Search Results (185)

Background/Objectives: Safe discharge after neurosurgical traumatic brain injury (TBI) depends on documented counseling and appropriate referrals, yet real-world fidelity is uncertain in resource-constrained settings. We quantified discharge process quality and identified digitally actionable gaps. Methods: The sample for this study was a retrospective cohort of 559 consecutive neurosurgical TBI patients discharged from a Brazilian public tertiary center (2012–2022). Data were abstracted from electronic health records. The primary outcome was documentation of warning sign counseling at discharge. Proportions are reported with exact Clopper–Pearson 95% confidence intervals. Results: The median age was 66 years (IQR 47–79.5); 78.5% were male and most received care under the public health system. Subdural hematoma predominated; hematoma drainage was the most frequent procedure. Warning sign counseling was documented in 16.1% of cases (89/559; 95% CI 13.2–19.5), and no palliative care referrals were recorded. Conclusions: A low baseline for a safety-critical discharge element exposes an immediately actionable target. Embedding discharge order sets with mandatory counseling fields in the EHR, clinical decision support prompts for palliative care screening and follow-up, and QR-coded patient handouts represent a pragmatic path to improve discharge quality and end-of-life readiness in the digital era. Full article

Background/Objectives: Cardiovascular disease (CVD) remains a leading cause of mortality and disability worldwide, with timely diagnosis critical for preventing long-term functional impairment. Electrocardiograms (ECGs) provide essential biomarkers of cardiac function, but their interpretation is often complex, particularly across multi-institutional datasets. Methods: This study presents an explainable federated learning framework with long short-term memory (FL-LSTM) for multi-class heart disease classification, capable of distinguishing arrhythmia, ischemia, and healthy states while preserving patient privacy. Results: The model was trained and evaluated on three heterogeneous ECG datasets, achieving 92% accuracy, 99% AUC, and 91% F1 score, outperforming existing federated approaches. Model interpretability is provided via SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), highlighting clinically relevant ECG biomarkers such as P-wave height, R-wave height, QRS complex, RR interval, and QT interval. Conclusions: By integrating temporal modeling, federated learning, and interpretable AI, the framework enables secure and collaborative cardiac diagnosis while supporting transparent clinical decision-making in distributed healthcare settings. Full article

Objectives: This paper presents an innovative control framework for the classical Cart–Pole problem. Methods: The proposed framework combines Interval Type-2 Fuzzy Logic, the Dueling Double DQN deep reinforcement learning algorithm, and adaptive reward shaping techniques. Specifically, fuzzy logic acts as an a priori knowledge layer that incorporates measurement uncertainty in both angle and angular velocity, allowing the controller to generate adaptive actions dynamically. Simultaneously, the deep Q-network is responsible for learning the optimal policy. To ensure stability, the Double DQN mechanism successfully alleviates the overestimation bias commonly observed in value-based reinforcement learning. An accelerated convergence mechanism is achieved through a multi-component reward shaping function that prioritizes angle stability and survival. Results: Given the training results, the method stabilizes rapidly; it achieves a 100% success rate by episode 20 and maintains consistent high rewards (650–700) throughout training. While Standard DQN and other baselines take 100+ episodes to become reliable, our method converges in about 20 episodes (4–5 times faster). It is observed that in comparison with advanced baselines like C51 or PER, the proposed method is about 15–20% better in final performance. We also found that PPO and QR-DQN surprisingly struggle on this task, highlighting the need for stability mechanisms. Conclusions: The proposed approach provides a practical solution that balances exploration with safety through the integration of fuzzy logic and deep reinforcement learning. This rapid convergence is particularly important for real-world applications where data collection is expensive, achieving stable performance much faster than existing methods without requiring complex theoretical guarantees. Full article

Background/Objectives: High-altitude hypoxia may affect ECG readings, but it is unclear whether the “live-low–train-high” approach prevents these changes in winter sports athletes. Methods: This cross-sectional study assessed cardiovascular parameters in 102 winter-sport athletes (mean age 20 ± 4 y; 57% women), divided by training altitude into a high-altitude (HA) group (2500–3300 m, n = 70; skiers/snowboarders) and a low-altitude (LA) group (738 m, n = 32; ice hockey/figure skaters). Mid-season assessments included resting ECG, blood pressure, blood biochemistry, and three 24 h dietary recalls. Results: All ECG parameters were physiological, and no significant differences (p < 0.05) were observed in heart rate, PR interval, or QTc between groups. However, HA group exhibited higher systolic blood pressure and a short QT interval. Lactate was significantly higher in HA (p = 0.028). The HA diet contained more saturated fat (p < 0.001), cholesterol (p = 0.013), magnesium (p = 0.003) and potassium (p = 0.001), whereas LA athletes consumed more glucose (p = 0.024). In HA, total energy expenditure correlated positively (p ≥ 0.05) with QRS (ρ = 0.52) and QT (ρ = 0.56), while heart rate correlated inversely with vitamin D (ρ = −0.59). In LA, QTc showed strong inverse correlations with zinc (ρ = −0.62) and selenium (ρ = −0.85). Conclusions: This finding suggests that intermittent high-altitude training did not alter ECG patterns when nutrient intake was adequate. High lactate level and specific nutrient correlations point to a residual physiological load and a modulatory role of electrolytes, B-vitamins, and vitamin D on cardiac repolarisation. Full article

Background/Objectives: Coronary artery disease (CAD) is a leading cause of global mortality, necessitating effective risk stratification tools for optimal patient management. The electrical risk score (ERS) is a multi-parametric index incorporating various electrocardiographic (ECG) parameters, previously shown to predict unfavorable cardiovascular outcomes. However, the relationship between ERS and the presence and severity of CAD remains unclear. This study aimed to investigate the association of ERS with the presence and extent of CAD as assessed by coronary angiography. Methods: This retrospective study included 314 consecutive patients who underwent coronary angiography. ERS was calculated using six ECG parameters: heart rate > 75 bpm, left ventricular hypertrophy, delayed QRS transition zone, frontal QRS-T angle > 90°, prolonged QTc interval, and extended T peak to T end interval. Results: Of the study population (mean age 57.8 ± 11.4, 61.5% male), 158 were diagnosed with CAD, and 156 constituted the control group. The mean ERS was significantly higher in the CAD group than the control group (2.34 ± 1.35 vs. 1.78 ± 1.12, p = 0.006). Among ERS components, delayed QRS transition (p = 0.023), prolonged QTc (p = 0.004), and extended T peak to T end interval (p = 0.001) were notably more prevalent in the CAD group. ERS was independently associated with the presence of CAD on multivariate logistic regression analysis (p < 0.05). Conclusions: ERS is significantly associated with the presence and severity of CAD in stable patients. Elevated ERS, particularly due to delayed QRS transition, prolonged QTc, and extended T peak to T end interval, may serve as a valuable, non-invasive marker for prediction and early identification of CAD. Full article

Background/Objectives: Pregnancy is associated with profound physiological alterations that, together with anemia and spinal anesthesia, may influence myocardial repolarization. The frontal QRS-T [f(QRS-T)] angle has emerged as a reliable electrocardiographic parameter for evaluating repolarization heterogeneity. Materials and Methods: This observational prospective study included 100 term pregnant women [18–45 years, American Society of Anaesthesiologists (ASA) II] undergoing elective cesarean delivery under spinal anesthesia at Sanliurfa Training and Research Hospital between May and August 2025. Participants were divided into two groups: anemic (Hb < 10.5 g/dL, n = 50) and non-anemic (Hb ≥ 10.5 g/dL, n = 50). Standard monitoring and 12-lead ECGs were performed preoperatively and postoperatively. The f(QRS-T) angle was calculated as the absolute difference between QRS and T axes; values > 180° were adjusted by subtracting from 360°. Results: Demographic variables were comparable between groups. No significant differences were observed in mean arterial pressure or heart rate. Preoperative QTc and f(QRS-T) angle values did not differ significantly. However, postoperative QTc was prolonged in the anemic group compared with non-anemic women (426.3 ± 19.2 ms vs. 417.2 ± 20.7 ms, p = 0.026). Likewise, the postoperative f(QRS-T) angle was significantly higher in anemic patients (29.5 [16.0–45.3] vs. 20.5 [9.8–34.5], p = 0.017). Within-group analysis revealed significant postoperative increases in both QTc (p < 0.001) and f(QRS-T) angle (p < 0.001) in the anemic group, but not in controls. Hemoglobin levels correlated negatively with postoperative QTc (r = −0.267, p = 0.008) and f(QRS-T) angle (r = −0.264, p = 0.008). Conclusions: In anemic patients undergoing cesarean delivery under spinal anesthesia, the postoperative QTc interval and f(QRS-T) angle increased significantly compared with both baseline values and non-anemic counterparts. Assessment of the f(QRS-T) angle, a simple and inexpensive ECG-derived parameter, may aid in perioperative risk stratification and enhance patient safety. Full article

The electrocardiogram (ECG) signal carries information crucial for health assessment, but its analysis can be challenging due to noise and signal variability; therefore, automated processing focused on noise removal and detection of key features is necessary. This paper introduces an ECG signal analysis and abnormality detection application developed to process single-lead ECG signals. In this study, the Lobachevsky University database (LUDB) was used as the source of ECG signals, as it includes annotated recordings using a multi-class, multi-label taxonomy that covers several diagnostic categories, each with specific diagnoses that reflect clinical ECG interpretation practices. The main aim of the paper is to provide a tool that efficiently filters noisy ECG data, accurately detects the QRS complex, PQ and QT intervals, calculates heart rate, and compares these values with normal ranges based on age and gender. Additionally, a multi-class, multi-label SVM-based model was developed and integrated into the application for heart abnormality diagnostics, i.e., assigning one or several diagnoses from various diagnostic categories. The MATLAB-based application is capable of processing raw ECG signals, allowing the use of ECG records not only from LUDB but also from other databases. Full article

This paper presents an optimized hybrid deep learning model for power load forecasting—QR-FMD-CNN-BiGRU-Attention—that integrates similar day selection, load decomposition, and deep learning to address the nonlinearity and volatility of power load data. Firstly, the original data are classified using Gaussian Mixture Clustering optimized by ICPO (ICPO-GMM), and similar day samples consistent with the predicted day category are selected. Secondly, the load data are decomposed into multi-scale components (IMFs) using feature mode decomposition optimized by ICPO (ICPO-FMD). Then, with the IMFs as targets, the quantile interval forecasting is trained using the CNN-BiGRU-Attention model optimized by ICPO. Subsequently, the forecasting model is applied to the features of the predicted day to generate interval forecasting results. Finally, the model’s performance is validated through comparative evaluation metrics, sensitivity analysis, and interpretability analysis. The experimental results show that compared with the comparative algorithm presented in this paper, the improved model has improved RMSE by at least 39.84%, MAE by 26.12%, MAPE by 45.28%, PICP and MPIW indicators by at least 3.80% and 2.27%, indicating that the model not only outperforms the comparative model in accuracy, but also exhibits stronger adaptability and robustness in complex load fluctuation scenarios. Full article

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments. Full article

To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, the historical dataset is divided into three weather scenarios (sunny, cloudy, and rainy) to generate training and test samples under the same weather conditions. Second, a TCN is used to extract local temporal features, and BiLSTM captures the bidirectional temporal dependencies between power and meteorological data. To address the non-differentiable issue of traditional interval prediction quantile loss functions, the Huber norm is introduced as an approximate replacement for the original loss function by constructing a differentiable improved Quantile Regression (QR) model to generate confidence intervals. Finally, Kernel Density Estimation (KDE) is integrated to output probability density prediction results. Taking a distributed PV power station in East China as the research object, using data from July to September 2022 (15 min resolution, 4128 samples), comparative verification with TCN-QRLSTM and QRBiLSTM models shows that under a 90% confidence level, the Prediction Interval Coverage Probability (PICP) of the proposed model under sunny/cloudy/rainy weather reaches 0.9901, 0.9553, 0.9674, respectively, which is 0.56–3.85% higher than that of comparative models; the Percentage Interval Normalized Average Width (PINAW) is 0.1432, 0.1364, 0.1246, respectively, which is 1.35–6.49% lower than that of comparative models; the comprehensive interval evaluation index (I) is the smallest; and the Bayesian Information Criterion (BIC) is the lowest under all three weather conditions. The results demonstrate that the model can effectively quantify and mitigate PV power generation uncertainty, verifying its reliability and superiority in short-term PV power probabilistic prediction, and it has practical significance for ensuring the safe and economical operation of power grids with high PV penetration. Full article

Background: Psychotropic medications are frequently prescribed to elderly patients in emergency settings, yet their potential to induce electrocardiographic (ECG) abnormalities, particularly QTc interval prolongation, raises safety concerns. Older adults may be especially vulnerable due to polypharmacy, age-related cardiac changes, and comorbidities. Methods: We conducted a retrospective observational study on patients aged ≥65 years who underwent psychiatric evaluation in the Emergency Department (ED) of a tertiary hospital between 2015 and 2023. Data was extracted on demographics, psychiatric symptoms, psychotropic drug use, and ECG findings. The primary outcome was the prevalence of major ECG abnormalities (QTc or QRS prolongation), and secondary analyses explored associations with drug class and hospitalization. Results: Seventy-seven patients were included (62.3% female, median age 74 years). Overall, 22.1% exhibited ECG abnormalities, with QTc prolongation in 16.9% and QRS widening in 5.2%. ECG alterations were more common among patients receiving psychotropic drugs (30.7% vs. 13.2%; p = 0.046). Multivariate analysis confirmed psychotropic therapy as an independent predictor of ECG abnormalities (OR 2.84; 95% CI: 1.01–7.98; p = 0.049). No significant sex-related differences were observed. Conclusions: ECG abnormalities are common in elderly patients undergoing psychiatric assessment in the ED and seem associated with psychotropic medication use. However, non-pharmacological factors also contribute significantly to risk. Integrated multidisciplinary evaluation is essential to ensure both psychiatric and cardiovascular safety in this fragile population. Full article

(1) Background: Physical activity, altered metabolic parameters, and socio-economic status may affect electrocardiographic (ECG) parameters in children. However, a direct comparison of their effects on resting ECG has not yet been performed. (2) Methods: A total of 139 participants (60 male), aged 10–17 years, were recruited. Resting 1-minute ECG recordings and clinical and laboratory investigations were obtained, while socio-economic status and physical activity were assessed using a questionnaire. Associations between these factors and ECG parameters were analyzed using analysis of covariance (ANCOVA). (3) Results: Age, sex, metabolic syndrome, and physical activity significantly influenced the average RR interval (η² = 0.292, 0.070, 0.078, and 0.070, respectively). Similar effects were observed on the T_end–P interval. The PR, QRS, QTc, and T_peak–T_end intervals were moderately influenced by age (η² = 0.084, 0.056, 0.072, and 0.049, respectively). QTc was additionally affected by sex (η² = 0.060). None of the modifiable factors had any effect on depolarization or repolarization parameters. Socio-economic status had no significant effect on resting ECG. (4) Conclusions: Physical activity exerts similar effects on resting ECG in both sexes, while metabolic syndrome is an independent determinant of several ECG parameters. Further studies are warranted to clarify the clinical relevance of these findings. Full article

Cardiovascular diseases are the leading cause of death worldwide, with arrhythmia being a prevalent and potentially fatal condition. The multi-lead electrocardiogram (ECG) is the primary tool for detecting arrhythmias. However, existing detection methods have shortcomings: they cannot dynamically integrate inter-lead correlations with multi-scale temporal changes in cardiac electrical activity. They also lack mechanisms to simultaneously focus on key leads and time segments, and thus fail to address multi-lead redundancy or capture comprehensive spatial-temporal relationships. To solve these problems, we propose a Multi-Scale Lead-Temporal Co-Attention Framework (MS-LTCAF). Our framework incorporates two key components: a Lead-Temporal Co-Attention Residual (LTCAR) module that dynamically weights the importance of leads and time segments, and a multi-scale branch structure that integrates features of cardiac electrical activity across different time periods. Together, these components enable the framework to automatically extract and integrate features within a single lead, between different leads, and across multiple time scales from ECG signals. Experimental results demonstrate that MS-LTCAF outperforms existing methods. On the PTB-XL dataset, it achieves an AUC of 0.927, approximately 1% higher than the current optimal baseline model (DNN_zhu’s 0.918). On the LUDB dataset, it ranks first in terms of AUC (0.942), accuracy (0.920), and F1-score (0.745). Furthermore, the framework can focus on key leads and time segments through the co-attention mechanism, while the multi-scale branches help capture both the details of local waveforms (such as QRS complexes) and the overall rhythm patterns (such as RR intervals). Full article

Background and Objectives: Obstructive sleep apnea syndrome (OSAS) and heart failure (HF) frequently coexist, amplifying cardiovascular risk through mechanisms involving chronic inflammation and autonomic dysfunction. This study investigates the impact of systemic inflammation, measured by the systemic immune-inflammation index (SII), and OSAS severity, assessed by the apnea–hypopnea index (AHI), on myocardial repolarization heterogeneity in patients with both conditions. Materials and Methods: In this retrospective study, 160 patients with HF and polysomnography-confirmed OSAS (AHI ≥ 5 events/h) were evaluated between January 2018 and November 2024. Patients were stratified by QT dispersion (QTd < 40 ms vs. ≥40 ms) to assess electrical heterogeneity. SII was calculated from neutrophil, platelet, and lymphocyte counts, and electrocardiographic markers (QTd, frontal QRS-T angle, T wave peak-to-end interval [TPEI]) were measured. Logistic regression and receiver operating characteristic (ROC) analyses were used to identify predictors of repolarization heterogeneity and ventricular arrhythmias. Results: Patients with QTd ≥ 40 ms (n = 78) exhibited higher SII (p < 0.001) and AHI (p < 0.001) compared to those with QTd < 40 ms (n = 82). SII and AHI independently predicted increased QTd in multivariate analysis (p = 0.01 and p < 0.001, respectively). ROC analysis identified SII ≥ 625.4 (sensitivity 73.1%, specificity 72%) and AHI ≥ 22.4 (sensitivity 79.5%, specificity 79.3%) as optimal cut-offs for predicting repolarization heterogeneity. SII, QTd, and TPEI were significantly associated with ventricular arrhythmias (p < 0.05). Patients with moderate-to-severe OSAS (AHI ≥ 15) had higher rates of ventricular tachyarrhythmias (17.8% vs. 5.7%, p = 0.03) and sudden cardiac death (9.3% vs. 1.9%, p = 0.05). Conclusions: Elevated SII and AHI are independent predictors of myocardial repolarization heterogeneity in patients with HF and OSAS, contributing to increased arrhythmic risk. These findings highlight the potential use of SII and AHI as accessible biomarkers for risk stratification, particularly in patients with a preserved ejection fraction, and underscore the need for targeted interventions to mitigate inflammation and OSAS severity. Full article

This study assesses how classical and modern features extracted from a single ECG lead (II) influence automated arrhythmia classification. Using the Large Scale 12-Lead Electrocardiogram Database for Arrhythmia Study and MATLAB^®, we compared traditional morphological measures (e.g., QRS duration, QT interval, atrial/ventricular rates) with advanced time-, frequency-, and nonlinear-domain descriptors. The method classifies ECGs into four or eight categories using 15–39 features, either automatically selected or combined. In the eight-class task, 29–39 features yielded 69% accuracy; in the four-class task, 15 MRMR-selected features achieved 94.2% accuracy. A key strength is efficiency: relying on a single lead reduces preprocessing, storage, and classification time by a factor of ~12 compared with 12-lead approaches. These findings show that advanced descriptors from a single lead can match multi-lead performance, enabling practical, scalable clinical applications. Full article