and T waves and the QRS complex with an efficiency of 97.5%. Cardiac enzyme evaluation indicated no statistically significant differences between the control and experimental groups. Macroscopic and microscopic examination revealed no apparent signs of damage or inflammatory responses in heart tissues. This study enhances our understanding of the cardiovascular impact of Heloderma venom, suggesting a greater influence on changes in conduction and arrhythmias than on direct cardiac damage to the myocardium.

Artificial intelligence and electrocardiography: A modern approach to heart rate monitoring

The integration of Artificial Intelligence (AI) in Electrocardiography (ECG) and Photoplethysmography (PPG) signifies AI's profound influence on heart rate monitoring and analysis. ECG traditionally offers critical insights into cardiac health, necessitating expert interpretation. This study introduces an AI framework with Fast Fourier Transformation Analysis for swift, human-like interpretation of complex ECG signals. A multilayer AI Network accurately detects intricate features, enhancing ECG analysis precision. Leveraging comprehensive datasets, AI models proficiently identify heart dysfunctions like atrial fibrillation and hypertrophic cardiomyopathy, and can estimate age, sex, and race. The proliferation of mobile ECG technologies has spurred AI-based ECG phenotyping, impacting clinical and population health. This research explores AI's role in enhancing cardiac health assessment and clinical decision-making using MATLAB, acknowledging its transformative potential and inherent limitations.

A simple and effective deep neural network based QRS complex detection method on ECG signal.

Introduction: The QRS complex is the most prominent waveform within the electrocardiograph (ECG) signal. The accurate detection of the QRS complex is an essential step in the ECG analysis algorithm, which can provide fundamental information for the monitoring and diagnosis of the cardiovascular diseases. Methods: Seven public ECG datasets were used in the experiments. A simple and effective QRS complex detection algorithm based on the deep neural network (DNN) was proposed. The DNN model was composed of two parts: a feature pyramid network (FPN) based backbone with dual input channels to generate the feature maps, and a location head to predict the probability of point belonging to the QRS complex. The depthwise convolution was applied to reduce the parameters of the DNN model. Furthermore, a novel training strategy was developed. The target of the DNN model was generated by using the points within 75 milliseconds and beyond 150 milliseconds from the closest annotated QRS complexes, and artificial simulated ECG segments with high heart rates were generated in the data augmentation. The number of parameters and floating point operations (FLOPs) of our model was 26976 and 9.90M, respectively. Results: The proposed method was evaluated through a cross-dataset test and compared with the sophisticated state-of-the-art methods. On the MITBIH NST, the proposed method demonstrated slightly better sensitivity (95.59% vs. 95.55%) and lower presicion (91.03% vs. 92.93%). On the CPSC 2019, the proposed method have similar sensitivity (95.15% vs.95.13%) and better precision (91.75% vs. 82.03%). Discussion: Experimental results show the proposed algorithm achieved a comparable performance with only a few parameters and FLOPs, which would be useful for the application of ECG analysis on the wearable device.

A Comprehensive Review on the Electrocardiographic Manifestations of Cardiac Sarcoidosis: Patterns and Prognosis.

Cardiac sarcoidosis (CS) refers to cardiac involvement in sarcoidosis and is usually associated with worse outcomes. This comprehensive review aims to elucidate the electrocardiographic (ECG) signs and features associated with CS, as well as examine modern techniques and their importance in CS evaluation. The exact pathogenesis of CS is still unclear, but it stems from an abnormal immunological response triggered by environmental factors in individuals with genetic predisposition. CS presents with non-cardiac symptoms; however, conduction system abnormalities are common in patients with CS. The most common electrocardiographic (ECG) signs include atrioventricular blocks and ventricular tachyarrhythmia. Distinct patterns, such as fragmented QRS complexes, T-wave alternans, and bundle branch blocks, are critical indicators of myocardial involvement. The application of advanced ECG techniques such as signal-averaged ECG, Holter monitoring, wavelet-transformed ECG, microvolt T-wave alternans, and artificial intelligence-supported analysis holds promising outcomes for opportune detection and monitoring of CS. Timely utilisation of inexpensive and readily available ECG possesses the potential to allow early detection and intervention for CS. The integration of artificial intelligence models into ECG analysis is a promising approach for improving the ECG diagnostic accuracy and further risk stratification of patients with CS.

Estimation of invasive coronary perfusion pressure using electrocardiogram and Photoplethysmography in a porcine model of cardiac arrest

BackgroundCoronary perfusion pressure (CPP) indicates spontaneous return of circulation and is recommended for high-quality cardiopulmonary resuscitation (CPR). This study aimed to investigate a method for non-invasive estimation of CPP using electrocardiography (ECG) and photoplethysmography (PPG) during CPR. MethodsNine pigs were used in this study. ECG, PPG, invasive arterial blood pressure (ABP), and right atrial pressure (RAP) signals were simultaneously recorded. The CPPs were estimated using three datasets: (a) ECG, (b) PPG, and (c) ECG and PPG, and were compared with invasively measured CPPs. Four machine-learning algorithms, namely support vector regression, random forest (RF), K-nearest neighbor, and gradient-boosted regression tree, were used for estimation of CPP. ResultsThe RF model with a combined ECG and PPG dataset achieved better estimation of CPP than the other algorithms. Specifically, the mean absolute error was 4.49 mmHg, the root mean square error was 6.15 mm Hg, and the adjusted R2 was 0.75. A strong correlation was found between the non-invasive estimation and invasive measurement of CPP (r = 0.88), which supported our hypothesis that machine-learning-based analysis of ECG and PPG parameters can provide a non-invasive estimation of CPP for CPR. ConclusionsThis study proposes a novel estimation of CPP using ECG and PPG with machine-learning-based algorithms. Non-invasively estimated CPP showed a high correlation with invasively measured CPP and may serve as an easy-to-use physiological indicator for high-quality CPR treatment.

Methodological identification of anomalies episodes in ECG streams: a systematic mapping study

An electrocardiogram is a medical examination tool for measuring different patterns of heart blood flow circle either in the form of usual or non-invasive patterns. These patterns are useful for the identification of morbidity condition of the heart especially in certain conditions of heart abnormality and arrhythmia. Myocardial infarction (MI) is one of them that happened due to sudden blockage of blood by the cause of malfunction of heart. In electrocardiography (ECG) intensity of MI is highlighted on the basis of unusual patterns of T wave changes. Various studies have contributed for MI through T wave’s classification, but more to the point of T wave has always attracted the ECG researchers. Methodology. This Study is primarily designed for proposing the combination of latest methods that are worked for the solutions of pre-defined research questions. Such solutions are designed in the form of the systematic review process (SLR) by following the Kitchen ham guidance. The literature survey is a two phase’s process, at first phase collect the articles that were published in IEEE Xplore, Scopus, science direct and Springer from 2008 to 2023. It consist of steps; the first level is executed by filtrating the articles on the basis of keyword phase of title and abstract filter. Similarly, at two level the manuscripts are scanned through filter of eligibility criteria of articles selection. The last level belongs to the quality assessment of articles, in such level articles are rectified through evaluation of domain experts. Results. Finally, the selected articles are addressed with research questions and briefly discuss these selected state-of-the-art methods that are worked for the T wave classification. These address units behave as solutions to research problems that are highlighted in the form of research questions. Conclusion and future directions. During the survey process for these solutions, we got some critical observations in the form of gaps that reflected the other directions for researchers. In which feature engineering, different dependencies of ECG features and dimensional reduction of ECG for the better ECG analysis are reflection of future directions.

Pectus carinatum, small pneumothorax, and Brugada patterns: First description and pathophysiological analysis

Pectus carinatum is a chest wall deformity where the breastbone and ribs protrude outward, sometimes referred to as “pigeon chest” due to the bird-like appearance of the chest. This condition occurs in approximately 1 in 1000 children, more commonly affecting boys than girls. Pectus carinatum tends to worsen as a child grows, especially during puberty, with about 15% of the children experiencing associated conditions like scoliosis. We present a case report of a young patient with pectus carinatum who experienced a small right apex pneumothorax and exhibited Brugada patterns on electrocardiogram tracing. This is the first clinical case reporting Brugada pattern presentation in both a patient with pectus carinatum and a small pneumothorax. We have conducted a clinical anatomical and electrocardiographic analysis to explore a physiopathological explanation for these findings, considering that our patient has no history or clinical signs of Brugada Syndrome. To further elucidate the clinical, anatomical, and electrocardiographic findings observed in our patient, detailed analyses were conducted. These investigations focused on how the pectus carinatum may influence electrocardiographic patterns through structural alterations of the thoracic wall, potentially modifying the transmural electrical field across the myocardium. This analysis aimed to explore a physiopathological explanation for the Brugada patterns observed, considering our patient’s unique thoracic anatomy and the absence of clinical Brugada Syndrome. We conclude that: 1. Pectus carinatum may create a window that facilitates the less resistive measurement of cardiac action potentials; 2. Small pneumothoraces can induce modifications in the cardiac electrical signal detected by surface electrodes, and pectus carinatum could unmask these modifications; and 3. Surface electrodes may measure subepicardial potentials that include and are modified by adjacent pericardial tissues, potentially resulting in alterations in transmural potential differences, thus producing fictitious Brugada patterns. Similarly, there is a possibility that the right ventricular outflow tract may be included within the subendocardial potentials.

Evaluation of ventricular repolarization in dermatomyositis and relationship with inflammation and autoimmunity.

Dermatomyositis (DM) is a chronic multi-systemic inflammatory disorder of autoimmune origin, which has been associated with cardiovascular complications, including ventricular arrhythmias and sudden cardiac death. The Tp-e interval and Tp-e/QT ratio have been accepted as new markers for the assessment of myocardial repolarization and ventricular arrhythmogenesis. The aim of this study was to evaluate ventricular repolarization by using Tp-e interval and Tp-e/QT ratio in patients with DM, and to assess the relation with inflammation and autoimmunity. This study included 281 DM patients (180 females, 101 males; mean age 52.73 ± 15.80years) and 281 control subjects (180 females, 101 males; mean age 53.38 ± 15.72years). QTc, Tp-e interval and Tp-e/QT ratio were measured from the 12-lead ECG. The plasma level of blood routine test, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) was measured. These parameters were compared between groups. No statistically significant difference was found between two groups in terms of basic characteristics. In electrocardiographic parameters analysis, QTc, Tp-e interval and Tp-e/QT ratio were significantly increased in DM patients compared to the control group (441.44 ± 26.62ms vs 422.72 ± 11.7ms, 104.16 ± 24.34ms vs 77.23 ± 16.25ms and 0.27 ± 0.06ms vs 0.20 ± 0.04ms, all P value < 0.01). QTc, Tp-e interval and Tp-e/QT were positively correlated with NLR, CRP, and ESR (all P values < 0.01), and were increased in anti-Ro/SSA-52kD positive patients compared to those negative (452.33 ± 24.89ms vs 438.55 ± 26.37ms, 114.05 ± 22.68ms vs 101.53 ± 24.13ms, and 0.29 ± 0.06ms vs 0.27 ± 0.05ms, all P value < 0.01). Our study demonstrated that QTc, Tp-e interval, and Tp-e/QT ratio were increased in DM patients and were associated with inflammatory markers and anti-Ro/SSA-52kD positivity.

Artificial intelligence-enhanced electrocardiography analysis as a promising tool for predicting obstructive coronary artery disease in patients with stable angina.

The clinical feasibility of artificial intelligence (AI)-based electrocardiography (ECG) analysis for predicting obstructive coronary artery disease (CAD) has not been sufficiently validated in patients with stable angina, especially in large sample sizes. A deep learning framework for the quantitative ECG (QCG) analysis was trained and internally tested to derive the risk scores (0-100) for obstructive CAD (QCGObstCAD) and extensive CAD (QCGExtCAD) using 50 756 ECG images from 21 866 patients who underwent coronary artery evaluation for chest pain (invasive coronary or computed tomography angiography). External validation was performed in 4517 patients with stable angina who underwent coronary imaging to identify obstructive CAD. The QCGObstCAD and QCGExtCAD scores were significantly increased in the presence of obstructive and extensive CAD (all P < 0.001) and with increasing degrees of stenosis and disease burden, respectively (all P trend < 0.001). In the internal and external tests, QCGObstCAD exhibited a good predictive ability for obstructive CAD [area under the curve (AUC), 0.781 and 0.731, respectively] and severe obstructive CAD (AUC, 0.780 and 0.786, respectively), and QCGExtCAD exhibited a good predictive ability for extensive CAD (AUC, 0.689 and 0.784). In the external test, the QCGObstCAD and QCGExtCAD scores demonstrated independent and incremental predictive values for obstructive and extensive CAD, respectively, over that with conventional clinical risk factors. The QCG scores demonstrated significant associations with lesion characteristics, such as the fractional flow reserve, coronary calcification score, and total plaque volume. The AI-based QCG analysis for predicting obstructive CAD in patients with stable angina, including those with severe stenosis and multivessel disease, is feasible.

Notched P‐wave on digital electrocardiogram predicts the recurrence of atrial fibrillation in patients who have undergone catheter ablation

AbstractBackgroundA notched P‐wave is associated with the occurrence of atrial fibrillation (AF). However, the association between a notched P‐wave and AF recurrence in patients who have undergone a catheter ablation for AF is unclear.MethodsWe enrolled 100 subjects who underwent catheter ablation for AF (paroxysmal AF: 60 cases; persistent AF: 40 cases). Twelve‐lead electrocardiography (ECG) was conducted, and the peak‐to‐peak distance in the M shape was calculated automatically using a 12‐lead ECG analysis system. A notched P‐wave was defined as a P‐wave with an M‐shape and a peak‐to‐peak distance of ≥20 ms in lead II. We compared the recurrence of AF in the patients with notched P‐wave and the others.ResultsThe mean follow‐up period was 12 ± 8 months, and a recurrence of AF was observed in 28 patients. The recurrence of AF in the notched P‐wave group was significantly higher than that in the controls (log rank 5.14, p = .023). A notched P‐wave was a significant predictor of the recurrence of AF after adjustment for age, gender, history of heart failure, history of catheter ablation, persistent AF, use of antiarrhythmic drugs, and the left atrial volume index (hazard ratio 2.470, 95% confidence interval 1.065–5.728, p = .035).ConclusionsAutomatically identified notched P‐waves with peak‐to‐peak distance ≥20 ms were associated with AF recurrence in patients who had undergone catheter ablation.