New coronavirus infection in pediatric practice: clinical and epidemiological features of COVID-19 in the city of Smolensk.

The aim of the study was to study the clinical and epidemiological features of the new COVID-19 coronavirus infection in children hospitalized in the infectious department. Material and methods. 249 case histories of patients from 0 to 18 years of age who are on inpatient treatment at <<Clinical Hospital N1>> in Smolensk for the period from April 2020 to July 2022 were studied by the continuous sampling method. Verification of a new coronavirus infection was carried out by examining smears from the nasopharynx and oropharynx for the presence of SARS-CoV-2 by real-time PCR. Results. The prevalence of patients from 1 to 3 (19.3%, 49.1%) and from 6-15 (15.8%, 50.5%) years was revealed both in 2020 and in 2021 and the first half of 2022. No significant differences in gender were found. The largest number of cases in 2020 was registered in April (16%) and November (14%), in 2021 - in December (18%) and November (16%). The prevailing severity in both 2020 and 2021, 2022 was the average severity (63%, 72%, 93%, respectively). The main syndromes of COVID-19 have been identified: intoxication syndrome, respiratory catarrhal syndrome, bronchopulmonary, intestinal. Bilateral pneumonia was most often detected (47% in 2020, 44% in 2021, 62% in 2022), right-sided pneumonia (33% in 2020, 30% in 2021, 31% in 2022), and left-sided pneumonia (20%, 26% and 7%, respectively). The main co-morbid pathologies are noted, and cases of somatic diseases first registered against the background of COVID-19 are described.Copyright © Children Infections.All rights reserved

A smart healthcare monitoring system for patients using IoT and cloud computing

Health monitoring systems are rapidly growing in recent times, Continuous monitoring of the patients is one of the big challenges for hospitals. Smart systems have been established to track the patient present health status;we focus on monitoring the patient's blood pressure, body temperature, Heart rate. In this project we use Arduino Mega 2560 which is a microcontroller board based on the ATmega2560 comments. In this paper, Embedded C language by using Arduino is used to obtain the sensor values. IOT data cloud is used in this project. IOT is used in healthcare system to track the patients' health condition as a monitoring device. Cloud computing develops as a platform for IoT data storage, processing and analytics because of its simplicity, expandability and affordability. Transmit sensor values to Arduino and it sends to GSM and WIFI module to monitor the parameters of the patients. In this project the notifications of patient's health status are sent to the caretaker and nurse, simultaneously it is updated in webpages also for the doctor's reference. Taking in account, COVID 19 Pandemic is highly infectious and spreadable disease, so to maintain the social distance, this monitoring system is needed. It reduces the need for face-to-face appointments with doctors in hospital. ECG sensor is also used to decide the heart activity of the patients. This project aims for the patients who are in continuous monitoring and bedridden. The GSM and IOT technologies give the architecture for healthcare in this project. © 2023 Author(s).

A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19

Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin. © 2023 IEEE.

Metaheuristic secured transmission in Telecare Medical Information System (TMIS) in the face of post-COVID-19.

The outbreak of novel corona virus had led the entire world to make severe changes. A secured healthcare data transmission has been proposed through Telecare Medical Information System (TMIS) based on metaheuristic salp swarm. Patients need proper medical remote treatments in this Post-COVID-19 time from their quarantines. Secured transmission of medical data is a significant challenge of digitally overwhelmed environment. The objective is to impart the patients' data by encryption with confidentiality and integrity. Eavesdroppers can carry sniffing and spoofing in order to deluge the data. In this paper, a novel scheme on metaheuristic salp swarm based intelligence has been sculptured to encrypt electrocardiograms (ECG) for data privacy. Metaheuristic approach has been blended in cryptographic engineering to address the TMIS security issues. Session key has been derived from the weight vector of the fittest salp from the salp population. The exploration and exploitation control the movements of the salps. The proposed technique baffles the eavesdroppers by the key strength and other robustness factors. The results, thus obtained, were compared with some existing classical techniques with benchmark results. The proposed MSE and RMSE were 28,967.85, and 81.17 respectively. The time needed to decode 128 bits proposed session key was 8.66 × 1052 years. The proposed cryptographic time was 8.8 s.

COVID Detection Using ECG Image Reports: A Survey

COVID-19 is one of the greatest pandemics that threaten individuals, especially the elders. It was first reported inWuhan, China in 2019. It was discovered recently that COVID-19 disease can be detected using three main protocols. The first protocol is based on Polymerase Chain reaction (PCR), while the second protocol is based on lung chest (ultrasound, X-ray, and CT-Scan), and the final protocol is based on the ECG image reports. This review aims to present a survey on the methodologies and algorithms applied for the detection of COVID disease using electrocardiogram (ECG). In this study, various papers were presented for determining how the COVID can be diagnosed using ECG image reports relying on symptoms and changes in the ECG peaks and intervals. In addition to this, other studies are presented on techniques applied to the ECG reports for the detection of COVID. Also, the main limitations and future works are illustrated. It can be concluded that COVID can be detected with high accuracy using ECG reports and it is even more efficient than other protocols. Finally, based on the performance of the studies it can be shown that the ECG image report is close to an acceptable level in the detection of COVID disease.

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening.

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.

"Cardioxy" - A Novel and Portable Instrumentation Amplifier based and IoT enabled ECG device

During the COVID-19 pandemic the healthcare facilities all over world collapsed due to shortage of essential biomedical devices. ECG devices are one of those crucial instruments required for tracing electrical activities of heart. Due to the high cost of gold standard ECG devices used in the medical industries, the availability of on-demand ECG devices was not accessible to everyone. Thus, the need of portable, low cost, on-demand ECG device was needful at the earliest. In this paper we propose a novel, versatile, 3-lead, IoT enabled, LM324/LM741 operational amplifiers in instrumentation amplifier configuration Electrocardiogram machine that is aimed towards providing accurate information about the electrical activity of our heart in real time. In this attempt, we have come up with an analogue circuit design consisting of multiple operational amplifier IC based fundamental circuit blocks. The prototype is designed in such a way that the output of ECG can be visualised worldwide using IoT. © 2023 IEEE.

Remotely Controlled Real-Time Health Surveillance System of COVID-19 Patients

This work focuses on the development of a portable physiological monitoring framework that can continuously monitor the patient's heartbeat, oxygen levels, temperature, ECG measurement, blood pressure, and other fundamental patient's data. As a result of this, the workload and the chances of being infected by COVID-19 of the health workers will be reduced and an efficient patient monitoring system can be maintained. In this paper, an IoT based continuous monitoring system has been developed to monitor all COVID-19 patient conditions and store patient data in the cloud server using Wi-Fi Module-based remote communication. In this monitoring system, data stored on IoT platform can be accessed by an authorized individual and ailments can be examined by the doctors from a distance based on the values obtained. If a patient's physical condition deteriorates, the doctor will immediately receive the emergency alert notification. This model proposed in this research work would be extremely important in dealing with the Corona epidemic around the world. © 2022 IEEE.

Myocarditis and pericarditis after vaccination for COVID-19.

Two previously healthy males presented to the emergency symptoms with signs of pericarditis/myocarditis after being vaccinated with an mRNA vaccine for COVID-19.

The use of face masks and the impact on heart rate variability during baseline ECG

Sumary We compared electrocardiograms (ECGs) findings with one year difference between each other with and without use of face mask at the moment to be tested. The first ECG was done one year before without face mask, and the second ECG with a mask one year later after 3 months of mandatory use for epidemiological COVID-19 pandemic justifications in healty youth elite athletes. Results Regarding heart rate variability (HRV), an increase in RMSSD was recorded when the test was performed with a mask (M): 108.5 ± 90 ms vs. No mask (NM): 72.9 ± 54.2 ms (p <0.002). And also an increase in SDNN, when the test was done with a M: 86.2 ± 47.2 ms vs. NM: 65.9 ± 43.5 ms (p <0.036). Conclusions The results on ECG are consistent with the increasing predominance of parasympathetic regulation, which is responsible for regulation of the autonomic loop when the subject is using face mask.

An effective ECG signal compression algorithm with self controlled reconstruction quality.

Electrocardiogram (ECG) signals are frequently used in the continuous monitoring of heart patients. These recordings generate huge data, which is difficult to store or transmit in telehealth applications. In the above context, this work proposes an efficient novel compression algorithm by integrating the tunable-Q wavelet transform (TQWT) with coronavirus herd immunity optimizer (CHIO). Additionally, this algorithm facilitates the self-adaptive nature to regulate the reconstruction quality by limiting the error parameter. CHIO is a human perception-based algorithm, used to select optimum TQWT parameters, where decomposition level of TQWT is optimized for the first time in the field of ECG compression. The obtained transform coefficients are then thresholded, quantized, and encoded to improve the compression further. The proposed work is tested on MIT-BIH arrhythmia database. The compression and optimization performance using CHIO is also compared with well-established optimization algorithms. The compression performance is measured in terms of compression ratio, signal-to-noise ratio, percent root mean square difference, quality score, and correlation coefficient.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review.

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.

COVID-19 associated Brugada pattern electrocardiogram: Systematic review of case reports

Background: Fever is a common clinical manifestation of COVID-19 infection. Fever has also been associated with unmasking Brugada pattern ECG in patients and may result in life-threatening arrhythmia. Little is known regarding COVID-19 associated Brugada pattern ECG. There is paucity of data and guidance in how to manage these patients. Method(s): To identify all published case reports, the latest Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed. A literature search was conducted using PubMed, EMBASE, and Scopus through September 2021. A systematic review was performed to identify the incidence, clinical characteristics, and management outcomes of COVID-19 patients with a Brugada pattern ECG. Result(s): A total of 18 cases were collected. The mean age was 47.1 years and 11.1% were women. No patient had prior confirmed diagnosis of Brugada syndrome. The most common presenting clinical symptoms were fever (83.3%), chest pain (38.8%), shortness of breath (38.8%), and syncope (16.6%). All 18 patients presented with type 1 Brugada pattern ECG. Four patients (22.2%) underwent left heart catheterization, and none demonstrated the presence of obstructive coronary disease. The most common reported therapies included antipyretics (55.5%), hydroxychloroquine (27.7%), and antibiotics (16.6%). One patient (5.5%) died during hospitalization. Three patients (16.6%) who presented with syncope received either an implantable cardioverter defibrillator or wearable cardioverter defibrillator at discharge. At follow up, thirteen patients (72.2%) had resolution of type 1 Brugada pattern ECG. Conclusion(s): COVID-19 associated Brugada pattern ECG is rare. Most patients may see resolution of the ECG pattern once their symptoms have improved. Increased awareness and timely use of antipyretics is warranted in this population.

Prevalence and diagnostic significance of novel 12-lead ECG patterns following COVID-19 infection in elite soccer players

Background: Identification of athletes with cardiac inflammation following COVID-19 can prevent exercise fatalities. The efficacy of pre and post COVID-19 infection electrocardiograms (ECGs) for detecting athletes with myopericarditis has never been reported. We aimed to assess the prevalence and diagnostic significance of novel 12-lead ECG patterns following COVID-19 infection in elite soccer players. Method(s): We conducted a multicentre study over a 2-year period involving 5 centres and 34 clubs and compared pre COVID and post COVID ECG changes in 455 consecutive athletes. ECGs were reported in accordance with the International recommendations for ECG interpretation in athletes. The following patterns were considered abnormal if they were not detected on the pre COVID-19 infection ECG: (a) biphasic T-waves;(b) reduction in T-wave amplitude by 50% in contiguous leads;(c) ST-segment depression;(d) J-point and ST-segment elevation >0.2 mV in the precordial leads and >0.1 mV in the limb leads;(e) tall T-waves >=1.0 mV (f) low QRS-amplitude in >3 limb leads and (g) complete right bundle branch block. Athletes exhibiting novel ECG changes underwent cardiovascular magnetic resonance (CMR) scans. One club mandated CMR scans for all 28 (6%) athletes, despite the absence of cardiac symptoms or ECG changes. Result(s): Athletes were aged 22+/-5 years (89% male and 57% white). 65 (14%) athletes reported cardiac symptoms. The mean duration of illness was 3+/-4 days. The post COVID ECG was performed 14+/-16 days following a positive PCR. 440 (97%) athletes had an unchanged post COVID- 19 ECG. Of these, 3 (0.6%) had cardiac symptoms and CMRs resulted in a diagnosis of pericarditis. 15 (3%) athletes demonstrated novel ECG changes following COVID-19 infection. Among athletes who demonstrated novel ECG changes, 10 (67%) reported cardiac symptoms. 13 (87%) athletes with novel ECG changes were diagnosed with inflammatory cardiac sequelae;pericarditis (n=6), healed myocarditis (n=3), definitive myocarditis (n=2), and possible/probable myocarditis (n=2). The overall prevalence of inflammatory cardiac sequelae based on novel ECG changes was 2.8%. None of the 28 (6%) athletes, who underwent a CMR, in the absence of cardiac symptoms or novel ECG changes revealed any abnormalities. Athletes revealing novel ECG changes, had a higher prevalence of cardiac symptoms (67% v 12% p<0.0001) and longer symptom duration (8+/-8 days v 2+/-4 days;p<0.0001) compared with athletes without novel ECG changes. Among athletes without cardiac symptoms, the additional yield of novel ECG changes to detect cardiac inflammation was 20% (n=3). Conclusion(s): 3% of elite soccer players demonstrated novel ECG changes post COVID-19 infection, of which almost 90% were diagnosed with cardiac inflammation during subsequent investigation. Most athletes with novel ECG changes exhibited cardiac symptoms. Novel ECGs changes contributed to a diagnosis of cardiac inflammation in 20% of athletes without cardiac symptoms.

Analysis of fatal cardiac adverse events caused by chloroquine: a study based on the related data from the WHO global database of individual case safety reports. TT -:Who.

Objective: To analyze the clinical characteristics of fatal cardiac adverse events associated with chloroquine, which was recommended for the antiviral treatment of novel coronavirus pneumonia, and provide reference for clinical safe drug use. Method(s): The fatal cardiac adverse events associated with chloroquine were searched from the World Health Organization global database of individual case safety reports (VigiBase). The clinical characteristics of the individual cases with well-documented reports (VigiGrade completeness score >=0.80 or with detailed original reports) were analyzed. The adverse events were coded using the systematic organ classification (SOC) and preferred term (PT) of Medical Dictionary for Regulatory Activities (MedDRA) version 22.1 of International Conference on Harmonization (ICH). Result(s): Up to 23 February 2020, a total of 45 reports of fatal heart injuries related to chloroquine were reported in VigiBase, which were from 16 countries. Of them, 30 reports were fully informative. Among the 30 reports,20 cases developed fatal cardiac adverse events after a single large dose of chloroquine. Of them, 17 cases' fatal cardiac adverse events were caused by overdose of chloroquine (15 cases were suicide or suspected suicide, and 2 children took chloroquine by mistake);3 cases' fatal cardiac adverse events were caused in clinical treatment;18 cases showed arrhythmia and cardiac arrest;6 cases showed prolonged QRS wave or QT interval;6 cases were with hypokalemia, including 4 severe ones. Among the 30 reports, 10 cases developed fatal cardiac adverse events after multiple administration of chloroquine, of which 4 cases were treated with chloroquine for 23 days to 2 months and died of heart failure, cardiac arrest or myocardial infarction;6 cases were treated with chloroquine for 20 months to 29 years and all of them had cardiomyopathy, which were confirmed by endomyocardial biopsy to be caused by chloroquine in 3 cases. Conclusion(s): Cardiac toxicity was the primary cause of fatal adverse events caused by chloroquine;the main manifestation of single large dose of chloroquine was arrhythmia and the manifestation of multiple administration was cardiomyopathy.Copyright © 2020 by the Chinese Medical Association.

Clinical Course and Short-Term Follow-up of SARS-CoV-2 Vaccine-Related Myocarditis in Children and Adolescents within the Prospective German Registry for Suspected Myocarditis "MYKKE

Background: Several studies described occurrence of myocarditis after SARS-CoV-2 vaccination in pediatric patients. Weaimed to characterize the clinical course of myocarditis following SARS-CoV2 vaccination including follow-up data within the prospective German registry for suspected myocarditis in children and adolescents "MYKKE." Method: Patients younger than 18 years with suspected myocarditis and onset of symptoms within 21 days followingSARS-CoV2 vaccination were enrolled within the MYKKE registry. The suspect of myocarditis is valid in patients with clinical symptoms and diagnostic findings typically seen in myocarditis. Clinical data are monitored at initial admission and duringshort-term and long-term follow-up. Result(s): Between July 2021 and August 2022, a total of 48 patients with a median age of 16.2 years (IQR: 15.2-16.8)were enrolled by 13 centers, 88% male. Onset of symptoms occurred at a median of 3 days (IQR: 2-7) after vaccine administration, most frequently after the second dose (52%). Most common symptoms at initial admission were anginapectoris (81%), fatigue (56%), dyspnea (24%) and documented arrhythmias (17%). Initial ECG abnormalities included ST-elevation (48%) and T-wave inversion (23%). Elevated Tropon in was observed in 32 patients (67%) and in 19 cases (40%)NT-proBNP was above the normal range with a median level of 171 pg/mL (IQR: 32-501). 11 (23%) patients presentedwith mildly reduced systolic function at initial echocardiography or cardiac MRI. In 40 patients cardiac MRI and/orendomyocardial biopsy was performed (83%) and diagnosis of myocarditis could be verified in 27 cases (68%). Thirty-nine patients underwent short-term follow-up with a median of 2.8 months (IQR: 1.9-3.9) after discharge. 19 patients (49%)presented with either clinical symptoms (n = 9) and/or diagnostic abnormalities (n = 16) at follow-up. 12 patients (38%)still had medical treatment. Except for one patient with malign arrhythmias (ventricular tachycardia), no major cardiac adverse events were observed during initial admission and follow-up. Conclusion(s): Our data confirm that SARS-CoV-2 vaccine-related myocarditis is characterized by a mild disease course. However, after short-term follow-up a considerable number of patients still presented with symptoms and/or diagnostic abnormalities. Data on long-term follow-up are awaited.

Analysis Performance Of Image Processing Technique Its Application by Decision Support Systems On Covid-19 Disease Prediction Using Convolution Neural Network

The Covid-19 pandemic has been identified as a key issue for human society, in recent times. The presence of the infection on any human is identified according to different symptoms like cough, fever, headache, breathless and so on. However, most of the symptoms are shared by various other diseases, which makes it challenging for the medical practitioners to identify the infection. To aid the medical practitioners, there are a number of approaches designed which use different features like blood report, lung and cardiac features to detect the disease. The method captures the lung image using magnetic resonance imaging scan device and records the cardiac features. Using the image, the lung features are extracted and from the cardiac graph, the cardiac features are extracted. Similarly, from the blood samples, the features are extracted. By extracting such features from the person, the method estimates different weight measures to predict the disease. Different methods estimate the similarity of the samples in different ways to classify the input sample. However, the image processing techniques are used for different problems in medical domain;the same has been used in the detection of the disease. Also, the presence of Covid-19 is detected using different set of features by various approaches. [ FROM AUTHOR] Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A CNN for COVID-19 Detection Using ECG signals

We developed an end-to-end automatic algorithm for the detection of signs of COVID-19 virus infection in ECGs. We analyzed 12-lead ECGs from patients infected by COVID-19 (C-group) and from a control group (NC-group). The C-group (896 cases) included patients (age range [19-96] years) hospitalized at Ospedale San Matteo in Pavia (Italy) during the first 2020 pandemic outbreak. Infection was confirmed by nasal swab testing. The NC-group (also 896 cases) was built by collecting ECG in sinus rhythm from 3 datasets: Georgia ECG (USA), PTB-XL (Germany) and CPSC 2018 (China). Control ECGs were matched by gender, age and heart rate. An additional control group, only used for testing, was extracted from the Ningbo (China) database. A 4-layers convolutional neural network (CNN), with increasing filter size plus a final fully connected (FC) layer, was designed to classify C vs NC-group. The CNN was trained and k-fold cross validated (k=7) on 1536 ECGs (1316 for testing-220 for validation). Every fold model was used to classify the remaining, separate common test set of 256 ECGs. The accuracy was 0.86 ± 0.01 on validation, 0.86 ± 0.01 on the test set. The FPR on the NC-group was 0.14 ± 0.03 on validation, 0.13 ± 0.02 on test and 0.10 ± 0.01 on the Ningbo test set (p > 0.05,ns) showing that no bias was induced by the selection of datasets. © 2022 Creative Commons.