Search Results (13)

Background: Prior studies evaluating post-atrial fibrillation (AF) ablation pulmonary vein (PV) ostial gaps via magnetic resonance imaging (MRI) have shown circumferential PV fibrosis in a minority of patients, and their correlation with AF recurrence was weak. These studies were mostly based on radio-frequency AF ablations. Aim: We aimed to assess cryoballoon ablation-induced PV fibrosis via MRI and its correlation with AF recurrence. Methods and Results: This was a prospective study of consecutive patients with symptomatic AF who underwent pre- and post-ablation MRI to assess baseline and ablation-induced fibrosis, respectively. Post-ablation PV gaps were assessed by new semi-quantitative visual analysis assisted by computerized ADAS analysis. AF recurrence monitored via multiple ECGs and event monitoring at 6 and 12 months post ablation. Nineteen patients with 80 PVs were included, age 56 ± 11, with paroxysmal and persistent AF in 17/19 and 2/19 patients, respectively. Baseline MRI showed minimal LA fibrosis. All patients underwent successful cryoballoon PV electrical isolation. Post-ablation MRI revealed circumferential PV fibrosis among 63/80 (78.8%) PVs and partial fibrosis with major gaps among 17/80 (21.2%) PVs. AF recurred within one year in 5/9 (55.5%) patients with partial PV fibrosis, while no AF recurred among the 10 patients in whom all PVs had circumferential fibrosis (p < 0.01). Similarly, there were significantly more PVs without circumferential fibrosis (due to major gaps) among patients with AF recurrence as compared with patients without AF recurrence (42.9% vs. 13.5%; p < 0.01). Conclusion: Cryoballoon AF ablation results in circumferential PV fibrosis in the majority of PVs, as assessed by a new clinically relevant MRI-LGE analysis. Significant correlation was found between major PV gaps on post-ablation MRI and AF recurrence, suggesting that MRI might have the ability to predict AF recurrence. Full article

Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease. Full article

Background. Long COVID-19 symptoms appeared in many COVID-19 survivors. However, the prevalence and symptoms associated with long COVID-19 and its comorbidities have not been established. Methods. In total, 312 patients with long COVID-19 from 21 primary care centers were included in the study. At the six-month follow-up, their lung function was assessed by computerized tomography (CT) and spirometry, whereas cardiac function was assessed by electrocardiogram, Holter ECG, echocardiography, 24 h blood pressure monitoring, and a six-minute walk test (6MWT). Results. Of the 312 persons investigated, significantly higher systolic and diastolic blood pressure, left ventricular hypertrophy, and elevated NT-proBNP were revealed in participants with hypertension or type 2 diabetes. Left ventricular diastolic dysfunction was more frequently present in patients with hypertension. The most common registered CT abnormalities were fibrotic changes (83, 36.6%) and mediastinal lymphadenopathy (23, 10.1%). Among the tested biochemical parameters, three associations were found in long COVID-19 patients with hypertension but not diabetes: increased hemoglobin, fibrinogen, and ferritin. Nine patients had persisting IgM antibodies to SARS-CoV-2. Conclusions. We demonstrated a strong association between signs of cardiac dysfunction and lung fibrotic changes with comorbidities in a cohort of long COVID-19 subjects. Full article

Computerized electrocardiography (ECG) has been widely used and allows linkage to electronic medical records. The present study describes the development and clinical applications of an electronic cohort derived from a digital ECG database obtained by the Telehealth Network of Minas Gerais, Brazil, for the period 2010–2017, linked to the mortality data from the national information system, the Clinical Outcomes in Digital Electrocardiography (CODE) dataset. From 2,470,424 ECGs, 1,773,689 patients were identified. A total of 1,666,778 (94%) underwent a valid ECG recording for the period 2010 to 2017, with 1,558,421 patients over 16 years old; 40.2% were men, with a mean age of 51.7 [SD 17.6] years. During a mean follow-up of 3.7 years, the mortality rate was 3.3%. ECG abnormalities assessed were: atrial fibrillation (AF), right bundle branch block (RBBB), left bundle branch block (LBBB), atrioventricular block (AVB), and ventricular pre-excitation. Most ECG abnormalities (AF: Hazard ratio [HR] 2.10; 95% CI 2.03–2.17; RBBB: HR 1.32; 95%CI 1.27–1.36; LBBB: HR 1.69; 95% CI 1.62–1.76; first degree AVB: Relative survival [RS]: 0.76; 95% CI0.71–0.81; 2:1 AVB: RS 0.21 95% CI0.09–0.52; and RS 0.36; third degree AVB: 95% CI 0.26–0.49) were predictors of overall mortality, except for ventricular pre-excitation (HR 1.41; 95% CI 0.56–3.57) and Mobitz I AVB (RS 0.65; 95% CI 0.34–1.24). In conclusion, a large ECG database established by a telehealth network can be a useful tool for facilitating new advances in the fields of digital electrocardiography, clinical cardiology and cardiovascular epidemiology. Full article

Updates to industry consensus standards for ECG equipment is a work-in-progress by the ISO/IEC Joint Work Group 22. This work will result in an overhaul of existing industry standards that apply to ECG electromedical equipment and will result in a new single international industry, namely 80601-2-86. The new standard will be entitled “80601, Part 2-86: Particular requirements for the basic safety and essential performance of electrocardiographs, including diagnostic equipment, monitoring equipment, ambulatory equipment, electrodes, cables, and leadwires”. This paper will provide a high-level overview of the work in progress and, in particular, will describe the impact it will have on requirements and testing methods for computerized ECG interpretation algorithms. The conclusion of this work is that manufacturers should continue working with clinical ECG experts to make clinically meaningful improvements to automated ECG interpretation, and the clinical validation of ECG analysis algorithms should be disclosed to guide appropriate clinical use. More cooperation is needed between industry, clinical ECG experts and regulatory agencies to develop new data sets that can be made available for use by industry standards for algorithm performance evaluation. Full article

Ever since the first publication of the standard communication protocol for computer-assisted electrocardiography (SCP-ECG), prENV 1064, in 1993, by the European Committee for Standardization (CEN), SCP-ECG has become a leading example in health informatics, enabling open, secure, and well-documented digital data exchange at a low cost, for quick and efficient cardiovascular disease detection and management. Based on the experiences gained, since the 1970s, in computerized electrocardiology, and on the results achieved by the pioneering, international cooperative research on common standards for quantitative electrocardiography (CSE), SCP-ECG was designed, from the beginning, to empower personalized medicine, thanks to serial ECG analysis. The fundamental concept behind SCP-ECG is to convey the necessary information for ECG re-analysis, serial comparison, and interpretation, and to structure the ECG data and metadata in sections that are mostly optional in order to fit all use cases. SCP-ECG is open to the storage of the ECG signal and ECG measurement data, whatever the ECG recording modality or computation method, and can store the over-reading trails and ECG annotations, as well as any computerized or medical interpretation reports. Only the encoding syntax and the semantics of the ECG descriptors and of the diagnosis codes are standardized. We present all of the landmarks in the development and publication of SCP-ECG, from the early 1990s to the 2009 International Organization for Standardization (ISO) SCP-ECG standards, including the latest version published by CEN in 2020, which now encompasses rest and stress ECGs, Holter recordings, and protocol-based trials. Full article

The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis. Deep learning (DL) has made significant advances in the medical industry, including the use of magnetic resonance imaging (MRI) scans, computerized tomography (CT) scans, and electrocardiograms (ECGs) to detect life-threatening diseases, including heart disease, cancer, and brain tumors. However, more advancement in the field of pathology is needed, but the main hurdle causing the slow progress is the shortage of large-labeled datasets of histopathology images to train the models. The Kimia Path24 dataset was particularly created for the classification and retrieval of histopathology images. It contains 23,916 histopathology patches with 24 tissue texture classes. A transfer learning-based framework is proposed and evaluated on two famous DL models, Inception-V3 and VGG-16. To improve the productivity of Inception-V3 and VGG-16, we used their pre-trained weights and concatenated these with an image vector, which is used as input for the training of the same architecture. Experiments show that the proposed innovation improves the accuracy of both famous models. The patch-to-scan accuracy of VGG-16 is improved from 0.65 to 0.77, and for the Inception-V3, it is improved from 0.74 to 0.79. Full article

Background and Objectives: The aim of this study was to investigate parameters that can be used to predict mortality in patients diagnosed with COVID-19 in the emergency department (ED). Materials and Methods: Patients diagnosed with COVID-19 in the ED were included in this prospective study. The patients were divided into two groups. The surviving patients were included in Group 1 (survivors), and the patients who died were included in Group 2 (non-survivors). The electrocardiogram (ECG), laboratory results and chest computerized tomography (CCT) findings of the two groups were compared. The CCT images were classified according to the findings as normal, mild, moderate and severe. Results: Of the 419 patients included in the study, 347 (83%) survived (survivor) and 72 (17%) died (non-survivor). The heart rate and respiratory rate were found to be higher, and the peripheral oxygen saturation (SpO₂) and diastolic blood pressure (DBP) were found to be lower in the non-survivor patients. QRS and corrected QT interval (QTc) were measured as longer in the non-survivor patients. In the CCT images, 79.2% of the non-survivor patients had severe findings, while 11.5% of the survivor patients had severe findings. WBC, neutrophil, NLR, lactate, D-dimer, fibrinogen, C- Reactive Protein (CRP), urea, creatinine, creatine kinase-MB (CK-MB) and hs-Troponin I levels were found to be higher and partial pressure of carbon dioxide (PCO₂₎, base excess (BE), bicarbonate (HCO₃₎, lymphocyte eosinophil levels were found to be lower in non-survivor patients. The highest AUC was calculated at the SpO₂ level and the eosinophil level. Conclusions: COVID-19 is a fatal disease whose mortality risk can be estimated when the clinical, laboratory and imaging studies of the patients are evaluated together in the ED. SpO₂ that is measured before starting oxygen therapy, the eosinophil levels and the CT findings are all important predictors of mortality risk. Full article

The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice. Full article

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database. Full article

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG. Full article

Objectives. The aim of the study was to assess the effect of the anthropometric data of basketball players on the alterations of cardiovascular parameters during the physical load applying the model of integrated evaluation.
Material and Methods. The research sample consisted of 113 healthy Caucasian male basketball players, candidates of the Lithuanian National men’s basketball teams. Basketball players were divided into 2 groups: 69 taller and heavier male basketball players (with a higher percentage of body fat) (TMB) and 44 shorter and less heavy male basketball players (with a lower percentage of body fat) (SMB). The amount of fat, expressed in percentage, was measured using the body composition analyzer TBF–300. “Kaunas-Load,” a computerized ECG analysis system, was used to evaluate the functional condition of the cardiovascular system during the load.
Results. The TMB group had a lower heart rate during the warming-up phase and the steady state of the load as compared with the SMB group (P<0.05). The JT interval in the TMB group was greater during the warming-up and the steady state as compared with the SMB group (P<0.05). The JT/RR ratio index in the TMB group was found to be lower in the warming-up phase and in the steady state compared with the respective parameter in the SMB group (P<0.05).
Conclusions. T he cardiovascular system of taller and heavier male basketball players with a greater relative amount of body fat functioned more economically. Full article

We consider the human body as an adaptable, complex, and dynamic system capable of organizing itself, though there is none, the only one, factor inside the system capable of doing this job. Making use of the computerized ECG analysis system “Kaunas-load” with parallel registration of ECG carrying out body motor characteristics, ABP, or other processes characterizing hemodynamics enable one to reveal and evaluate the synergistic aspects of essential systems of the human body what particularly extends the possibilities of functional diagnostics. The aim of the study was to determine the features of alterations in the functional condition of basketball and football players and nonathletes during the bicycle ergometry test by applying the model of evaluation of the functional condition of the human body. Material and methods. The study population consisted of 266 healthy athletes and nonathletes. Groups of male basketball players, male football players, male nonathletes, female basketball players, and female nonathletes were studied. A computerized ECG analysis system “Kaunas-load” that is capable of both registering and analyzing the power developed by the subject and 12-lead ECG synchronically were used for evaluating the functional condition of the CVS. The subject did a computer-based bicycle ergometry test. The following ECG parameters at rest and throughout the load – HR, JT interval, and the deduced JT/RR ratio index that reflects the condition between regulatory and supplying systems – were evaluated. After measuring ABP, the pulse amplitude (S–D) was evaluated. The pulse blood pressure ratio amplitude (S–D)/S that depicts the connection between the periphery and regulatory systems was also evaluated. Speeds of changes in physiological parameters during physical load were evaluated too.
Results. Heart rate and JT/RR ratio of athletes at the rest and during load were lower, and JT interval of rest was longer and became shorter more slowly during load, compared to that of healthy nonathletes. The pulse arterial blood pressure amplitude of men at rest and during load was higher than that of women. The pulse ABP amplitude of athletes was higher than that of nonathletes. The relative pulse ABP amplitude in the state of rest in the groups of men was higher than in groups of women. The relative pulse amplitude of female basketball players at rest and during load was higher than that of female nonathletes. Significant differences in the dynamics of speed of changes in HR, the pulse ABP amplitude, and the relative pulse ABP amplitude of male and female basketball players, male football players, as well as male and female nonathletes were observed.
Conclusions. The newly deduced parameters, namely, speeds of changes in the parameters with changes in the phase of the load reflect very well peculiarities of functional condition of the human body during bicycle ergometry test. The sum total of those newly deduced parameters and customary parameters reveals new functional peculiarities of the human body. Full article