Electrocardiographic Recordings Research Articles

Dynamic Course of Clinical State Transitions in Patients Undergoing Advanced Life Support after Out-of-Hospital Cardiac Arrest

Objectives Studies of out-of-hospital cardiac arrest generally document the presenting (pulseless electrical activity [PEA], ventricular fibrillation/tachycardia (VF/VT), asystole), and the final states (resuming stable spontaneous circulation [s-ROSC], being declared dead). Only a few studies described the transitions between clinical states during advanced life support (ALS). The aim of this study was to describe and analyze the dynamics of state transitions during ALS. Methods A retrospective analysis of 464 OHCA events was conducted. Any observed state and its corresponding changing time were documented through continuous electrocardiographic and trans-thoracic impedance recording. Results When achieved, most s-ROSCs were obtained by 30 min, regardless of the presenting state. After this time point, the persistence of any transient state was associated with a great probability of being declared dead. The most probable change for VF/VT or PEA at any time was the transition to asystole (36.4% and 34.4%, respectively); patients in asystole at any time had a 70% probability of death. Patients achieving s-ROSC mostly came from a VF/VT state. In most cases, the presenting rhythm tended to persist over time during ALS. Asystole was the most stable state; a higher degree of instability was observed when the presenting rhythms were VF/VT or PEA. Transient ROSC episodes occurred mainly as the first transition after the presenting state, especially for initial PEA. Conclusions An understanding of the dynamic course of clinical state transitions during ALS may allow treatment strategies to be tailored in patients affected by OHCA.

Risk assessment of post-myocardial infarction patients with preserved ejection fraction using 45-min short resting Holter electrocardiographic recordings.

Risk stratification for sudden cardiac death in post-myocardial infarction (post-MI) patients remains a challenging task. Several electrocardiographic noninvasive risk factors (NIRFs) have been associated with adverse outcomes and were used to refine risk assessment. This study aimed to evaluate the performance of NIRFs extracted from 45-min short resting Holter ECG recordings (SHR), in predicting ventricular tachycardia inducibility with programmed ventricular stimulation (PVS) in post-MI patients with preserved left ventricular ejection fraction (LVEF). We studied 99 post-MI ischemia-free patients (mean age: 60.5 ± 9.5 years, 86.9% men) with LVEF ≥40%, at least 40 days after revascularization. All the patients underwent PVS and a high-resolution SHR. The following parameters were evaluated: mean heart rate, ventricular arrhythmias (premature ventricular complexes, couplets, tachycardias), QTc duration, heart rate variability (HRV), deceleration capacity, heart rate turbulence, late potentials, and T-wave alternans. PVS was positive in 24 patients (24.2%). HRV, assessed by the standard deviation of normal-to-normal R-R intervals (SDNN), was significantly decreased in the positive PVS group (42 ms vs. 51 ms, p = .039). SDNN values <50 ms were also associated with PVS inducibility (OR 3.081, p = .032 in univariate analysis, and 4.588, p = .013 in multivariate analysis). No significant differences were identified for the other NIRFs. The presence of diabetes, history of ST-elevation MI (STEMI) and LVEF <50% were also important predictors of positive PVS. HRV assessed from SHR, combined with other noninvasive clinical and echocardiographic variables (diabetes, STEMI history, LVEF), can provide an initial, practical, and rapid screening tool for arrhythmic risk assessment in post-MI patients with preserved LVEF.

Evaluation of differential effects of CDP-choline and choline on parasympathetic activity and changes in choline levels with heart rate variability

Objective: Heart rate variability (HRV) is used to evaluate the autonomic activity of heartbeat. This study aimed to investigate the&#x0D; effects of cholinomimetic drugs cytidine diphosphate-choline (CDP-choline) and choline, on short-term HRV parameters.&#x0D; Materials and Methods: Animals were randomized into three groups; control (0.9% NaCl), choline (100 mg/kg), CDP-choline (400&#x0D; mg/kg). Electrocardiography recordings were obtained for 45-minutes after treatments with 15-minutes intervals. HRV analyses and&#x0D; total choline level measurements in serum and heart tissues were performed.&#x0D; Results: High frequency power and total power increased in treatment groups, while heart rates were decreased. Low frequency was&#x0D; decreased with choline while very low frequency power decreased with CDP-choline. Choline affected most of the HRV parameters&#x0D; in the first 15 minutes, while the effect of CDP-choline started within 30 minutes. Total choline levels were higher in both treatment&#x0D; groups than in the control while the levels were also higher in the choline group compared to CDP-choline group.&#x0D; Conclusion: This study showed that CDP-choline and choline treatments produced a rapid response to short-term HRV parameters,&#x0D; while increasing tissue choline levels. Moreover, the differences in effects and onset time between the drugs on HRV might be related&#x0D; to tissue choline concentration.

Ensemble Learning-Based Atrial Fibrillation Detection From Single Lead ECG Wave for Wireless Body Sensor Network

With the popularity of wireless techniques and portable devices, continuous and real-time monitoring patient&#x2019;s health status by collecting physiological signals is becoming more popular. Data mining of electrocardiograph (ECG) attracts widespread attention, where automatic atrial fibrillation (AF) detection has become a hot research field. In this paper, an ensemble learning algorithm is proposed for AF detection from single lead ECG recordings collected by wearable devices. This algorithm includes two modules. First, denoised 1-D time series (ECG), time-frequency spectrum and Poincare plot are used to train three component learners through a parallel style, respectively, and each component learner produces four probability values. Then, all the outputs are combined using a weighted matrix constructed by a Bayesian optimization algorithm, which is capable of producing the final classification result. Quantities of experiments have been implemented, and the results well prove the algorithm&#x2019;s effectiveness and its advantage over some state-of-the-art counterparts.

An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning.

Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in literature and has shown very good performance. However, this performance assumes that the training data is centralized, which is often not the case in real-life scenarios, where data resides in multiple places and only a small portion of it is labeled. Therefore, in this work, we propose an ECG classification system that focuses on preserving data privacy and enhancing overall system efficiency. We analyzed the complexity of previously proposed deep learning-based models and showed that the temporal convolutional network-based models (TCN) were the most efficient. Then, we built on the TCN models a modified split-learning (SL) system that achieves the same classification performance as the basic SL but reduces the communication overhead between the server and the client by 71.7% as well as reducing the computations at the client by 46.5% compared to the original SL system based on the TCN network. Finally, we implement semi-supervised learning in our system to enhance its classification performance by 9.1%-15.7%, when the training data consists only of 10% labeled data. We have tested our proposed system on a test IoT setup and it achieved satisfactory classification accuracy while being private and energy efficient for green-AI applications.

Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

Interactions between the central nervous system (CNS) and autonomic nervous system (ANS) play a crucial role in modulating perception, cognition, and emotion production. Previous studies on CNS–ANS interactions, or heart–brain coupling, have often used heart rate variability (HRV) metrics derived from electrocardiography (ECG) recordings as empirical measurements of sympathetic and parasympathetic activities. Functional near-infrared spectroscopy (fNIRS) is a functional brain imaging modality that is increasingly used in brain and cognition studies. The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms. Therefore, fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously, allowing analysis of CNS–ANS interactions. In this proof-of-concept study, fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena (MOBA) game. From the fNIRS recordings, neural and heartbeat frequency bands were extracted to assess prefrontal activities and short-term pulse rate variability (PRV), an approximation for short-term HRV, respectively. Under the experimental conditions used, fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards, in terms of absolute measurements and video game playing (VGP)-related changes. It was also observed that, similar to previous studies on physical activity and exercise, the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP. It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP, facilitating the understanding of VGP-related heart–brain coupling.

Demonstration of subclinical left ventricular electrical and mechanical dysfunction in overweight subjects by frontal QRS-T angle and 3D-speckle tracking echocardiography.

Overweightness is a considerable step in the process leading to obesity. There are no sufficient studies on the effect of cardiomyopathy defined in obese patients about overweight subjects. We thought that it may be useful to examine the myocardial involvement in overweight individuals electro-mechanically with more sensitive techniques before the development of obesity cardiomyopathy. The aim of the present study was to demonstrate whether or not there are subclinical left ventricular (LV) electrical and mechanical dysfunctions in overweight patients using frontal QRS-T (fQRS-T) angle (electrically) and 3D-speckle tracking echocardiography (mechanically). A total of 80 overweight patients and 80 age- and sex-matched normal weight individuals were enrolled into the study. 3D-STE examinations of the patients were performed. Electrocardiographic recordings were obtained for fQRS-T angle assessment. The LV-GLS and LV-GCS were significantly depressed in the overweight group than in the normal weight group (-14.5±3.4vs. -21.7±3.6, p<.001; -15.2±4.6vs. -24.3±4.8, p<.001, respectively). The fQRS-T angle was found to be increased in the overweight group (142.5±39.2vs. 114.7±43.5, p=<.001). Statistically significant positive linear correlations were observed between BMI with LV-GLS, LV-GCS, and fQRS-T angle. LV-GLS and LV-GCS were found to be disrupted linearly as BMI increased (r=.718 for BMI and LV-GLS, r=.653 for BMI and LV-GCS). As BMI increased, it was found that the fQRS-T angle increased (r=.692 for BMI and fQRS-T angle). Our results support that, overweight individuals, despite their being apparently healthy, may have subclinical LV myocardial mechanical and electrical dysfunction.

The moderating role of depressive symptoms in the association between heart rate variability and cognitive performance in cardiac patients

IntroductionCoronary heart disease (CHD) is strongly associated with cognitive impairment, which is a core feature of depression, highly prevalent in patients with CHD. Interestingly, patients with CHD and individuals with depression display reduced heart rate variability (HRV), which proxies a complex network integrating autonomic and attentional systems. This study investigated the moderating role of depressive symptoms in the relation between reduced HRV and cognitive performance in patients with CHD. MethodThe sample included 274 patients with CHD (mean [standard deviation] age = 62 [9.5] years; 13 % women) admitted to cardiac rehabilitation units. Visual attention and task switching were assessed through the Trail Making Test (TMT). Depressive symptoms were assessed with the Beck Depression Inventory-II (BDI-II). Resting electrocardiographic recordings were collected to compute HRV indices. ResultsPatients with more severe depressive symptoms displayed an inverse association between HRV and cognitive performance (TMT-A: b = −0.08, p = .022; TMTB: b = −0.07, p = .042), whereas patients with milder depressive symptoms showed no significant association (TMT-A: b = −0.00, p = .90; TMTB: b = −0.02, p = .44). ConclusionsDepressive symptoms may strengthen the relation between reduced HRV and poorer cognitive performance in cardiac patients. The presence of depressive symptoms may signal the dysfunction of a network subserving autonomic and cognitive function.

Deep learning-mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms.

Concealed accessory pathway (AP) may cause atrial ventricular reentrant tachycardia impacting the health of patients. However, it is asymptomatic and undetectable during sinus rhythm. To detect concealed AP with electrocardiography (ECG) images, we collected normal sinus rhythmic ECG images of concealed AP patients and healthy subjects. All ECG images were randomly allocated to the training and testing datasets, and were used to train and test six popular convolutional neural networks from ImageNet pre-training and random initialization, respectively. We screened 152 ECG recordings in concealed AP group and 600 ECG recordings in control group. There were no statistically significant differences in ECG characteristics between control group and concealed AP group in terms of PR interval and QRS interval. However, the QT interval and QTc were slightly higher in control group than in concealed AP group. In the testing set, ResNet26, SE-ResNet50, MobileNetV3_large_100, and DenseNet169 achieved a sensitivity rate more than 87.0% with a specificity rate above 98.0%. And models trained from random initialization showed similar performance and convergence with models trained from ImageNet pre-training. Our study suggests that deep learning could be an effective way to predict concealed AP with normal sinus rhythmic ECG images. And our results might encourage people to rethink the possibility of training from random initialization on ECG image tasks.

Sex Related Differences in Electrocardiography.

Since its implementation into the clinical medicine by Willem Einthoven electrocardiography had become one of crucial diagnostic method in cardiology. In spite of this fact effects of gender differences on parameters of electrocardiographic recordings started to be studied only recently. Sex related differences in physiological ECG are only minimal in childhood but there are developing during adolescence reflecting rapidly evolving differences particularly in hormonal secretion and activity of an autonomic nervous system. The heart rate is approximately 7 % higher in women than in men, PQ and QRS intervals are longer in men while QT interval is longer in women. The ST segment in females is flatter but generally the sex-related differences in ST-T waveform patterns are relatively very small with higher level of ST segment and taller T wave in men. The effects of sex-related differences, including sex hormones, on cardiac cell injury and death and their influence in determining rhythmogenesis and action potential configuration and conduction play an important role in clinics. Women have a higher prevalence of sick sinus syndrome, inappropriate sinus tachycardia, atrioventricular nodal reentry tachycardia, idiopathic right ventricular tachycardia, and arrhythmic events in the long QT syndrome. In contrast, men have a higher prevalence of atrioventricular block, carotid sinus syndrome, atrial fibrillation, supraventricular tachycardia due to accessory pathways, Wolff-Parkinson-White syndrome, reentrant ventricular tachycardia, ventricular fibrillation and sudden death, and the Brugada syndrome.

Statistical Validation of An Automated Method for Calculating Time Domain Heart Rate Variability on The QT Dataset.

Heart rate variability measures calculated from electrocardiography recordings reflect social competence. Clinical assessments of social skills have found that reduced heart rate variability is related to differences in the development of social skills in children and increase the risk of mental disorders. Limited by widespread manual signal processing and R-peak detection in current clinical assessments, most literature reports only short-term baseline studies, with fewer studies reporting social interaction settings with prolonged recording. There is an urgent need for an automated physiological signal processing toolbox to detect R-peaks and perform heart rate variability measurements in social settings. This paper proposes a modified automated Neurokit2 toolbox with signal processing procedures similar to the MindWare software that requires manual inspection of R-peak locations. We calculate time domain heart rate variability metrics from the publicly available QT database by PhysioNet collected at resting states and under stress tests, mimicking social interaction stress scenarios. Statistical analysis conveys that heart rate variability metrics calculation applying both signal processing approaches using the Neurokit2 toolbox are statistically equivalent in comparison to the hand-labelled R-peaks from the QT database (n= 10 in the normal sinus rhythm group, and n= 6 in the ST Change group). Such validation results are crucial for the adoption of automated toolboxes for heart rate variability measures in social interaction assessments, where more movement and mood changes of participants are expected.Clinical Relevance- This contributes to the body of evidence of the reliability of the Neurokit2 toolbox for automatic cleaning of prolonged cardiac electrophysiological signals and calculation of heart rate variability in time-domain characterization in social interaction stress assessment.

Rationale and Design of the Randomized Bayesian Multicenter COME-TAVI Trial in Patients With a New Onset Left Bundle Branch Block

Patients with new-onset left bundle branch block (LBBB) after transcatheter aortic valve implantation (TAVI) are at risk of developing delayed high-degree atrioventricular block. Management of new-onset LBBB post-TAVI remains controversial. In the Comparison of a Clinical Monitoring Strategy Versus Electrophysiology-Guided Algorithmic Approach in Patients With a New LBBB After TAVI (COME-TAVI) trial, consenting patients with new-onset LBBB that persists on day 2 after TAVI, meeting exclusion/inclusion criteria, are randomized to an electrophysiological study (EPS)-guided approach or 30-day electrocardiographic monitoring. In the EPS-guided approach, patients with a His to ventricle (HV) interval ≥ 65 ms undergo permanent pacemaker implantation. Patients randomized to noninvasive monitoring receive a wearable continuous electrocardiographic recording and transmitting device for 30 days. Follow-up will be performed at 3, 6, and 12 months. The primary endpoint is a composite outcome designed to capture net clinical benefit. The endpoint incorporates major consequences of both strategies in patients with new-onset LBBB after TAVI, as follows: (i) sudden cardiac death; (ii) syncope; (iii) atrioventricular conduction disorder requiring a pacemaker (for a class I or IIa indication); and (iv) complications related to the pacemaker or EPS. The trial incorporates a Bayesian design with a noninformative prior, outcome-adaptive randomization (initially 1:1), and 2 prespecified interim analyses once 25% and 50% of the anticipated number of primary endpoints are reached. The trial is event-driven, with an anticipated upper limit of 452 patients required to reach 77 primary outcome events over 12 months of follow-up. In summary, the aim of this Bayesian multicentre randomized trial is to compare 2 management strategies in patients with new-onset LBBB post-TAVI—an EPS-guided approach vs noninvasive 30-day monitoring. Trial registration number: NCT03303612.

Detection of Ischemic ST-Segment Changes Using a Novel Handheld ECG Device in a Porcine Model

BackgroundPortable, smartphone-sized electrocardiography (ECG) has the potential to reduce time to treatment for patients suffering acute cardiac ischemia, thereby lowering the morbidity and mortality. In the UMC Utrecht, a portable, smartphone-sized, multi-lead precordial ECG recording device (miniECG 1.0, UMC Utrecht) was developed. ObjectivesThe purpose of this study was to investigate the ability of the miniECG to capture ischemic ECG changes in a porcine coronary occlusion model. MethodsIn 8 animals, antero-septal myocardial infarction was induced by 75-minute occlusion of the left anterior descending artery, after the first or second diagonal. MiniECG and 12-lead ECG recordings were acquired simultaneously before, during and after coronary artery occlusion and ST-segment deviation was evaluated. ResultsDuring the complete occlusion and reperfusion period, miniECG showed large ST-segment deviation in comparison to 12-lead ECG. MiniECG ST-segment deviation was observed within 1 minute for most animals. The miniECG was positive for ischemia (ie, ST-segment deviation ≥1 mm) for 99.7% (Q1-Q3: 99.6%-99.9%) of the occlusion time, while the 12-lead was only positive for 79.8% (Q1-Q3: 81.1%-98.7%) of the time (P = 0.018). ST-segment deviation reached maxima of 10.5 mm [95% CI: 6.5-14.5 mm] vs 5.0 mm [95% CI: 2.0-8.0 mm] for the miniECG vs 12-lead ECG, respectively. ConclusionsMiniECG ST-segment deviation was observed early and was of large magnitude during 75 minutes of porcine transmural antero-septal infarction. The miniECG was positive for ischemia for the complete occlusion period. These findings demonstrate the potential of the miniECG in the detection of cardiac ischemia. Although clinical research is required, data suggests that the miniECG is a promising tool for the detection of cardiac ischemia.

Increased Dispersion of Ventricular Repolarization as an Arrhythmic Risk Marker in Drug-free Patients With Major Depressive Disorder: A Preliminary Comparative Study.

Drug-free patients with major depressive disorder (MDD) are understudied in terms of increased risk for arrhythmias. In this study, we compared changes in corrected QT interval (QTc), QTc dispersion (QTcd), Tpeak-Tend (Tp-e), Tp-e/QT ratio, corrected JT interval (JTc), and JTc dispersion (JTcd), which are considered to be among the risk factors for the emergence of ventricular arrhythmias in patients with MDD. The study involved 50 patients with MDD who had been free of psychotropic medications for at least 1 month and 52 age-matched and sex-matched healthy controls. Illness-related characteristics, including duration of illness and Beck Depression Inventory scores, were recorded. Electrocardiography recordings made under a standardized procedure were performed for all participants, and arrhythmia risk markers were calculated from the electrocardiograms. The patient group had significantly higher QTcd, JTc, and JTcd values compared with the controls. Among electrocardiogram markers, only Tp-e/QTc was significantly and inversely correlated with the duration of illness, while none of the markers was associated with Beck Depression Inventory scores. Alterations in electrocardiogram-derived markers of ventricular arrhythmia, which can be obtained easily and inexpensively, can be evaluated for the prediction and prevention of severe cardiac conditions in patients with MDD and considered in selecting the safest antidepressant options available.

Individuals with chronic epilepsy have elevated P-wave heterogeneity comparable to patients with atrial fibrillation.

Identification of epilepsy patients with elevated risk for atrial fibrillation (AF) is critical given the heightened morbidity and premature mortality associated with this arrhythmia. Epilepsy is a worldwide health problem affecting nearly 3.4 million people in the United States alone. The potential for increased risk for AF in patients with epilepsy is not well appreciated, despite recent evidence from a national survey of 1.4 million hospitalizations indicating that AF is the most common arrhythmia in people with epilepsy. We analyzed inter-lead heterogeneity of P-wave morphology, a marker reflecting arrhythmogenic nonuniformities of activation/conduction in atrial tissue. The study groups consisted of 96 patients with epilepsy and 44 consecutive patients with AF in sinus rhythm before clinically indicated ablation. Individuals without cardiovascular or neurological conditions (n = 77) were also assessed. We calculated P-wave heterogeneity (PWH) by second central moment analysis of simultaneous beats from leads II, III, and aVR ("atrial dedicated leads") from standard 12-lead electrocardiography (ECG) recordings from admission day to the epilepsy monitoring unit (EMU). Female patients composed 62.5%, 59.6%, and 57.1% of the epilepsy, AF, and control subjects, respectively. The AF cohort was older (66 ± 1.1 years) than the epilepsy group (44 ± 1.8 years, p < .001). The level of PWH was greater in the epilepsy group than in the control group (67 ± 2.6 vs. 57 ± 2.5 μV, p = .046) and reached levels observed in AF patients (67 ± 2.6 vs. 68 ± 4.9 μV, p = .99). In multiple linear regression analysis, PWH levels in individuals with epilepsy were mainly correlated with the PR interval and could be related to sympathetic tone. Epilepsy remained associated with PWH after adjustments for cardiac risk factors, age, and sex. Patients with chronic epilepsy have increased PWH comparable to levels observed in patients with AF, while being ~20 years younger, suggesting an acceleration in structural change and/or cardiac electrical instability. These observations are consistent with emerging evidence of an "epileptic heart" condition.

The Retinal Nerve Fiber Layer Thickness Is Associated with Systemic Neurodegeneration in Long-Term Type 1 Diabetes.

To determine whether the retinal nerve fiber layer thickness can be used as an indicator for systemic neurodegeneration in diabetes. We used existing data from 38 adults with type 1 diabetes and established polyneuropathy. Retinal nerve fiber layer thickness values of four scanned quadrants (superior, inferior, temporal, and nasal) and the central foveal thickness were extracted directly from optical coherence tomography. Nerve conduction velocities were recorded using standardized neurophysiologic testing of the tibial and peroneal motor nerves and the radial and median sensory nerves, 24-hour electrocardiographic recordings were used to retrieve time- and frequency-derived measures of heart rate variability, and a pain catastrophizing scale was used to assess cognitive distortion. When adjusted for hemoglobin A1c, the regional thickness of the retinal nerve fiber layers was (1) positively associated with peripheral nerve conduction velocities of the sensory and motor nerves (all P < 0.036), (2) negatively associated with time and frequency domains of heart rate variability (all P < 0.033), and (3) negatively associated to catastrophic thinking (all P < 0.038). Thickness of the retinal nerve fiber layer was a robust indicator for clinically meaningful measures of peripheral and autonomic neuropathy and even for cognitive comorbidity. The findings indicate that the thickness of the retinal nerve fiber layer should be studied in adolescents and people with prediabetes to determine whether it is useful to predict the presence and severity of systemic neurodegeneration.

Evaluation of Outpatients in the Post-COVID-19 Period in Terms of Autonomic Dysfunction and Silent Ischemia.

In this context, the objective of this study is to evaluate the 24-hour ambulatory electrocardiography (ECG) recordings, autonomous function with heart rate variability (HRV), and silent ischemia (SI) attacks with ST depression burden (SDB) and ST depression time (SDT) of post-COVID-19 patients. Materials and methods: The 24-hour ambulatory ECG recordings obtained >12 weeks after the diagnosis of COVID-19 were compared between 55 consecutive asymptomatic and 73 symptomatic post-COVID-19 patients who applied to the cardiology outpatient clinic with complaints of palpitation and chest pain in comparison with asymptomatic post-COVID-19 patients in Kars Harakani state hospital. SDB, SDT, and HRV parameters were analyzed. Patients who had been on medication that might affect HRV, had comorbidities that might have caused coronary ischemia, and were hospitalized with severe COVID-19 were excluded from the study. There was no significant difference between symptomatic and asymptomatic post-COVID-19 patients in autonomic function. On the other hand, SDB and SDT parameters were significantly higher in symptomatic post-COVID-19 patients than in asymptomatic post-COVID-19 patients. Multivariate analysis indicated that creatine kinase-myoglobin binding (CK-MB) (OR:1.382, 95% CI:1.043-1.831; p=0.024) and HRV index (OR: 1.033, 95% CI:1.005-1.061; p=0.019) were found as independent predictors of palpitation and chest pain symptoms in post-COVID-19 patients. The findings of this study revealed that parasympathetic overtone and increased HRV were significantly higher in symptomatic patients with a history of COVID-19 compared to asymptomatic patients with a history of COVID-19 in the post-COVID-19 period. Additionally, 24-hour ambulatory ECG recordings and ST depression analysis data indicated that patients who experienced chest pain in the post-COVID-19 period experienced silent ischemia (SI) attacks.

Changes in Electrocardiogram in Patients With Spontaneous Subarachnoid Hemorrhage: A Cross-Sectional Study.

Background Electrocardiographic (ECG) changes are frequently reported findings in patients with subarachnoid hemorrhage (SAH). We conducted a retrospective descriptive study to assess the prevalence of electrocardiographic changes in patients with non-traumatic SAH. Methods In this single-center retrospective cross-sectional study, ECG recordings of 45 patients who presented to Tribhuvan University Teaching Hospital in the year 2019 with SAH were collected and analyzed to detect any abnormalities. Results In our study, 88.8% of patients had some form of ECG abnormality. The most common ECG abnormalities associated with SAH were QTc prolongation, T-wave abnormalities, and bradycardia, which were found, respectively, in 35.5%, 24.4%, and 24.4% of the patients. Other ECG changes we observed include ST depression, large U-waves, atrial fibrillation, and premature ventricular contractions. Conclusion Morphological and rhythm abnormalities are frequently observed in patients with SAH, which may cause diagnostic dilemmas and unnecessary workups. Further studies are required to evaluate their significance and correlate ECG changes with clinical outcomes.

Automatic electrocardiograph diagnosis of myocardial ischemia with support vector machine

ABSTRACT Background: Myocardial ischemia is a severe cardiac disease and it happens when the heart's blood flow is insufficient, which impairs its capacity to operate correctly and causes several systemic issues. A standard auxiliary for the clinical diagnosis of myocardial ischemia is an electrocardiograph (ECG). However, the typical static ECG cannot record the myocardial ischemia paroxysmal fragments quickly and accurately. The information contained in long-term ECG recordings is more abundant, but the large volume of data makes manual processing costly. Our aim is to propose an automated method for handling ECG signals and diagnosing myocardial ischemia. Methods: To get evidence of myocardial ischemia, dynamic ECG is frequently employed. This paper suggests a machine learning technique to create a classification model using support vector machine (SVM), automatically identifying the existence of myocardial ischemia fragments. This study comprises waveform identification, feature extraction, creation of data sets, model training, and classification. The Long Term ST Database, made available by PhysioNet, is used as the database for this research. Result: According to the final test results, the classification accuracy is 97.98%. The ECG signals can be automatically segmented, and the automated diagnosis of myocardial ischemia can achieve an accuracy rate of 97.98%. Conclusion: The results suggest that the method proposed in this paper can more precisely and practically identify myocardial ischemia pieces.

Premature depolarisations in horses competing in United States Eventing Association and Fédération Equestre Internationale-sanctioned 3-day events.

Injuries and sudden death during the cross-country (XC) phase of eventing have raised interest in the frequency and types of cardiac arrhythmias occurring in these equine athletes. To characterise the frequency and types of rhythm disturbances and examine possible risk factors for premature depolarisations (PDs) occurring during the XC phase of United States Eventing Association (USEA) and Fédération Equestre Internationale (FEI)-sanctioned events. Prospective, cross-sectional study. Continuous electrocardiographic (ECG) recordings were obtained from horses prior to, during and immediately following the XC competition. Physiological rhythms pre-XC were included in the arrhythmia group for analysis. The frequency of PDs was calculated for all horses, as well as by division. PDs were categorised by complexity (singles, couplets, triplets, complex) and variably grouped for statistical analysis. Multiple logistic regression was used to identify risk factors for the presence of specific cardiac arrhythmia groupings during XC. PDs were identified in 42/75 horses (56% [45, 67]) during XC. Horses competing in the upper divisions of eventing had higher odds for having any PD during XC compared to the lower divisions (OR = 17.5 [4.3, 72.01], p = 0.006). The amount of time the heart rate (HR) was greater than 199 beats per minute (BPM) was associated with more complex arrhythmias (triplets, salvos and tachyarrhythmias) (OR = 1.01 [1.0, 1.02], p = 0.005). An arrhythmia at rest (physiological or PD) was associated with arrhythmia in the early recovery period (OR = 3.5 [1.1, 10.8], p = 0.03). Convenience sampling and technical challenges of continuous ECG recordings under competition settings limited the number of enrollments. PDs were seen in a high percentage of horses during the XC competition. Upper levels and time the HR was greater than 199 BPM were related to PD presence and complexity.