Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation.

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

Prevalence of frequent premature ventricular contractions and left-ventricular systolic dysfunction in patients receiving Holter monitoring.

BACKGROUND: Premature ventricular contractions (PVCs) are frequently observed with left ventricular (LV) systolic dysfunction, although the prevalence of these associated conditions in the general population remains unknown. OBJECTIVE: We sought to understand the prevalence of frequent PVCs (defined PVCs > 5%) and high burden PVCs (defined PVCs > 10%) and LV systolic dysfunction in patients receiving ambulatory Holter monitors (HM). METHODS: A prospective multicenter (eight US medical centers) cross-sectional study collected demographic and PVC burden data from consecutive patients undergoing 24-h, 48-h, and 14-day HM (July 2018-June 2020). Left ventricle ejection fraction (LVEF) data was collected if obtained within 6 months of HM. Four PVC burden groups were analyzed (<1%, 1%-5%, 5.1%-10%, and >10% burden) and stratified by normal LVEF (≥50%) or presence LVEF < 50%. RESULTS: The prevalence of PVC burden of 5.1%-10% and >10% was 4% and 5%, respectively in the population undergoing HM (n = 6529). Age was significantly different between PVC groups (p < .001). In those with LVEF assessment (n = 3713), the prevalence of LVEF < 50% and both LVEF < 50% and PVC > 5% was 16.4% and 4.2%, respectively. The prevalence of PVC > 5% and PVC > 10% in patients with LVEF < 50% was 26% and 16%, respectively. PVC > 5% were more prevalent in older, male, and Caucasians (p < .001). Females had a lower prevalence of PVC > 5% than males (6% vs. 11%; p < .001), but not among those with LVEF < 50% (24% vs. 26%, p = .10). CONCLUSION: PVC > 5% and PVC > 10% and LVEF < 50% are prevalent in patients undergoing HM. PVC > 5% are associated with older age. Females have a lower prevalence of PVC > 5% than males but similar combined PVC > 5% and LVEF < 50%. CLINICALTRIAL: gov identifier: NCT03228823.

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals. METHODS: A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert-Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used. RESULTS: The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods. CONCLUSIONS: The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.

Fine Tuning ECG Interpretation for Young Athletes: ECG Screening Using Z-score-based Analysis.

BACKGROUND: Electrocardiograms (ECGs) in athletes commonly reveal findings related to physiologic adaptations to exercise, that may be difficult to discern from true underlying cardiovascular abnormalities. North American and European societies have published consensus statements for normal, borderline, and abnormal ECG findings for athletes, but these criteria are not based on established correlation with disease states. Additionally, data comparing ECG findings in athletes to non-athlete control subjects are lacking. Our objective was to compare the ECGs of collegiate athletes and non-athlete controls using Z-scores for digital ECG variables to better identify significant differences between the groups and to evaluate the ECG variables in athletes falling outside the normal range. METHODS: Values for 102 digital ECG variables on 7206 subjects aged 17-22 years, including 672 athletes, from Hawaii Pacific Health, University of Hawaii, and Rady Children's Hospital San Diego were obtained through retrospective review. Age and sex-specific Z-scores for ECG variables were derived from normal subjects and used to assess the range of values for specific ECG variables in young athletes. Athletes with abnormal ECGs were referred to cardiology consultation and/or echocardiogram. RESULTS: Athletes had slower heart rate, longer PR interval, more rightward QRS axis, longer QRS duration but shorter QTc duration, larger amplitude and area of T waves, prevalent R' waves in V1, and higher values of variables traditionally associated with left ventricular hypertrophy (LVH): amplitudes of S waves (leads V1-V2), Q waves (V6, III) and R waves (II, V5, V6). Z-scores of these ECG variables in 558 (83%) of the athletes fell within - 2.5 and 2.5 range derived from the normal population dataset, and 60 (8.9%) athletes had a Z-score outside the - 3 to 3 range. While 191 (28.4%) athletes met traditional voltage criteria for diagnosis of LVH on ECG, only 53 athletes (7.9%) had Z-scores outside the range of -2.5 to 2.5 for both S amplitude in leads V1-V2 and R amplitude in leads V5-6. Only one athlete was diagnosed with hypertrophic cardiomyopathy with a Z-score of R wave in V6 of 2.34 and T wave in V6 of -5.94. CONCLUSION: The use of Z-scores derived from a normal population may provide more precise screening to define cardiac abnormalities in young athletes and reduce unnecessary secondary testing, restrictions and concern.

Mitigating effects of agmatine on myocardial infarction in rats subjected to isoproterenol.

Isoproterenol (ISO) usage is limited by its potential for cardiotoxicity. We sought to investigate the potential of agmatine in mitigating ISO-induced cardiotoxicity. Agmatine (100 mg/kg/day) was intraperitoneally administered to Wistar rats for 7 days in the presence or absence of cardiotoxicity induced by subcutaneous injection of ISO (85 mg/kg) on the sixth and seventh days. ECG parameters, lactate dehydrogenase (LDH), malondialdehyde (MDA), and creatinine phosphokinase (CPK) were investigated. Changes in cardiac tissue were also investigated using H&E staining. The heart weight/body weight ratio increased in ISO-treated rats. In the agmatine + ISO group, the increased heart rate observed in ISO-treated rats was reversed (317.2 ± 10.5 vs 452.2 ± 10.61, P < 0.001). Agmatine ameliorated the change in PR, RR, and ST intervals and the QRS complex, which was reduced by ISO. Treatment with saline, ISO, and agmatine had no significant effect on papillary muscle stimulation (P > 0.05). The administration of agmatine to ISO-receiving group could mitigate several parameters when compared to ISO-receiving group including increasing papillary muscle contraction (0.83 vs 0.71 N/M2 respectively, P < 0.01), decreasing LDH levels (660 ng/ml vs 1080 ng/ml, respectively, P < 0.05), decreasing CPK levels (377 U/l vs 642 U/l, respectively, P < 0.05) and decreasing MDA levels (20.32 µM/l vs 46.83 µM/l, P < 0.001). Coadministration of agmatine and ISO is capable of ameliorating ISO cardiotoxicity by antioxidant effects and controlling the hemostasis of calcium in myocytes.

The Effects of Pediatric Acute Lymphoblastic Leukemia Treatment on Cardiac Repolarization.

Background: In recent years, cardiac dysfunction in childhood cancer survivors has become an important issue. Studies are focusing on identifying means for the early identification of patients at risk. Considering this, our study aims to investigate 24-hour Holter electrocardiogram (ECG) repolarization changes throughout doxorubicin (DOX) and cyclophosphamide (CPM) administration in pediatric patients treated for acute lymphoblastic leukemia (ALL). Methods: This was an investigator-driven, single-center, prospective, observational study. Enrolled children had a baseline bedside ECG examination performed before starting chemotherapy (T0). Serial Holter ECG examinations were conducted at three moments during their treatment protocol: day 8 (T1), day 29 (T2), and day 36 (T3). This study evaluated several ECG repolarization parameters, such as the QT interval, corrected QT interval (QTc), and QTc dispersion, as well as ST segment variations. Results: We evaluated 37 children diagnosed with ALL. The T0 examination revealed that over a third of patients had a resting heart rate (HR) outside the normal range for their age and sex. During chemotherapy, statistically significant increases in both HR as well as QT and QTc dispersion values were noticed, especially during the first DOX administration. What is more, a significant increase in the percentage of patients with ST segment depression from T1 to T2 and T3 was noticed. Rhythm disturbances were rare in the study population, with only a few patients presenting ventricular or supraventricular extrasystoles. Conclusions: This study reveals silent repolarization changes occurring early during anticancer treatment in children treated for ALL. These findings could aid in a better understanding of the cardiac toxicity mechanism, and they could potentially improve cardiac risk stratification for oncologic patients. Because of the small number of patients, our results need to be validated by larger studies.

Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm.

In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected electrocardiogram data. The compressed data may be mixed with noise during the transmission process. The denoising-based approximate message passing (AMP) algorithm performs well in reconstructing noisy signals, so the denoising-based AMP algorithm is introduced into electrocardiogram signal reconstruction. The weighted nuclear norm minimization algorithm (WNNM) uses the low-rank characteristics of similar signal blocks for denoising, and averages the signal blocks after low-rank decomposition to obtain the final denoised signal. However, under the influence of noise, there may be errors in searching for similar blocks, resulting in dissimilar signal blocks being grouped together, affecting the denoising effect. Based on this, this paper improves the WNNM algorithm and proposes to use weighted averaging instead of direct averaging for the signal blocks after low-rank decomposition in the denoising process, and validating its effectiveness on electrocardiogram signals. Experimental results demonstrate that the IWNNM-AMP algorithm achieves the best reconstruction performance under different compression ratios and noise conditions, obtaining the lowest PRD and RMSE values. Compared with the WNNM-AMP algorithm, the PRD value is reduced by 0.17∼4.56, the P-SNR value is improved by 0.12∼2.70.

Modified torso vs distal limb electrode placement for performing ECGs in children: A method comparison study.

BACKGROUND: The electrocardiogram (ECG) is routinely performed in children with the limb electrodes positioned on the torso, but few studies have investigated the effects of this modification on the pediatric ECG. Our objective was to assess the agreement between the standard limb lead configuration and a modified torso electrode configuration in normal, healthy children, and to assess the effect of height on that agreement. METHODS: 185 children aged 5-18 years underwent two consecutive 12­lead ECGs, one with standard distal limb lead placement and one with the limb leads placed on the torso. Agreement was assessed for 17 ECG parameters (intervals, axes, and amplitudes) using Bland-Altman plots, height-dependent mean error, and false positive rates. RESULTS: The torso configuration systematically biased the QRS and P wave axes rightwards (towards aVF). Adequate agreement was observed for PR interval and QRS duration, but QTc limits of agreement (±40 ms) were wide. The torso configuration overestimated left-precordial Q, R, and S wave amplitudes and underestimated right-precordial R and S wave amplitudes compared to the distal limb placement. Mean measurement errors increased with the magnitude of the ECG parameter. Mean and variance of measurement errors were more pronounced in shorter children. False positive rates did not differ between the torso and distal limb configurations. CONCLUSION: Modified placement of the limb electrodes onto the torso resulted in multiple differences in the pediatric ECG signals. This may lead to misclassification of electrocardiographic abnormalities, particularly in children with measurement values at the upper limits of normal.

Association between exposure to greenspace and ECG abnormalities in China: a nationwide longitudinal study.

BACKGROUND: Greenspace is an important modifiable environmental factor that is associated with health and well-being. Although previous research has shown that exposure to greenspace is beneficial for cardiovascular diseases, studies have rarely focused on early subclinical health outcomes such as electrocardiogram (ECG) abnormality. METHODS: We developed a longitudinal study using data from the China National Stroke Screening Survey (2013-2019). Monthly exposure to greenspace was assessed by the Normalized Difference Vegetation Index (NDVI) values. ECG abnormality was diagnosed by physicians based on the results of the 12-lead electrocardiogram. We used fixed-effects logistic regression with participant-specific intercepts to estimate the association between exposure to greenspace and ECG abnormalities. RESULTS: A total of 132,108 visits from 61,029 participants with ≥ 2 ECG measurements were included. At baseline, the lag-1 month average NDVI value was 0.29 (SD = 0.15). In the fully adjusted model, per 0.1-unit increment in lag-1 month average NDVI value was associated with a 3.0% (95% CI -1.6 ∼ 7.4%) decreased risk of ECG abnormality. In urban areas, the decreased risk associated with per 0.1-unit increment in NDVI was 14.81% (95% CI 9.38 ∼ 19.92%). CONCLUSION: Our study found that higher exposure to greenspace was associated with a decreased risk of ECG abnormality, especially in urban areas. Our findings suggested that monthly exposure to greenspace might be beneficial to cardiovascular health, and greenspace provision and maintenance could be considered an important public health intervention in urban planning.

Low-Contact Impedance Textile Electrode for Real-Time Detection of ECG Signals.

The quality of the electrocardiography (ECG) signals depends on the effectiveness of the electrode-skin connection. However, current electrocardiogram electrodes (ECGE) often face challenges such as high contact impedance and unstable conductive networks, which hinder accurate measurement during movement and long-term wearability. Herein, in this work, a bionic 3D pile textile as an ECGE with high electrical conductivity and flexibility is prepared by a facile, continuous, and high-efficiency electrostatic self-assembly process. Integrating pile textiles with conductive materials creates a full textile electrode for bioelectrical signal detection that can retain both the inherent characteristics of textiles and high conductivity. Moreover, the dense piles on the textile surface make full contact with the skin, mitigating motion artifacts caused by the sliding between the textile and the skin. The continuous conductive network formed by the interconnected piles allows the pile textile ECGE (PT-ECGE) to function effectively under both static and dynamic conditions. Leveraging the unique pile structure, the PT-ECGE achieves superior flexibility, improved conductivity, low contact impedance, and high adaptivity, washability, and durability. The textile electrode, as a promising candidate for wearable devices, offers enormous application possibilities for the unconscious and comfortable detection of physiological signals.

Athlete's ECG Made Easy: A Practical Guide to Surviving Everyday Clinical Practice.

Electrocardiogram modifications in athletes are common and usually reflect structural and electrical heart adaptations to regular physical training, known as the athlete's heart. However, these electrical modifications sometimes overlap with electrocardiogram findings that are characteristic of various heart diseases. A missed or incorrect diagnosis can significantly impact a young athlete's life and potentially have fatal consequences during exercise, such as sudden cardiac death, which is the leading cause of death in athletes. Therefore, it is crucial to correctly distinguish between expected exercise-related electrocardiogram changes in an athlete and several electrocardiogram abnormalities that may indicate underlying heart disease. This review aims to serve as a practical guide for cardiologists and sports clinicians, helping to define normal and physiology-induced electrocardiogram findings from those borderlines or pathological, and indicating when further investigations are necessary. Therefore, the possible athlete's electrocardiogram findings, including rhythm or myocardial adaptation, will be analyzed here, focusing mainly on the differentiation from pathological findings.

[Implementation of Wearable Wireless Chest Patch Monitoring Terminal].

Objective: A wearable wireless chest patch monitoring terminal is designed to realize the acquisition, processing, and wireless transmission of ECG, respiration, and body temperature signals. Methods: The analog front-end ADS1292R, which integrates respiratory impedance and ECG front-end, is utilized to collect human ECG and respiratory signals. The body temperature is collected using a low-power, high-precision digital temperature sensor MAX30208. A filter algorithm for signal processing and wireless transmission is designed through a low-power nRF52840 Bluetooth SoC with an Arm Cortex-M4F kernel. Results: The experimental results show that the designed monitoring terminal can monitor the ECG, respiration, and body temperature parameters of the human body in real-time and send the monitoring results via Bluetooth, with a continuous working time of more than 13 hours. Conclusion: The wearable wireless chest patch monitoring terminal features good portability, long standby time, and high measurement accuracy, and it has promising application prospects in the fields of family health monitoring, mobile medical treatment, and smart healthcare.

A wavelet subband based LSTM model for 12-lead ECG synthesis from reduced lead set.

Synthesis of a 12-lead electrocardiogram from a reduced lead set has previously been extensively studied in order to meet patient comfort, minimise complexity, and enable telemonitoring. Traditional methods relied solely on the inter-lead correlation between the standard twelve leads for learning the models. The 12-lead ECG possesses not only inter-lead correlation but also intra-lead correlation. Learning a model that can exploit this spatio-temporal information in the ECG could generate lead signals while preserving important diagnostic information. The proposed approach takes leverage of the enhanced inter-lead correlation of the ECG signal in the wavelet domain. Long-short-term memory (LSTM) networks, which have emerged as a powerful tool for sequential data mining, are a type of recurrent neural network architecture with an inherent capability to capture the spatiotemporal information of the heart signal. This work proposes the deep learning architecture that utilizes the discrete wavelet transform and the LSTM to reconstruct a generic 12-lead ECG from a reduced lead set. The experimental results are evaluated using different diagnostic measures and similarity metrics. The proposed framework is well founded, and accurate reconstruction is possible as it can capture clinically significant features and provides a robust solution against noise.

Evaluation of the Relationship between QT Interval in ECG and GRACE Score Amount of Hospitalized Patients with NSTEMI.

Background: Non-ST-elevation myocardial infarction (NSTEMI) is a significant component of acute coronary syndrome (ACS) and typically exhibits a relative incidence that is more than double that of ST-segment elevation myocardial infarction (STEMI). Data obtained from the International Long QT Syndrome Registry indicate that the risk of developing malignant arrhythmias in individuals with long QT syndrome is exponentially associated with the duration of the QTc interval. Therefore, the aim of this study was to assess the potential inclusion of prolonged QTc as a prognostic risk factor in NSTEMI patients. Methods: A cross-sectional study was conducted on patients with NSTEMI diagnosis admitted to the Bu-Ali Hospital of Qazvin between April 2021 and September 2021 by census method. The QT interval was measured in the electrocardiogram at admission. The documented grace score was calculated and its relationship with the corrected QTc interval was estimated using the Hodges formula. Finally, the relationship between QTc and GRACE score was investigated as a prognostic factor in ACS patients. Relationships were assessed by using both the T-test and the chi-square test. Results: A total of 60 patients (31.7% females, 63.8% males) with a mean age of 63 ± 12.7 years were evaluated. Most of the patients (68.3%) were at low risk regarding the Grace score category. In evaluating the relationship between QTc in the electrocardiogram at admission with total GRACE score, the Pearson correlation results were significant and there was a positive relationship between these two factors (r = 0.497, P < 0.001). Conclusion: This study revealed a significant relationship between the QTc interval of patients and the GRACE Score. It was shown patients' QTc can be a predictive factor of patients' mortality.

Cardiovascular effects and clinical outcomes in acute opioid toxicity: A case-control study from Port Said and Damietta Governorates Egypt.

Substance abuse, particularly opioid intoxication, presents a significant public health challenge, leading to severe cardiovascular complications. This case-control study assessed the cardiac profile and clinical outcomes of 51 patients with confirmed acute opioid toxicity, compared to 51 control participants, in general hospitals across Port Said and Damietta governorates, Egypt. The study revealed that opioid-intoxicated patients exhibited significant cardiovascular abnormalities, including hypotension (39.2 %) and electrocardiogram (ECG) changes (72.5 %), with sinus bradycardia (51 %) being the most common. Additionally, echocardiographic abnormalities were found in 40 % of cases, with abnormal regional wall motion and valvular defects observed in several patients. Elevated levels of cardiac enzymes, such as Troponin-I and CK-MB, were significantly correlated with increased ICU stay length and higher mortality rates. The most common morbidities included coma (64.7 %) and shock (39.2 %). The study underscores the critical need for early cardiac assessment in opioid-intoxicated patients to predict clinical outcomes and guide therapeutic interventions.

Real-Time Ambulatory ECG Does Not Expedite Care.

BACKGROUND: Ambulatory ECG (AECG) monitoring is pivotal to the diagnosis of arrhythmias and can be performed with near "real-time" notification of abnormalities. There are limited data on the relative benefit of real-time monitoring compared with traditional Holter monitoring. METHODS AND RESULTS: This is a retrospective observational analysis of University of Utah Health patients who underwent ambulatory ECG studies from 2010 to 2022. The study cohort was stratified by patients with an ambulatory ECG that provides real-time event notification (non-Holter) versus those who do not (Holter). The outcomes were cardiac implantable electronic device procedure, ablation procedure, emergency department/hospitalization visit, and initiation of anticoagulation out to 6 months. We identified 20 259 patients, 16 650 with non-Holter studies and 3609 with Holter studies. Holter patients were younger (mean 52 versus 55, P<0.001), more often women (60.2% versus 57%, P<0.001), and had lower mean CHADS2-VA2Sc scores (1.7 versus 2.1, P<0.001). The median time to ablation procedure was 74 versus 72 (P=0.5), for Holter versus non-Holter, respectively. Median days to new cardiac implantable electronic device implantation was 54 days versus 52 (P=0.6); initiation of anticoagulation among patients not already treated was 42 versus 31 days (P=0.03). Time to first emergency department visit or hospitalization was 63 versus 57 (P=0.6). In multivariable models, there were no significant differences in time to intervention between Holter and non-Holter for each outcome. CONCLUSIONS: Real-time monitoring demonstrates mixed results in terms of reducing time to intervention, with the significant benefit limited to oral anticoagulation initiation. It is time to revisit clinical scenarios where real-time ambulatory monitoring may not improve health care efficiency.

Electrocardiographic correlates of cardiac magnetic resonance findings in women with myocardial infarction with non-obstructive coronary arteries.

BACKGROUND: Myocardial infarction with nonobstructive coronary arteries (MINOCA) occurs in 6-15 % of MI patients. Cardiac magnetic resonance (CMR) imaging identifies MINOCA etiologies, but access may be limited. METHODS: We assessed associations between the index electrocardiogram (ECG) and CMR in MINOCA. Women with MI and < 50 % angiographic stenosis in all vessels were prospectively enrolled at 16 sites. CMR (median 6d from MI) was analyzed for late gadolinium enhancement (LGE), myocardial edema, and wall motion. We assessed ECGs for T-wave inversions (TWI), Q-waves (QW), ST-elevations (STE), ST-depressions (STD), and fragmented QRS complexes (fQRS). We calculated the DETERMINE score (# leads TWI + # fQRS +2*[# QW], excluding aVR, V1). RESULTS: Among 112 women with interpretable ECG, 81.3 % (91/112) had abnormal ECG; 50 % (56/112) had ≥1 TWI. CMR was abnormal in 74.1 % (83/112), with LGE in 49.1 % (55/112) and myocardial edema in 61.6 % (69/112). DETERMINE score ≥ 3 was associated with abnormal CMR (adjusted odds ratio [aOR] aOR 6.06 [1.89, 24.6], p = 0.002) and LGE (aOR 3.10 [1.26, 8.00], p = 0.013), but not edema (aOR 1.86 [0.80, 4.43], p = 0.152). TWI was also associated with abnormal CMR and LGE after adjustment (aOR 3.13 [1.08, 10.1], p = 0.036, aOR 3.23 [1.27, 8.63], p = 0.013, respectively), but not edema (aOR 1.26 [0.54, 2.96], p = 0.589). Specificity for abnormal CMR was 0.83 for DETERMINE score ≥ 3 and 0.75 for TWI. No other ECG findings were associated with CMR abnormality. CONCLUSION: DETERMINE score ≥ 3 and the presence of any TWI were associated with abnormal CMR and with LGE in MINOCA. Our findings demonstrate that the index ECG can provide insight on CMR findings but without sensitivity or specificity required to forgo the CMR. We reaffirm the central role of CMR in elucidating MINOCA pathophysiology.

Screening for Life-Threatening Arrhythmia in Asymptomatic Patients After Paediatric Cardiac Surgery: A Single-Centre Retrospective Analysis of 790 Pre-hospital-discharge 24-h Holter Electocardiogram Recordings.

Severe arrhythmias may occur early after open heart surgery. Because younger patients do not usually show any specific symptoms, presently Holter monitoring is routinely performed for 24 h predischarge at our centre to prevent adverse outcomes. It is unknown whether this test is truly justified in this patient population. Retrospective single-centre analysis of all consecutive patients younger than 19 years old after open heart surgery 2013-2019 who underwent routine Holter monitoring before hospital discharge. Patients with permanent pacemakers and patients who died during this hospital stay were excluded. The cohort was divided into two groups depending on whether severe arrhythmia occurred or not. The study includes 790 Holter recordings from 666 patients with a median age of 0.5 years (IQR 0.23-3.08), performed at a median time of 8 days (IQR 6-15) postoperatively. Postoperative arrhythmia was detected in 554 of 790 24-h Holter recordings (70%); in 47 of 790 (6%), this arrhythmia was classified as severe. The most common severe arrhythmias were premature ventricular contractions (n = 26/47) and long pauses (n = 14/47). A longer aortic cross-clamp time (mean 94.5 (SD ± 53.0) versus 68.1 (SD ± 51.9) min, p = 0.001) was associated with the occurrence of severe postoperative arrhythmia. Severe arrhythmias are rare in predischarge assessments after open heart surgery in children. In current postoperative monitoring at our centre, the diagnostic yield of ECG Holter monitoring for 24 h is too low to justify routine screening in all paediatric patients after open heart surgery.

Efficient multi-view fusion and flexible adaptation to view missing in cardiovascular system signals.

The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning <3 % of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL.