Wavelet analysis of intermittent dynamics in nocturnal electrocardiography and electroencephalography data.

This paper presents the results of a study of the characteristics of phase synchronization between electrocardiography(ECG) and electroencephalography (EEG) signals during night sleep. Polysomnographic recordings of eight generally healthy subjects and eight patients with obstructive sleep apnea syndrome were selected as experimental data. A feature of this study was the introduction of an instantaneous phase for EEG and ECG signals using a continuous wavelet transform at the heart rate frequency using the concept of time scale synchronization, which eliminated the emergence of asynchronous areas of behavior associated with the "leaving" of the fundamental frequency of the cardiovascular system. Instantaneous phase differences were examined for various pairs of EEG and ECG signals during night sleep, and it was shown that in all cases the phase difference exhibited intermittency. Laminar areas of behavior are intervals of phase synchronization, i.e., phase capture. Turbulent intervals are phase jumps of 2π. Statistical studies of the observed intermittent behavior were carried out, namely, distributions of the duration of laminar sections of behavior were estimated. For all pairs of channels, the duration of laminar phases obeyed an exponential law. Based on the analysis of the movement of the phase trajectory on a rotating plane at the moment of detection of the turbulent phase, it was established that in this case the eyelet intermittency was observed. There was no connection between the statistical characteristics of laminar phase distributions for intermittent behavior and the characteristics of night breathing disorders (apnea syndrome). It was found that changes in statistical characteristics in the phase synchronization of EEG and ECG signals were correlated with blood pressure at the time of signal recording in the subjects, which is an interesting effect that requires further research.

Seismocardiography and echocardiography: the correlation in the systolic complex.

Aim. This study aimed to investigate the correlation between seismocardiographic and echocardiographic systolic variables and whether a decrease in preload could be detected by the seismocardiography (SCG).Methods. This study included a total of 34 subjects. SCG and electrocardiography were recorded simultaneously followed by echocardiography (echo) in both supine and 30◦head-up tilted position. The SCG signals was segmented into individual heartbeats and systolic fiducial points were defined using a detection algorithm. Statistical analysis included correlation coefficient calculations and paired sample tests.Results. SCG was able to measure a decrease in preload by almost all of the examined systolic SCG variables. It was possible to correlate certain echo variables to SCG time intervals, amplitudes, and peak to peak intervals. Also, changes between supineand tilted position of some SCG variables were possible to correlate to changes in echo variables. LVET, IVCT, S', strain, SR, SV, and LVEF were significantly correlated to relevant SCG variables.Conclusion. This study showed a moderate correlation, between systolic echo and systolic SCG variables. Additionally, systolic SCG variables were able to detect a decrease in preload.

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited ECG data. We pretrained Vision Transformer (ViT) models by reconstructing the masked ECG data with MAE. We fine-tuned this MAE-based ECG pretrained model on ECG-echocardiography data from The University of Tokyo Hospital (UTokyo) for the detection of left ventricular systolic dysfunction (LVSD), and then evaluated it using multi-center external validation data from seven institutions, employing the area under the receiver operating characteristic curve (AUROC) for assessment. We included 38,245 ECG-echocardiography pairs from UTokyo and 229,439 pairs from all institutions. The performances of MAE-based ECG models pretrained using ECG data from UTokyo were significantly higher than that of other Deep Neural Network models across all external validation cohorts (AUROC, 0.913-0.962 for LVSD, p < 0.001). Moreover, we also found improvements for the MAE-based ECG analysis model depending on the model capacity and the amount of training data. Additionally, the MAE-based ECG analysis model maintained high performance even on the ECG benchmark dataset (PTB-XL). Our proposed method developed high performance MAE-based ECG analysis models using limited ECG data.

A pilot study using the LASCA technique to analyze stress using heart rate variability.

In the quest to uncover biological cues that help explain organic changes brought on by an external stimulus, like stress, new technologies have become necessary. The Laser Speckle Contrast Analysis (LASCA) approach is one of these technologies that may be used to analyze biological data, including respiratory rate (RR) intervals, and then use the results to determine heart rate variability (HRV Thus, to evaluate the stress brought on by physical activity, this study used the LASCA approach. A stress induction procedure involving physical exertion was employed, and the results were compared to other established techniques (cortisol analysis and ECG signal) to verify the LASCA methodology as a tool for measuring HRV and stress. The study sample comprised 27 willing participants. The technique involving LASCA allowed for the non-invasive (non-contact) acquisition of HRV and the study of stress. Furthermore, it made it possible to gather pertinent data, such as recognizing modifications to the thermoregulation, peripheral vasomotor tonus, and renin-angiotensin-aldosterone systems that were brought on by elevated stress and, as a result, variations in HRV readings.

[Characteristics of electrocardiogram in fulminant myocarditis].

Objective: To investugate the unique electrocardiogram (ECG) characteristics of fulminant myocarditis (FM) patients and provide important clues for the diagnosis of FM. Methods: This was a retrospective study. Patients diagnosed with acute myocarditis at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 2017 to April 2022 were enrolled and divided into fulminant myocarditis group (FM) and non-fulminant myocarditis group (NFM) according to clinical diagnosis. A total of 246 healthy people who underwent physical examination in the Health examination Center of Tongji Hospital at the same period were selected as the control group. The clinical data and ECG characteristics of the above 3 groups were analyzed and compared. Logistic regression model was used to analyze the influence of ECG parameters on left ventricular ejection fraction in FM patients. Receiver operating curves were constructed to evaluate the predictive value of different ECG parameters for FM. Results: A total of 180 patients were included in this study (FM group: n=123; NFM group: n=57), with an age of (35.0±16.2) years and 106 males (58.89%). Compared with NFM group, ECG was significantly abnormal in FM group, with a higher incidence of sinus tachycardia, ventricular tachycardia or ventricular fibrillation, escape rhythm, right bundle branch block, third degree atrioventricular block, ST-segment elevation, low voltage, prolonged QTc interval, and widened QRS wave in the FM group (all P<0.05). The ECG parameters showed that the amplitude of the full lead QRS wave in FM group was lower than that in NFM group (P<0.01). The average heart rate and QTc interval of FM group were significantly higher than those of NFM and control groups (all P<0.05). Although ST-segment elevation had a higher incidence in the FM group, ECG parameters showed that except for leads Ⅲ and aVF, the ST segment levels in all leads in the FM group were lower than those in the control group (all P<0.05). There was a statistically significant difference in some ST segment changes between FM and NFM groups, while there was no statistical difference between the NFM and control groups. Multivariate regression analysis showed that widened QRS wave and increased heart rate were the influencing factors for left ventricular systolic dysfunction in FM patients (OR=16.914, 95%CI: 1.367-209.224, P=0.028; OR=1.026, 95%CI: 1.010-1.042, P=0.001). Receiver operating curve analysis showed that heart rate>86.90 beat/min, QTc>431.50 ms, and RV5+SV1<1.72 mV had certain predictive value for FM diagnosis. Conclusions: FM patients displayed marked and severe ECG abnormalities, and characteristic changes in ECG can provide important first clues for the diagnosis of FM.

Rare Presentation of Wide QRS Tachycardia in a Patient in Their 40s.

This article describes the case of a 40-year-old individual who presented with fulminant myocarditis. Initial ECG displayed sinus tachycardia with a heart rate of 117 bpm, QS complexes in leads V1-V3, ST-segment depression in leads II, III, aVF, V5-V6, and ST-segment elevation >0.2 mV in leads V1 through V3. The initial clinical assessment suggested an acute anteroseptal myocardial infarction. However, subsequent diagnostic evaluation through coronary angiography disclosed that the coronary arteries were normal. Therefore, clinicians should carefully consider the differential diagnosis between these conditions, as their management strategies differ markedly. Two hours after admission, the patient unexpectedly developed syncope. The ECG findings were consistent with the typical characteristics of bidirectional ventricular tachycardia. Our report described the appearance and morphology as well as mechanism of bidirectional ventricular tachycardia in detail. Additionally, we delineate differential diagnoses for disease that can cause bidirectional ventricular tachycardia, such as aconite poisoning, digoxin overdose, immune checkpoint inhibitor (ICI), myocardial ischemia, and hereditary channelopathies, such as catecholaminergic polymorphic ventricular tachycardia (CPVT) and Andersen-Tawil syndrome. Therefore, clinicians should recognize this ECG finding immediately and initiate appropriate treatment promptly as these measures may be vital in saving the patient's life.

Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology.

Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet.

Atrial fibrillation (AF) is the most prevalent form of arrhythmia, with a rising incidence and prevalence worldwide, posing significant implications for public health. In this paper, we introduce an approach that combines the Recurrence Plot (RP) technique and the ResNet architecture to predict AF. Our method involves three main steps: using wavelet filtering to remove noise interference; generating RPs through phase space reconstruction; and employing a multi-level chained residual network for AF prediction. To validate our approach, we established a comprehensive database consisting of electrocardiogram (ECG) recordings from 1008 AF patients and 48,292 Non-AF patients, with a total of 2067 and 93,129 ECGs, respectively. The experimental results demonstrated high levels of prediction precision (90.5%), recall (89.1%), F1 score (89.8%), accuracy (93.4%), and AUC (96%) on our dataset. Moreover, when tested on a publicly available AF dataset (AFPDB), our method achieved even higher prediction precision (94.8%), recall (99.4%), F1 score (97.0%), accuracy (97.0%), and AUC (99.7%). These findings suggest that our proposed method can effectively extract subtle information from ECG signals, leading to highly accurate AF predictions.

Continuous sepsis trajectory prediction using tensor-reduced physiological signals.

The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, p = 0.01 ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, p < 0.01 ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, p = 0.01 ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, p < 0.01 ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.

Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain.

BACKGROUND: Tailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients. METHODS: A multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis. RESULTS: The study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables. CONCLUSIONS: The reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic. CLINICALTRIALS: gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.

Multichannel ballistocardiography: A comparative analysis of heartbeat detection across different body locations.

The paper presents a validation of novel multichannel ballistocardiography (BCG) measuring system, enabling heartbeat detection from information about movements during myocardial contraction and dilatation of arteries due to blood expulsion. The proposed methology includes novel sensory system and signal processing procedure based on Wavelet transform and Hilbert transform. Because there are no existing recommendations for BCG sensor placement, the study focuses on investigation of BCG signal quality measured from eight different locations within the subject's body. The analysis of BCG signals is primarily based on heart rate (HR) calculation, for which a J-wave detection based on decision-making processes was used. Evaluation of the proposed system was made by comparing with electrocardiography (ECG) as a gold standard, when the averaged signal from all sensors reached HR detection sensitivity higher than 95% and two sensors showed a significant difference from ECG measurement.

[Diagnostic and intervention value of implantable cardiac monitor in patients over 60 years of age with unexplained syncope].

Objective: To investigate the value of implantable cardiac monitor (ICM) in the diagnosis and treatment of patients over 60 years old with unexplained syncope. Methods: This was a multi-center, prospective cohort study. Between June 2018 and April 2021, patients over the age of 60 with unexplained syncope at Beijing Hospital, Fuwai Hospital, Beijing Anzhen Hospital and Puren Hospital were enrolled. Patients were divided into 2 groups based on their decision to receive ICM implantation (implantation group and conventional follow-up group). The endpoint was the recurrence of syncope and cardiogenic syncope as determined by positive cardiac arrhythmia events recorded at the ICM or diagnosed during routine follow-up. Kaplan-Meier survival analysis was used to compare the differences of cumulative diagnostic rate between the 2 groups. A multivariate Cox regression analysis was performed to determine independent predictors of diagnosis of cardiogenic syncope in patients with unexplained syncope. Results: A total of 198 patients with unexplained syncope, aged (72.9±8.25) years, were followed for 558.0 (296.0,877.0) d, including 98 males (49.5%). There were 100 (50.5%) patients in the implantation group and 98 (49.5%) in the conventional follow-up group. Compared with conventional follow-up group, patients in the implantation group were older, more likely to have comorbidities, had a higher proportion of first degree atrioventricular block indicated by baseline electrocardiogram, and had a lower body mass index (all P<0.05). During the follow-up period, positive cardiac arrhythmia events were recorded in 58 (58.0%) patients in the ICM group. The diagnosis rate (42.0% (42/100) vs. 4.1% (4/98), P<0.001) and the intervention rate (37.0% (37/100) vs. 2.0% (2/98), P<0.001) of cardiogenic syncope in the implantation group were higher than those in the conventional follow-up group (all P<0.001). Kaplan-Meier survival analysis showed that the cumulative diagnostic rate of cardiogenic syncope was significantly higher in the implantation group than in the traditional follow-up group (HR=11.66, 95%CI 6.49-20.98, log-rank P<0.001). Multivariate analysis indicated that ICM implantation, previous atrial fibrillation, diabetes mellitus or first degree atrioventricular block in baseline electrocardiogram were independent predictors for cardiogenic syncope (all P<0.05). Conclusions: ICM implantation improves the diagnosis and intervention rates in patients with unexplained syncope, and increases diagnostic efficiency in patients with unexplained syncope.

Fatty infiltration and ventricular premature beats originating from right ventricular outflow tract: association or causality?

BACKGROUND: Anatomical evidence reveals heterogeneous fat distribution in both atrial and ventricular myocardium that are considered normal, but at the same time arrhythmogenic, and numerous cardiac pathophysiological conditions are associated with myocardial fat deposits. The relationship between fatty infiltration, especially in the epicardial layer and its pathophysiological implication is not completely understood. AIM: The aim of this study was to establish a positive or negative relationship between the ventricular burden and several parameters related to right ventricle (RV) adipose tissue - the RV thickness, RV indexed mass, body mass index (BMI), age, gender. PATIENTS, MATERIALS AND METHODS: Twenty-three patients with documented premature ventricular contractions (PVCs) originating from right ventricular outflow tract based on electrocardiography (ECG) evaluation were hospitalized between January 2018-November 2022 for electrophysiological study and PVCs ablation. Data obtained after collecting the clinical characteristics, ECG, RV measurements from transthoracic echocardiography (TTE), cardiac computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. RESULTS: A weak positive relationship between the ventricular burden and BMI (r=0.14, p=0.49), tricuspid annular plane systolic excursion (TAPSE) (r=0.07, p=0.7), the RV thickness (r=0.03, p=0.8), epicardial adipose tissue (r=0.13, p=0.55), RV mass indexed (r=0.05, p=0.82) was observed. No clear cut-off of the PVCs burden could be established in terms related to the increase in BMI, RV thickness, epicardial adipose tissue, RV mass indexed. CONCLUSIONS: No significant positive or negative relationship between the ventricular burden and the RV thickness, RV indexed mass were found in individuals with a high PVCs originating from right ventricular outflow tract (RVOT) burden.

A case of Williams syndrome with Wolff-Parkinson-White syndrome.

INTRODUCTION: Williams syndrome (WS) cases have been reported to have with 25-100 times greater increased risk of sudden cardiac death (SCD). SCD has been reported in cases without any evidence of structural cardiovascular anomalies. Wolff-Parkinson-White (WPW) syndrome is characterized by short PR interval and delta wave. Ventricular preexcitations can develop paroxysmal reentrant tachycardia through Kent bundle or less frequent atrial fibrillation and in some cases with accessory pathway effective refractory period (APERP) under 250 ms considered as risky and may lead to SCD. WS associated with WPW has not been reported before. CASE REPORT: An 11-year-old male who had been followed up with WS was referred to pediatric cardiology outpatient clinic with the complaint of palpitation. Electrocardiographic examination showed short PR interval and delta wave in the ECG consistent with WPW. He underwent electrophysiological study (EPS). Basic measurements were performed, and APERP was found at 280 ms cycle atrial pacing. RF energy was delivered using a 4 mm tip nonirrigated radiofrequency (RF) ablation catheter where the best ventriculoatrial (VA) signals were received and the AP was abolished within few seconds. DISCUSSION AND CONCLUSIONS: Although, WPW cases are usually asymptomatic or related to SVT, the risk of SCD should not be ignored. Thus, all patients with WPW deserve an EPS for assessing the AP conduction properties. Due to the increased risk of SCD in patients with WS compared to general population, in the presence of concomitant WPW, these patients should be evaluated with EPS even if they do not have symptoms.

Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.

BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability. OBJECTIVE: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF. METHODS: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF). RESULTS: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001). CONCLUSIONS: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.

Evaluating regression and probabilistic methods for ECG-based electrolyte prediction.

Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction-a method with high potential impact within multiple clinical scenarios.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Prediction of Micro- and Macrovascular Complications in Type 2 Diabetes Mellitus using Heart Rate Variability.

OBJECTIVE: To explore the utility of heart rate variability (HRV), a noninvasive marker of cardiac autonomic activity, as a prescreening tool for the prediction of micro- and macrovascular complications in type 2 diabetes mellitus (T2DM). METHODS: Consenting type 2 diabetic patients of both genders between 30 and 70 years, without known micro- and macrovascular complications of diabetes, were enrolled. Patients with medications affecting the HRV were excluded. Prior to other screening tests, 15 minutes of resting electrocardiogram (ECG) (1 kHz) was recorded in enrolled patients, followed by an exercise stress test and assessment for nephropathy, retinopathy, and peripheral neuropathy. The patients with positive stress tests were referred for coronary angiography to confirm coronary artery disease. Based on screening test results, patients were grouped as Group I-T2DM without complications (n = 31) and Group II-T2DM with micro/macrovascular complications (n = 29), (total = 60). RESULTS: Group comparison and test for association were employed, and p-value of <0.05 was considered significant. Significantly reduced HRV (decreased standard deviation of NN interval) between groups and a strong association of HRV indices with complications of diabetes were observed. Logistic regression to classify complicated vs noncomplicated group was used, and an accuracy of 0.78 with 85% sensitivity, 74% specificity with area under the curve (AUC) of 0.83 was observed. CONCLUSION: Significantly reduced HRV, stronger association with complications, and 85% sensitivity, 74% specificity, and 78% accuracy of classification make HRV indices a promising prescreening tool to predict micro- and macrovascular complications in type 2 diabetes.