ECG analysis

Human body motions can have a significant impact on on-body electromagnetic (EM) wave propagations and affect wireless body area network (WBAN) applications. To help understand the body motion effects, an arm-swinging phantom model of the human upper body is developed in both measurement and simulation. The phantom model performance is compared to human volunteer results and shows similar time-varying behaviors. Wireless Body Area Networks (WBANs) are increasingly being used for real-time monitoring of physiological signals in patients. The ESP-WROOM-32 module is a popular Wi-Fi and Bluetooth module that can be used for developing WBAN systems. This paper presents a design and implementation of a WBAN system using the ESP-WROOM-32 module. The system includes various physiological sensors, power source, antennas, display, and software for data processing. The ESP-WROOM-32 module provides built-in Wi-Fi and Bluetooth connectivity, making it an efficient and reliable choice for developing WBAN systems. The paper also discusses the challenges and opportunities associated with the development of ESP-WROOM-32-based WBAN systems. The proposed system can be used for remote monitoring of patients in healthcare settings, sports performance monitoring, and other applications. Overall, this research demonstrates the potential of using the ESP-WROOM-32 module for developing efficient and reliable WBAN systems.

Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident type 2 diabetes in a non-diabetic population using a deep learning model (DLM) detecting prevalent type 2 diabetes using electrocardiogram (ECG). Methods In this retrospective study, participants who underwent health checkups at two tertiary hospitals in Seoul, South Korea, between Jan 1, 2001 and Dec 31, 2022 were included. Type 2 diabetes was defined as glucose ≥126 mg/ dL or glycated haemoglobin (HbA1c) ≥ 6.5%. For survival analysis on incident type 2 diabetes, we introduced an additional variable, diabetic ECG, which is determined by the DLM trained on ECG and corresponding prevalent diabetes. It was assumed that non-diabetic individuals with diabetic ECG had a higher risk of incident type 2 diabetes than those with non-diabetic ECG. The one-dimensional ResNet-based model was adopted for the DLM, and the Guided Grad-CAM was used to localise important regions of ECG. We divided the non-diabetic group into the diabetic ECG group (false positive) and the non-diabetic ECG (true negative) group according to the DLM decision, and performed a Cox proportional hazard model, considering the occurrence of type 2 diabetes more than six months after the visit. Findings 190,581 individuals were included in the study with a median follow-up period of 11.84 years. The areas under the receiver operating characteristic curve for prevalent type 2 diabetes detection were 0.816 (0.807-0.825) and 0.762 (0.754-0.770) for the internal and external validations, respectively. The model primarily focused on the QRS duration and, occasionally, P or T waves. The diabetic ECG group exhibited an increased risk of incident type 2 diabetes compared with the non-diabetic ECG group, with hazard ratios of 2.15 (1.82-2.53) and 1.92 (1.74-2.11) for internal and external validation, respectively. Interpretation In the non-diabetic group, those whose ECG was classified as diabetes by the DLM were at a higher risk of incident type 2 diabetes than those whose ECG was not. Additional clinical research on the relationship between the phenotype of ECG and diabetes to support the results and further investigation with tracked data and various ECG recording systems are suggested for future works.

The main goal of every method of automatic analysis, recognition and also understanding of any medical signal is to follow the physician’s method of observation and thinking. Apparently it is easy, because the procedures of human interpretation of biosignals are now well standardized. In fact the real way of making observations and thinking about diagnosis highly depends on the observer experience and personal skills. In this paper, a new eyetrackbased approach is proposed for quantitative assessment of these important factors. The visual experiment carried out on cardiologists of various professional experience supplied the scanpaths data for the analysis in context of observed ECG traces The proposed approach, without the verbalization necessity, allows the cardiologists know-how to be extracted, analyzed and implemented in the automatic interpretation algorithm for better emulate the human way of thinking. In can be very useful for designing of new algorithms for automatic ECG si...

The ECG interpretation process is currently fully automated and consists of various signal processing, pattern recognition and decisive procedures. The worldwide unified interpretation guidelines are differentially implemented by software manufacturers. Moreover, the most sophisticated algorithms are rarely used, since the corresponding medical cases are relatively infrequent. Our work focuses on designing all the advanced ECG interpretation routines as distributed web services. The key idea is to extend the interoperability by multiple method-specialized complementary interpretation services. The proposed architecture service concentrates the specialized processing tasks and, apart from the medical advantages, is a reasonable method preserving the inventor's intellectual property rights.

Purpose: Cardiologists use 12-lead electrocardiograph (ECG) and prolonged continuous Holter monitoring to detect abnormalities in heart rhythm, which could diagnose or hint at different cardiac diseases. However, the availability of a costly ECG machine and the expertise to interpret ECG is suboptimal in our country. The current state of the art proposes mitigating the cost and easy diagnosis by physicians with the use of low cost photoplethysmograph (PPG), which will deliver ECG information in terms of ECG fiducial parameters. Methods: The MIMIC-III matched paired datasets of PPG/Lead-II ECG signals with the six classes (right bundle branch block (RBBB), hypertension (HYPER), arrhythmia-atrial fibrillation (ARYTH-ATRFAB), dilated cardiomyopathy (DCM), complete heart block (CHB) and NORMAL) associated cardiovascular diseases (CVDs) syndromes of 30 patients with 5 minutes duration each were taken from Physionet. The proposed work tested by predicting ECG fiducial parameters (P, Q, R, S and T) through Gaussian process regression(GPR)-based predictor by using the PPG fiducial parameters with a training and testing ratio of 70:30. The performance was analyzed by different performance evaluation parameters e.g., RMSE, rRMSE, etc. Results: Although the shape of the ECG may differ in various ways from that of the normal ECG, the methodology can predict the ECG fiducials from PPG for a variety of CVD patients with a performance accuracy of 95-99% by evaluating through seven performance metrics. Conclusion: The ECG fiducial parameters can be used to analyze CVDs captured from a low-cost, handy, skincompatible continuous and non-invasive optical recording device well known as a PPG sensor.

The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.

Standard 12-lead ECG exercise testing is commonly used for screening of ischemic heart disease (IHD). We studied if high-resolution body surface potential mapping (HR-BSPM) during exercise offers advantages over current standards in noninvasive evaluation of IHD. This study was carried out on 90 IHD patients and 33 healthy controls. The 67-lead HR-BSPM was recorded at rest and during exercise. Twenty-one ECG parameters including classical ST criteria were compared. The effectiveness of methods was verified based on the results of SPECT and coronary angiography. The most effective parameters in the diagnosis of IHD were: amplitude parameter DST60 and dT parameter showing Twave morphology changes during exercise. The sensitivities/ specificities of DST60 and dT parameters for the HR-BSPM were 70/69 and 59/62%, while for the standard 12-lead ECG system they were: 63/62 and 59/56%. These results demonstrate the usefulness of HR-BSPM measurements during exercise. HR-BSPM resulted in higher sensitivities and specificities compared to the standard 12-lead exercise test. The advantage was partially associated with observed ischemic changes outside standard precordial leads position that were not visible when using the standard 12-lead exercise test. This justifies research into the optimization of the number and position of ECG leads in exercise testing.

Health aspects of the use of radiating devices, like mobile phones, are still a public concern. Stand-alone electrocardiographic systems and those built-in, more sophisticated, medical devices have become a standard tool used in everyday medical practice. GSM mobile phones might be a potential source of electromagnetic interference (EMI) which may affect reliability of medical appliances. Risk of such event is particularly high in places remote from GSM base stations in which the signal received by GSM mobile phone is weak. In such locations an increase in power of transmitted radio signal is necessary to enhance quality of the communication. In consequence, the risk of interference of electronic devices increases because of the high level of EMI. In the present paper the spatial, temporal, and spectral characteristics of the interference have been examined. The influence of GSM mobile phone on multilead ECG recordings was studied. It was observed that the electrocardiographic syste...

The study shows that in case of healthy people and those with detectable TWA, in whom no ventricular events were occurred, shape of consecutive T-loops does not change significantly from beat to beat. Detected TWA is supposed to be concordant, which is relatively benign. In electrocardiograms of patients who had ventricular tachycardia or ventricular fibrillation events, the shape of T-loops changes from beat to beat. It is supposed to be discordant TWA. It was showed that T-loop shape analysis can serve as noninvasive tool for differentiation between concordant TWA and discordant one what can help in better identification of patients at risk of sudden cardiac death.

The aim of this study was to investigate the application of wavelet denoising in noise reduction of multichannel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process were considered and whole procedures of noise reduction were implemented in MatLab environment. The Fast Wavelet Transform was use. The advantage of used denoising method is noise level decreasing in ECG signals, in which noise reduction by averaging has limited application, i.e. in case of arrhythmia, or in presence of extrasystols.

The aim of the study was to assess the relations between QRST and STT, QRS integral maps in three groups: healthy volunteers, patients without arrhythmia and patients with implanted cardioverter-defibrillator (ICD). The obtained results showed that the mean correlation coefficient between STT and QRST integral maps was highest in the group of healthy subjects while the mean correlation coefficient between QRS and QRST integral maps was lowest in the same group of subjects. The relation of depolarization phase with repolarization phase and the influence of depolarization sequence on repolarization sequence is noticeably disrupted in patients with impaired systolic function. In such cases the QRST integral maps seems to reflect depolarizationrepolarization disorder rather than only repolarization dispersion.

The aim of the study was a selection of best ECG leads to get the significant T-wave alternans signal (TWA). The group of 16 patients with implantable cardioverterdefibrillator (ICD) was examined. The 64 lead ECG system was used. Three lead sets were analyzed: 64 BSPM lead set, orthogonal lead set (XYZ) and standard 12 lead set. Patients were divided into two groups: TWA positive (TWA(+)) 5 patients and TWA negative (TWA(-)) 11 patients. In TWA(+) group mean alternans ratio was equal 5.4 (SD 8.09) and in TWA(-) group 0.73 (SD 0.26). Both standard ECG leads and XYZ leads are less sensitive to TWA than specially selected leads from 64 lead set

Abstrakt V štúdii je navrhnutá metóda na neinvazívnu identifikáciu oblastí poškodenia srdca so zmenenou repolarizáciou spôsobených lokálnou ischémiou. Metóda bola testovaná na po íta ovom modeli a na skupine pacientov. Vyhodnocuje zmeny v QRST integrálových mapách meraných na povrchu hrudníka so známou geometriou a vypo ítava ekvivalentný elektrický dipól, ktorý reprezentuje miesto, ve kos a orientáciu lézie. Testovanie metódy na po íta ovom modeli ukázalo jej schopnos lokalizova malé subendokardiálne a subepikardiálne poškodenia s chybou okolo 1 cm. Z 11 pacientov s ochorením 1 cievy, ktorí boli elektrokardiograficky mapovaní pred a po perkutálnej kardiálnej intervencii, bolo u 8 pacientov možné zmeny v QRST integrálových mapách reprezentova pomocou dipólu. Ischemické poškodenia boli lokalizované správne u 6 pacientov s ochorením avej (LAD) a u 1 pacienta s ochorením pravej koronárnej artérie (RCA), 1 lokalizácia ochorenia RCA nebola správna. Výsledky štúdie nazna ujú, že zmeny v integrálových mapách môžu pomôc pri identifikácii malých ischemických oblastí v oblasti epikardu alebo endokardu na základe ur enia parametrov ekvivalentného dipólu charakterizujúceho poškodenie. Summary A method for noninvasive identification of heart lesions with changed repolarization caused by local ischemia was proposed and tested on a model and on a group of patients. It evaluates changes in QRST integral maps measured on a chest surface of known geometry and computes an equivalent dipole representing the position, size and orientation of the lesion. Testing on a computer model indicated ability of the method to localize small subendocardial and subepicardial lesions with an error less about 1 cm. From 11 patients with single vessel stenosis mapped before and after the percutaneous cardiac intervention, differences in QRST integral maps could be represented by a dipole in 8 patients. 6 LAD and 1 RCA lesion were identified successfully, localization of 1 RCA lesion failed. Results of the study suggest that difference QRST integral maps can help in identification of small ischemic regions on the epicardial or endocardial surface by estimating parameters of an equivalent dipole characterizing the lesion.

T-wave alternans (TWA) allows for identification of patients at an increased risk of ventricular arrhythmia. Stress test, which increases heart rate in controlled manner, is used for TWA measurement. However, the TWA detection and analysis are often disturbed by muscular interference. The evaluation of wavelet based denoising methods was performed to find optimal algorithm for TWA analysis. ECG signals recorded in twelve patients with cardiac disease were analyzed. In seven of them significant Twave alternans magnitude was detected. The application of wavelet based denoising method in the pre-processing stage increases the T-wave alternans magnitude as well as the number of BSPM signals where TWA was detected.

The aim of the study was to evaluate diagnostic value of TCRT (spatial QRS-T angle) parameter in different sets of ECG leads and to select the ECG lead set, for which the TCRT parameter would have the best sensitivity and specificity in identifying patients threatened by ventricular tachycardia (VT pts). Two groups of patients after myocardial infarction were studied: 13 non VT pts and 30 VT pts. The TCRT parameter values differentiated VT pts group from non VT pts group for all analyzed sets of ECG leads. Considering sensitivity of the TCRT parameter in identifying VT patients the best set of ECG leads was selected.

This study examines the competencies of Grade 12 STEM students in interpreting motion graphs, employing a quantitative-qualitative descriptive methodology. A designed 20-item multiple-choice test was administered to evaluate students' understanding of position-time and velocity-time graphs. The findings reveal that both male (x ̅ = 7.85, s = 2.08) and female (x ̅ = 7.59, s = 2.08) students demonstrate a "Beginning" level of competency in graphical analysis of motion. Notably, students exhibited significant challenges, particularly with items related to position vs. time graphs; for instance, item 16 achieved a high correct response rate of 95.00%, while items 8 and 12 reflected only 5.00% accuracy. These results underscore substantial gaps in students' grasp of kinematics concepts, which are attributed to limited instructional time and emphasis. Consequently, the researchers advocate for remedial classes concentrating on motion graph interpretation and suggest a comprehensive curriculum review to ensure essential topics receive adequate attention. Furthermore, they recommend conducting similar studies with a larger sample size to enhance the reliability of the findings.

This paper presents a new compression scheme for single channel ECG, by delineating each ECG cycle. It uses multirate processing to normalize the varying period beats, followed by amplitude normalization. These beats are coded using vector quantization, with each beat being treated as a vector of uniform dimension. The average amplitude scale factor and the average beat period are made available at the decoder, along with the codebook of period and amplitude normalized beat vectors, to facilitate reconstruction of the signal. The actual beat period and the actual maximum amplitude of the beat are sent to the decoder by DPCM. It achieves a high quality approximation at less than 30 bits per second, with a compression ratio of around 100 : 1 to 200 : 1. To assess the technique properly we have evaluated two measures of error. Finally, the merits and demerits of the technique are discussed.

The Cardiac index is obtained by the ratio of cardiac output to the body
surface area of an individual which is between 2.5 to 4L/min/m2
[1]. The normal cardiac stroke volume ranges from 60ml to 100ml per beat and is give as an index ranging from 33-47ml/beat/ m2
[2]. The electrical pulse generated by the cardiac is converted to mechanical energy and the net output of the efficiency of the cardiac is given as K3-factor in this abstract.
Myocardial pulse generated at the SA node is measured and recorded
using ECG machine on the test of an individual. The myocardial conversion efficiency (ηmyo) is 40-50% [3] and is obtained by equation in Figure 1. Another investigation is to obtain mechanical efficiency of the cardiac which is the mechanical heart beat observed. The efficiency (ηmech) is obtained by equation in Figure 2. Theoretically, the input and output work done need to be calculated by algebraic sum of all the input vessels flow into the heart and algebraic sum of all the output vessels flow out of the heart as shown in Figure 3. The correlation between electrical and mechanical efficiency is then determine by equation in equation 1, considering any cardiac energy losses¢ [4]. The correlation factor (œ) is then obtained and recorded for that patient.

- Electrocardiography (ECG or EKG) is a recording of the electrical activity of the heart taken from a variety of angles, such that a 12‑lead ECG 'looks' at the heart electrically through a full 360 degrees. The recording is traditionally made over several seconds to allow assessment of the cardiac rhythm.



- It is a non-invasive procedure undertaken by attaching electrodes to the skin of the patient at predefined points, and recording the output via an electrocardiographic machine.



- The ECG machine relates the signal received at any given point, electrode, to that at a predefined neutral electrode, producing a resultant vector that is displayed and recorded.

Le applicazioni della Fisica e dell’Informatica alla Biomedicina includono i sistemi di individuazione di patologie (CAD, Computer-Assisted Detection) basati sul trattamento di dati provenienti da esami diagnostici (in particolare ma non limitandosi alle immagini diagnostiche quali TC, RM, etc.), gli strumenti di ausilio alla chirurgia (realta virtuale, telechirurgia), l’analisi e l’interpretazione di segnali di interesse biomedicale (per esempio segnali da elettroencefalogramma, EEG, o da elettrocardiogramma, ECG). Questo lavoro presenta una rassegna di applicazioni, in cui gli autori sono impegnati, dandone alcuni dettagli implementativi e discutendone brevemente i risultati. Le applicazioni si differenziano per il tipo di dati analizzati (serie temporali provenienti da misure EEG, oppure dati bi- o tridimensionali contenuti in immagini diagnostiche), per il distretto corporeo di intervento, le finalita, la patologia.

Background: Dengue fever, a mosquito-borne viral infection, can lead to significant cardiac complications. This study aimed to investigate the prevalence, characteristics, and clinical significance of cardiac manifestations in dengue patients at a tertiary care centre. Methods: We conducted a prospective observational study on 385 confirmed dengue patients admitted to our institution. All patients underwent clinical evaluation, electrocardiography, and echocardiography. Cardiac biomarkers were measured in patients with suspected myocardial involvement. Results: Cardiac manifestations were observed in 100 patients (26%, 95% CI: 21.7-30.3%). The most common findings were sinus bradycardia (12%), myocarditis (8%), and pericardial effusion (4%). Elevated cardiac biomarkers were found in 6% of patients. Severe dengue cases had a higher incidence of cardiac involvement compared to non-severe cases (42% vs. 20%, p <0.001). Patients with cardiac manifestations had longer hospital stays (median 7 days versus 5 days, p <0.001). Conclusion: Cardiac manifestations are common in dengue fever, with approximately one-fourth of patients showing some form of cardiac involvement. Close cardiac monitoring is crucial, especially in severe dengue cases, to improve patient outcomes.

Machine Learning (ML) requires a certain number of features (i.e., attributes) to train the model. One of the main challenges is to determine the right number and the type of such features out of the given dataset's attributes. It is not uncommon for the ML process to use dataset of available features without computing the predictive value of each. Such an approach makes the process vulnerable to overfit, predictive errors, bias, and poor generalization. Each feature in the dataset has either a unique predictive value, redundant, or irrelevant value. However, the key to better accuracy and fitting for ML is to identify the optimum set (i.e., grouping) of the right feature set with the finest matching of the feature's value. This paper proposes a novel approach to enhance the Feature Engineering and Selection (eFES) Optimization process in ML. eFES is built using a unique scheme to regulate error bounds and parallelize the addition and removal of a feature during training. eFES also invents local gain (LG) and global gain (GG) functions using 3D visualizing techniques to assist the feature grouping function (FGF). FGF scores and optimizes the participating feature, so the ML process can evolve into deciding which features to accept or reject for improved generalization of the model. To support the proposed model, this paper presents mathematical models, illustrations, algorithms, and experimental results. Miscellaneous datasets are used to validate the model building process in Python, C#, and R languages. Results show the promising state of eFES as compared to the traditional feature selection process.

The biggest cause of mortality globally is cardiac illness. If the initial diagnosis was more accurate, cardiac problems may be avoided. ECG testing is typically used as a diagnostic technique to screen for heart disorders. The electric cardiac signal is recorded by an ECG to look for various heart conditions. Utilizing a variety of datasets, a number of algorithms and methodologies have been developed to detect different heart illnesses. However, this study proposes to examine whether convolutional neural network (CNN) model and ResNet 50 can be used to identify cardiac illnesses such arrhythmia or abnormal heartbeat (AHB), myocardial infarction (MI), and previous history of MI (PMI) from electrocardiogram (ECG) trace images. A 1937 ECG image dataset from Kaggle is examined... The ECG pictures in the dataset are separated into four categories: normal, MI, AHB, and PMI. The suggested approach examined categorization of normal and various heart disorders with 99.12% accuracy (MI, AHB, and PMI

Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, are still unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, the propose approach is a useful tool for the detection and diagnosis of cardiac abnormalities. Povzetek: V članku je opisana metoda modularnih nevronskim mrež za prepoznavanje šumnih ECG signalov.

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.

This research focuses on securing networked Electrocardiogram (ECG) systems by using a quantitative approach to evaluate the effectiveness of various cybersecurity measures. With the increasing reliance on networked medical devices, these systems have become vulnerable to cyber-attacks that can compromise data integrity, system functionality, and patient safety. The study quantitatively examines the impact of encryption levels, authentication methods, and network segmentation on system security by measuring variables such as attack success rates, data breach occurrences, and unauthorized access attempts. Statistical analysis, including regression and ANOVA, is applied to determine the significance of these measures. The findings aim to provide data-driven insights into enhancing the security of networked ECG systems, ultimately contributing to safer healthcare environments.

The study's major contributions are:
1.Proposed a wavelet-based scattering transform method that can accurately separate several
kinds of ECG classes of cardiovascular disease.
2: Extracted other features such as spectral features, time domain, and frequency-time domain.
3: Analyze the results of the proposed methods on multi-class signals.
4: compare the results with other work.

Advances in screening technology have been made in tandem with the aging population and increasing atrial fibrillation (AF) prevalence. While several randomized controlled trials demonstrate the efficacy of AF screening, less evidence has been synthesized addressing the implementation and evaluation of AF screening programs. We systematically searched the PubMed database from 1st January 2000 to 18th January 2024. The search terms included "atrial fibrillation" and "screening" and their synonyms. Articles that described screening implementation, including screening methods, were included. Editorial, commentary, engineering, and basic science articles were excluded. 1767 abstracts were screened, of which 138 full articles were reviewed, and 87 studies were included: 90% from high-income, 8% from upper-middle-income and 2% from lower-middle-income countries/ regions. The screening initiatives included general practice (n = 31), remote self-screening (n = 30), pharmacy (n = 11), community centers and villages (n = 10), hospital (n = 4), and nursing home (n = 1). Most studies used handheld ECG devices (n = 72, 83%), some used wearable devices (n = 13, 15%), and two (2%) used implantable cardiac devices. Comparator groups were described in 17% (15/87) studies: all 6 remote self-screening trials showed superior AF detection rates compared to usual care (these studies applied intermittent screening using handheld ECG devices over 2 weeks to 12 months or wearing ECG patches for continuous monitoring over 2-4 weeks), but 9 trials using systematic and opportunistic screening in primary care settings showed mixed results. Among 72 studies without comparator groups, 18 reported new AF detection rates below 1%, 48 reported 1-10%, 5 reported above 10%, and one reported an AF incidence rate of 2.25% patient-years (95% CI 2.03-2.48). Only 22% (19/87) of studies reported on the implementation evaluation (12 by surveys and 7 by interviews), surveying participant acceptability, usability, and satisfaction, and some studies in general practice and pharmacy interviewing participants and qualitatively evaluating the enablers and barriers to implementation. These studies reported barriers of lack of resources and referral pathways and enablers of having a designated staff member to lead implementation at point-of-care settings. AF screening implementation studies were mainly conducted in high-income countries/ regions. Detection rates were highest in older and higher risk groups, and if longer continuous ECG monitoring was used. Few studies reported details of the implementation of AF screening programs concerning cost, scalability, or comparative effectiveness of remote technology-driven screening approaches versus lower-tech approaches such as pulse palpation.
Despite AF screening recommendations existing for some time, we seem to lack the data to effectively scale these initiatives.

Background: A variety of ECG changes occur as an aftermath of stroke. Prolongation of the QTc interval is a well-documented change. We analyzed QTc interval prolongation among patients with acute hemorrhagic strokes. Methods: This observational study was conducted at

Several electrocardiographic alterations due to hypokalemia have been described,
but the electrocardiographic presentation meeting criteria for occlusion
of the left main coronary artery is very rare. We describe a case of hypokalemia
simulating it.

In the current study we used transthoracic echocardiography to measure stroke volume (SV), heart rate (fH), and cardiac output (CO) in adult bottlenose dolphins (Tursiops truncatus), a male beluga calf (Delphinapterus leucas, body mass [Mb] range: 151-175 kg), and an adult female false killer whale (Pseudorca crassidens, estimated Mb: 500-550 kg) housed in managed care. We also recorded continuous electrocardiogram (ECG) in the beluga, bottlenose dolphin, false killer whale, killer whale (Orcinus orca), and pilot whale (Globicephala macrorhynchus) to evaluate cardiorespiratory coupling while breathing spontaneously under voluntary control. The results show that cetaceans have a strong Respiratory Sinus Arrythmia (RSA), during which both fH and SV vary within the interbreath interval, making average values dependent on the breathing frequency (fR). The RSA-corrected fH was lower for all cetaceans compared to similarly sized terrestrial mammals breathing continuously. As compared with...

The quality of life for diabetic patients is significantly improved through continuous monitoring. Integrating various technologies such as the Internet of Things (IoT), embedded software, communication technologies, artificial intelligence, and smart devices helps reduce the financial burden on the healthcare system. Advances in communication technologies have facilitated personalized and remote healthcare. To meet the growing demand for advanced e-health applications, developing intelligent healthcare systems and increasing the number of applications connected to the network is essential. Consequently, to achieve critical needs like high bandwidth and energy efficiency, the 5G network must support smart healthcare applications. This research proposes an intelligent infrastructure for monitoring diabetes patients using machine learning methods. The architecture incorporates smart devices, sensors, and mobile phones to provide comprehensive coverage of the patient\'s body. Data collected from the patient is analyzed and classified using machine learning to produce a diagnosis. Several machine learning methods were tested to evaluate the proposed prediction system, and simulation results indicated that the Sequential Minimum Optimization (SMO) method provides higher classification accuracy, sensitivity, and precision than other techniques

This paper presents methods for developing high-performance interface electrode materials designed to enhance signal collection efficacy during long-term (over 24 h) electrocardiography (ECG) monitoring. The electrode materials are fabricated by integrating commercial ECG liquid hydrogels with carbon nanotubes (CNTs), which are widely utilized in dry-electrode technologies and extensively discussed in the current scientific literature. The composite materials are either prepared by dispersing CNTs within the commercial liquid hydrogel matrix or by encasing the hydrogels in macroscopic CNT films. Both approaches ensure the optimal wetting of the epidermis via the hydrogels, while the CNTs reduce material impedance and stabilize the drying process. The resulting electrode materials maintain their softness, allowing for micro-conformal skin attachment, and are biocompatible. Empirical testing confirms that the ECG electrodes employing these hybrid hydrogels adhere to relevant standards for durations exceeding 24 h. These innovative hybrid solutions merge the benefits of both wet and dry ECG electrode technologies, potentially facilitating the extended monitoring of ECG signals and thus advancing the diagnosis and treatment of various cardiac conditions.

Approximately thirty percent of people live in poverty in rural areas. The difficulty of limited access to nursing and diagnostic services stems from the outdated healthcare infrastructure. As a result, when heart failure strikes, people frequently neglect to get help and make use of the resources that are accessible. A study suggests a smart electrocardiogram (ECG) monitoring system for heart patients based on the Internet of Things to address these problems. The ECG sensing network (data gathering), IoT cloud (data transmission), result analysis (data prediction), and monetization are the various components that make up the system. The P, Q, R, S, and T ECG signal characteristics are gathered by the ECG sensor network. For the purpose of managing future health, these signals are then pre-processed, examined, and projected down to the age level. Hypertext Transfer Protocol (HTTP) servers and message queuing telemetry transport (MQTT) systems can both access the cloudstored data. To ascertain the influence of error rate and ECG signal properties, the study used the linear regression method. The prediction evaluates the PQRST regularity variation and its applicability to an ECG monitoring device. The suggested system seeks to attain acceptable results by identifying the quality parameter values, which would ultimately lower future medical expenses and challenges for heart patients.

Le applicazioni della Fisica e dell’Informatica alla Biomedicina includono i sistemi di individuazione di patologie (CAD, Computer-Assisted Detection) basati sul trattamento di dati provenienti da esami diagnostici (in particolare ma non limitandosi alle immagini diagnostiche quali TC, RM, etc.), gli strumenti di ausilio alla chirurgia (realta virtuale, telechirurgia), l’analisi e l’interpretazione di segnali di interesse biomedicale (per esempio segnali da elettroencefalogramma, EEG, o da elettrocardiogramma, ECG). Questo lavoro presenta una rassegna di applicazioni, in cui gli autori sono impegnati, dandone alcuni dettagli implementativi e discutendone brevemente i risultati. Le applicazioni si differenziano per il tipo di dati analizzati (serie temporali provenienti da misure EEG, oppure dati bi- o tridimensionali contenuti in immagini diagnostiche), per il distretto corporeo di intervento, le finalita, la patologia.

Petrol is commonly used as a fuel for internal combustion engines. Humans and animals continued to be exposed to petrol vapor in the environment, occupationally by petroleum attendants and refinery workers, at home and on the roads. Fumes from petrol products cause environmental pollution that has resulted in various health challenges, including increased blood pressure and heart rate, which may lead to hypertension. This study is aimed at evaluating the effects of petrol on the electrocardiograms (ECG) and Cardiac troponin of Wistar rats and possible mitigation by Moringa oleifera extract. A total of 15 adult Wistar rats were randomly divided into three groups of five each: the control group (not given anything), the group exposed to petrol only and the group treated with Moringa oleifera extract after exposure to petrol. Rats were exposed to petrol vapor daily for 5 minutes over four weeks in a fume chamber with monitored room conditions, resulting in an average dosage exposure of 0.008 cm3/min/rat. Ketamine was used as an anesthetic agent, and the ECGs of the rats were measured with EDAN 10 veterinary electrocardiography. Serum cTn-I assay was analyzed at the end of the four weeks. The results showed an increase in heart rate, reduced QRS complex, increased in P wave and PR interval. Even though the cTn-I has considered revising but not statistically significant. It is concluded that petrol has effects on the electrocardiogram and cardiac troponin I of Wistar rats, and treatment with Moringa oleifera showed no remediating effect.