ECG Readings Research Articles

Optimisation of detecting chronic thromboembolic pulmonary hypertension in acute pulmonary embolism survivors: the InShape IV study.

Chronic thromboembolic pulmonary hypertension (CTEPH) is often diagnosed late in acute pulmonary embolism survivors: more efficient testing to expedite diagnosis may considerably improve patient outcomes. The InShape II algorithm safely rules out CTEPH (failure rate 0.29%) while requiring echocardiography in only 19% of patients but may be improved by adding detailed reading of the computed tomography pulmonary angiography diagnosing the index pulmonary embolism. We evaluated 12 new algorithms, incorporating the CTEPH prediction score, ECG reading, N‑terminal pro-brain natriuretic peptide levels and dedicated computed tomography pulmonary angiography reading, in the international InShape II cohort (n=341) and part of the German FOCUS cohort (n=171). Evaluation criteria included failure rate, defined as the incidence of confirmed CTEPH in pulmonary embolism patients in whom echocardiography was deemed unnecessary by the algorithm, and the overall net reclassification index compared to the InShape II algorithm. The algorithm starting with computed tomography pulmonary angiography reading of the index pulmonary embolism for six signs of CTEPH, followed by ECG/N-terminal pro-brain natriuretic peptide level assessment and echocardiography resulted in the most beneficial change compared to InShape II, with a need for echocardiography in 20% (+5%), a failure rate of 0% and a net reclassification index of +3.5%, reflecting improved performance over the InShape II algorithm. In the FOCUS cohort, this approach lowered echocardiography need to 24% (-6%) and missed no CTEPH cases, with a net reclassification index of +6.0%. Dedicated computed tomography pulmonary angiography reading of the index pulmonary embolism improved the performance of the InShape II algorithm and may improve the selection of pulmonary embolism survivors who require echocardiography to rule out CTEPH.

Study on ECG Signal Classification and Athlete Health Analysis Based on Attention Mechanism

Abstract In the training and competition process of athletes, their bodies are subjected to various levels of load and stress. As an important diagnostic tool, ECG signals can provide deep insights into the cardiac function of athletes, including heart rate, rhythm, and changes in cardiac electrical activity. By conducting a thorough examination of ECG readings, we are able to quickly identify possible heart conditions or irregularities, which is essential for preserving the heart health of athletes. However, ECG signals are highly complex and multidimensional. To accurately classify these signals, it is necessary to select the most representative and discriminative features. However, this is not an easy task, and the selection of effective features remains a pressing issue. To address this problem, this paper proposes the CSNet classification network model. This framework eradicates disruptions in electrocardiogram signals, performs attribute extraction via a direct network configuration, and combines channel focus mechanisms and spatial focus mechanisms to enhance attribute representation and categorization capabilities. Furthermore, to retain the temporal information of ECG signals, we introduce the Gated Recurrent Unit (GRU), which helps to better capture temporal patterns and dependencies in the signals, thus enabling more accurate classification of ECG signals.

Cardiology intensive care unit nurse in italy: definition of advanced competences. IN-CORE study

Abstract Background/Introduction Since the establishment of the Cardiology Intensive Care Units to date, there have been many scientific and technological progresses that have involved not only the pathways of diagnosis and treatment of patients in this specialist area, but also the specialist competencies that nurses must acquire in order to work in these settings. The epidemiological picture and incidence of comorbidities suggest the care of patients requiring intensive and specialised care, with the use of high technology and circulatory support systems, changing the usual setting of a cardiology intensive care unit to a high-intensity cardiology intensive care unit. To date, a few studies have been described in the literature that have mapped the competences of the cardiology intensive care unit nurse, but the heterogeneity of training education at a European level has not yet made a reference framework for this specialist area generalisable. (Endacott R., et al. 2022). In Italy, there are no specific postgraduate courses focused on specialist cardiac intensive care skills development for nurses. To develop such courses, content incorporating appropriate competencies is required and this study was designed to meet this. Purpose To identify the core competencies of nurses working in High-intensity cardiology intensive care units (ICUs) in Italy to develop national competence framework. Methods A three-round e-Delphi study was conducted with a group of 30 experienced cardiac intensive care unit nurses; 28 completed all three rounds. The first round asked each participant to write down five competences, which they were asked to prioritize in the second round. In the third round, they were asked to prioritize again, considering the scores made in the second round. The final list of competencies was the one that achieved a greater than 70% agreement among the participants. Results We identified 12 core competencies that include a few competence areas, including high-tech care, emergency management, teamwork, and critical decision-making. Only slight differences emerged among the panel of experts regarding the priority given to certain core competencies, such as ECG reading, emergency management and technical and practical skills in the intensive care area. Conclusions This is the first study in Italy to identify the core and advanced competencies of nurses working in high level cardiac intensive care units. This study provides an important basis for the development of postgraduate educational programme and a framework for the definition of national standards to be implemented in clinical practice.

Smart Health Recommendation using Vitals

The term "artificial intelligence" (AI) in healthcare means the application of machine-learning algorithms and software to mimic how humans think in the analysis, presentation, and comprehension of intricate medical and health care data, or to outperform human capabilities by offering novel approaches to illness diagnosis, treatment, and prevention. New customer wellbeing gadgets are being created to effortlessly screen various physiological boundaries on an ordinary premise. A considerable lot of these crucial sign estimation gadgets concentrated on in a clinical setting as now spread broadly all through the purchaser market. The purpose of this investigation was to examine the exactness and accuracy of pulse (HR), blood pressure (BP) and estimations by taking dataset through smartwatch. This paper provides information and methods employed in the health monitoring system utilizing K-means Clustering tasks such as monitoring blood pressure or ECG readings, Db scan for arranging unstructured data, SVM for Forecasting healthcare solutions and human health patterns and creating medical answers by combining devices, instruments, and cases. Neural Network for enhancing medical hardware, software, and instruments. Forecasting Healthcare Solutions for Utilizing machine learning for creating predictive healthcare solutions. Overall the paper gives detail knowledge about the technique used for a machine learning and artificial intelligence-based health recommendation system

Study of antiarrhythmic activity 2-(n-butylpyrrolidine)-N-(2-bromophenyl)carboxamide hydrochloride

Objective. To study the efficacy of a new derivative 2-(alkylpyrrolidine)-N-(aryl)carboxamide with high antiarrhythmic activity. Materials and methods. To study the antiarrhythmic activity of the compound, the experiment was carried out on models of arrhythmia caused by intravenous administration of aconitine and adrenaline. The effect was estimated by its ability to prevent the onset of arrhythmia, prolong the survival time of the animals or by the duration of an arrhythmia attack. In addition, the electrocardiogram of awake rats was analyzed. The studied compound and the comparison drug (lidocaine) were injected to the animals intravenously in effective antiarrhythmic doses. Results. In aconitine arrhythmia 2-(n-butylpyrrolidine)-N-(2-bromophenyl) carboxamide hydrochloride provides statistically significant limitation of the duration of arrhythmia attacks in experimental animals (1.7 times) in comparison with the control and also reduction of arrhythmia duration in comparison with lidocaine (2.5 times); besides, this compound guarantees animals’ survival in 100 % of cases. When causing arrhythmia by adrenaline administration, the compound does not prevent the occurrence of cardiac rhythm disorder. The electrocardiogram readings of animals do not change significantly. Conclusions. 2-(n-butylpyrrolidine)-N-(2-bromophenyl) carboxamide hydrochloride (compound K-23) shows visible activity in models of arrhythmia caused by administration of aconitine and calcium chloride, which may indicate its ability to impede the sodium flow through the cell membrane by slowing depolarization of cardiomyocytes. Since the compound studied, demonstrates high antiarrhythmic activity without changing the ECG readings, the drug created on its basis may be effective.

Impact of Vernix Caseosa Distribution on Non-Invasive Fetal ECG Morphology: A Computational Study.

Accurate monitoring of fetal cardiac activity is paramount for the early detection of fetal pathologies during pregnancy. Non-invasive fetal ECG has shown promise, offering advantages over traditional fetal monitoring techniques such as cardiotocography. However, extracting fetal signals from maternal abdominal recordings poses challenges, particularly due to the presence of the vernix caseosa, a fatty layer surrounding the fetus. This study aims to investigate how vernix caseosa distribution influences ECG morphology in a novel computational framework. A multi-compartment volume conductor, integrating fetal and maternal hearts, fetal body, amniotic fluid, and vernix caseosa embedded in a maternal torso, is constructed. Vernix caseosa distribution is varied homogeneously and heterogeneously on the fetal body. Fetal cardiac activity is simulated using a pseudo-bidomain formulation. Resulting body surface potential and ECG is analysed in terms of RDM, lnMAG, QRS complex and T-wave morphology at six abdominal sensor placements. Results indicate vernix caseosa conductive properties and presence on the fetal head do not notably interfere with ECG readings, except in rare instances where the signal strength is extremely low. Signal strength is reduced more when covering back compared to front of the fetus. Nonetheless, both scenarios have a notable impact on ECG signal and T/QRS ratio, aligning with earlier findings suggesting caution in interpreting T/QRS ratio when vernix caseosa is present. The presence of vernix caseosa warrants careful consideration regarding ECG and especially T/QRS ratio interpretation. The study contributes to advancing the understanding of non-invasive fetal ECG.

ACT5 Electrical Impedance Tomography System.

This article introduces the Adaptive Current Tomograph 5 (ACT5) Electrical Impedance Tomography (EIT) system. ACT5 is a 32 electrode applied-current multiple-source EIT system that can display real-time images of conductivity and susceptivity at 27 frames per second. The adaptive current sources in ACT5 can apply fully programmable current patterns with frequencies varying from 5 kHz to 500 kHz. The system also displays real-time ECG readings during the EIT imaging process. The hardware and software design and specifications are presented, including the current source design, FPGA hardware, safety features, calibration, and shunt impedance measurement. Images of conductivity and susceptivity are presented from ACT5 data collected on tank phantoms and a human subject illustrating the system's ability to provide real-time images of pulsatile perfusion and ECG traces. The portability, high signal-to-noise ratio, and flexibility of applied currents over a wide range of frequencies enable this instrument to be used to obtain useful human subject data with relative clinical ease.

English

Objective: The objective of this study is to determine the prevalence of electrocardiographic abnormalities that could endanger the lives of elite and sub-elite professional athletes based on normal, borderline, and abnormal findings described in international recommendations. Material and method: This retrospective observational study was performed only on men elite football players, men elite baseball players, men elite basketball players, and men sub-elite football players (second division, third division, U-15, U-17, and U-20). Data were collected from pre-competition ECGs performed by team-affiliated physicians in the 2012 – 2019 preseasons of active-roster athletes and sub-elite players. The qualitative characteristics of each ECG were analyzed using the international recommendations for electrocardiographic interpretation in athletes to detect accepted training-related ECG findings and findings classified as borderline and abnormal. Results: A total of 716 ECGs were included. Common training-related ECG changes were found in 63.1%; sinus bradycardia was the most prevalent training-related ECG change (47.2%). The prevalence of borderline ECG readings among all the participants was 3.9%; the most frequent change was right axis deviation. The prevalence of abnormal ECG findings overall was 4.2%. Conclusion: Electrocardiographic changes in athletes are frequently seen; however, a constant review of changes within abnormal or borderline parameters is recommended. It is suggested that further research studies should study the electrocardiographic changes in elite and sub-elite athletes and compare these changes considering the biological sex of the athletes to see if there are any differences.

Heart Disease Prediction using Machine Learning Algorithms

Presently, health conditions are on the rise primarily due to lifestyle factors and genetic predispositions. Among these, heart disease has notably surged, posing a significant risk to people's lives. Each person possesses unique benchmarks for vital health indicators like blood pressure, cholesterol levels, and pulse rates. However, according to established medical standards, normal values typically fall within certain ranges: Blood pressure ideally registers at 120/90, cholesterol between 100-129 mg/dL, pulse rate around 72, fasting blood sugar level at 100 mg/dL, heart rate ranging from 60-100 beats per minute (bpm), normal ECG readings, and major vessel widths spanning from 25 mm (1 inch) in the aorta to a mere 8 μm in capillaries. This study explores various classification techniques employed to predict the risk levels of individuals based on parameters such as age, gender, blood pressure, cholesterol levels, and pulse rate. The "Disease Prediction" system relies on predictive modeling to anticipate a user's disease risk by analyzing symptoms provided as input. By evaluating user-input symptoms, the system computes the probability of specific diseases as an output. Disease prediction involves the implementation of five techniques: Naïve Bayes, KNN, Decision Tree, Linear Regression, and Random Forest Algorithms. These methods gauge the likelihood of an individual developing a particular ailment. Consequently, the collective average prediction accuracy probability stands at an impressive 83%. Keywords: Naïve Bayes, KNN, Decision Tree, Linear Regression & Random Forest.

Update on Myocarditis: From Etiology and Clinical Picture to Modern Diagnostics and Methods of Treatment.

Although the frequency of myocarditis in the general population is very difficult to accurately determine due to the large number of asymptomatic cases, the incidence of this disease is increasing significantly due to better defined criteria for diagnosis and the development of modern diagnostic methods. The multitude of different etiological factors, the diversity of the clinical picture, and the variability of the diagnostic findings make this disease often demanding both for the selection of the diagnostic modality and for the proper therapeutic approach. The previously known most common viral etiology of this disease is today overshadowed by new findings based on immune-mediated processes, associated with diseases that in their natural course can lead to myocardial involvement, as well as the iatrogenic cause of myocarditis, which is due to use of immune checkpoint inhibitors in the treatment of cancer patients. Suspecting that a patient with polymorphic and non-specific clinical signs and symptoms, such as changes in ECG and echocardiography readings, has myocarditis is the starting point in the diagnostic algorithm. Cardio magnetic resonance imaging is non-invasive and is the gold standard for diagnosis and clinical follow-up of these patients. Endomyocardial biopsy as an invasive method is the diagnostic choice in life-threatening cases with suspicion of fulminant myocarditis where the diagnosis has not yet established or there is no adequate response to the applied therapeutic regimen. The treatment of myocarditis is increasingly demanding and includes conservative methods of treating heart failure, immunomodulatory and immunospressive therapy, methods of mechanical circulatory support, and heart transplantation. The goal of developing new diagnostic and therapeutic methods is to reduce mortality from this complex disease, which is still high.

Assessment of real-time electrocardiogram effects on interpretation quality by emergency physicians

BackgroundElectrocardiogram (ECG) is one of the most commonly performed examinations in emergency medicine. The literature suggests that one-third of ECG interpretations contain errors and can lead to clinical adverse outcomes. The purpose of this study was to assess the quality of real-time ECG interpretation by senior emergency physicians compared to cardiologists and an ECG expert.MethodsThis was a prospective study in two university emergency departments and one emergency medical service. All ECGs were performed and interpreted over five weeks by a senior emergency physician (EP) and then by a cardiologist using the same questionnaire. In case of mismatch between EP and the cardiologist our expert had the final word. The ratio of agreement between both interpretations and the kappa (k) coefficient characterizing the identification of major abnormalities defined the reading ability of the emergency physicians.ResultsA total of 905 ECGs were analyzed, of which 705 (78%) resulted in a similar interpretation between emergency physicians and cardiologists/expert. However, the interpretations of emergency physicians and cardiologists for the identification of major abnormalities coincided in only 66% (k: 0.59 (95% confidence interval (CI): 0.54–0.65); P-value = 1.64e-92). ECGs were correctly classified by emergency physicians according to their emergency level in 82% of cases (k: 0.73 (95% CI: 0.70–0.77); P-value ≈ 0). Emergency physicians correctly recognized normal ECGs (sensitivity = 0.91).ConclusionOur study suggested gaps in the identification of major abnormalities among emergency physicians. The initial and ongoing training of emergency physicians in ECG reading deserves to be improved.

KNOWLEDGE AND PRACTICE ON ELECTROCARDIOGRAM INTERPRETATION AMONG NURSES AT NATIONAL HEART INSTITUTE

Coronary heart disease (CHD) is the top killer globally, including in Malaysia. Patients with symptoms of chest pain are recommended to get an electrocardiogram immediately, in less than 10 minutes, to detect necrosis of heart tissue due to acute myocardial infarction (AMI). This cross-sectional study conducted among nurses at the National Heart Institute (NHI), Kuala Lumpur, Malaysia was aimed to identify the level of knowledge and practice of ECG interpretation among nurses at NHI. This current study used convenient sampling and involved 255 participants. Overall, 239 participants (93.7%) have good ECG interpretation knowledge and 88.2% (n = 225) have good practice on ECG. There was an association between the level of knowledge and practice regarding ECG readings (p = 0.01). In conclusion, the level of knowledge and practice among nurses in NIH was considered good.

Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data

BackgroundComputer-assisted interpretation of single-lead ECG is the preliminary method for clinicians to flag and further evaluate an arrhythmia of clinical importance for acutely ill patients. Critical scrutiny of novel detection...

ECG and NIBP Simulators in One Device Display on TFT Nextion

Accurate monitoring of NIBP (Non-Invasive Blood Pressure) parameters using vital sign monitors is crucial for patient care. Therefore, calibration of vital sign monitors is essential to ensure their safety and reliability. The purpose of this study is a vital sign simulator was developed, integrating ECG and NIBP parameters with a TFT Nextion display, to calibrate ECG and NIBP readings on vital sign monitors. The system utilized the Arduino Mega 2560 as the central controller and the MPX5050GP sensor for NIBP measurement and motor pump control. The NIBP parameters were measured at two settings: 60/30 and 80/50. The results showed a maximum systolic error of 3.5% and a diastolic error of 5.6% for the NIBP setting of 80/50. The largest standard deviation value of 2.05 was observed at the NIBP setting of 60/30. The highest uncertainty value of 0.5 was also found in the NIBP 60/30 setting. The obtained data indicated stable module readings within the acceptable threshold for vital sign monitor calibration. The developed vital sign simulator offers a reliable means of calibrating NIBP parameters, enabling accurate blood pressure measurements. Further research and refinement can be conducted to enhance the system's precision and expand its capabilities for calibration of additional vital sign parameters. By ensuring accurate calibration, healthcare professionals can rely on vital sign monitors for effective patient monitoring and diagnosis.

The AliveCor KardiaMobile ECG device allows electrocardiogram assessment in awake bonobos (Pan paniscus).

To determine the diagnostic utility of a smartphone-based ECG device (Alivecor KardiaMobile) in awake bonobos (Pan paniscus). 7 adult bonobos in human care. Bonobos were trained with positive reinforcement to hold 1 finger from each hand onto the KardiaMobile sensors for 30 seconds to obtain an ECG reading. Ten ECG tracings were recorded from each bonobo and evaluated by a veterinarian, a veterinary cardiologist, and a human cardiologist for tracing quality, tracing length, heart rate, identification of P-waves, and presence and quantification of premature ventricular or atrial contractions. 6 of the 7 bonobos were trained within 21 weeks to allow the collection of 10 diagnostic quality ECG tracings. The average heart rate recorded was 87 bpm (range = 60 to 118 bpm). Potential abnormalities identified by the KardiaMobile included premature ventricular contractions in 2 male bonobos and 1 premature atrial contraction in another male. There was strong agreement by reviewers in all ECG parameters except for the identification of P-waves. The Alivecor KardiaMobile device has diagnostic utility as a screening tool for use in bonobos in human care. The training was accomplished to yield diagnostic ECG readings of acceptable duration in awake bonobos. Given the prevalence of cardiovascular disease in great apes, this technology may identify a subset of great apes who may benefit from early intervention and treatment in an effort to delay the progression of cardiac disease.

IoT Based Tracking System for Patients

Abstract-Health-care Environment has developed in science and patients require ongoing monitoring of their ECG readings, body temperature, blood pressure (BP), heart rate, and other vital indicators. So, the primary goal of the working model is to use wireless technology to transmit the patient's health parameters to the doctor in case anything is abnormal. All the characteristics will be sensed, even if the patient is unconscious, and relayed to the medical professionals. So, we are proposing an innovative project to dodge such sudden death rates by using Patient Health Monitoring that uses sensor technology and uses the internet to communicate with assistance in case of problems. This system uses Temperature, Spo2, gyroscope, and heartbeat sensors for tracking patients’ health. These sensors are connected to the Arduino-Uno. A microcontroller that tracks the patient's health which is connected to an LCD display and a wireless network to transmit the information to a web server. In case of any abrupt changes in the patient’s heart rate, temperature, or blood oxygen saturation level, an alert message is sent about the patient using IoT. This tracking system also shows patients’ temperature, angular movements, and heartbeat tracked live data with timestamps over the Internetwork. Consequently, the IoT-based patient health tracking system actively monitors patient health through the internet, assists users in keeping an eye on their patients while at work, and perhaps saves lives. technology- based knowledge focused on Wireless-Sensing nodes. Key Words: Monitoring, Arduino-Uno, LCD display, temperature alert, Ubidots.

Denoising ECG Signal Using DWT with EAVO

Cardiovascular diseases are the leading cause of death across the world, and traditional methods for determining cardiac health are highly invasive and expensive. Detecting CVDs early is critical for effective treatment, yet traditional detection methods lack accessibility, accuracy, and cost-effectiveness – leaving patients with little hope of taking control of their own cardiac health. Noisy ECG signals make it difficult for health practitioners to accurately read and determine heart health. Unreliable readings can lead to misdiagnosis and needless expense. Despite the importance of ECG analysis, traditional methods of signal denoising are inefficient and can produce inaccurate results. This means that medical practitioners are struggling to obtain reliable readings, leaving them unable to accurately treat their patients and leading to a lack of confidence in the medical field. The Enhanced African Vulture Optimization (AVO) algorithm with Discrete Wavelet Transform (DWT) optimized by adaptive switching mean filtration (SMF) is proven to provide accurate denoising of the ECG signal. With this reliable method, medical professionals can quickly and accurately diagnose patients. Obtaining accurate ECG signals and interpreting them quickly is a challenge for healthcare professionals. Not only it takes a lot of time and skill but also requires specialized software to interpret the signals accurately. Healthcare professionals are facing a serious challenge when it comes to obtaining accurate ECG signals and interpreting them quickly. It requires them to spend extra time and effort, as well as specialize in the field with expensive software. Time is of the essence in healthcare and ECG readings can mean the difference between life and death. Specialized software can be expensive and time-consuming for those who don't have the resources or expertise. Our easy-to-use platform allows healthcare professionals to quickly interpret ECG signals, saving time, money, and lives! Get accurate readings. The EAVO algorithm and MIT-BIH dataset provide an effective solution to this problem. With the proposed filter built using EAVO, businesses can attain significant enhancements in reliable parameters and obtain accurate testing results in terms of SNR, MD, MSE and NRMSE.

Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting

The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI systems explainable to human decision makers. Rather than always making black boxes transparent, these approaches are at risk of painting the black boxes white, thus failing to provide a level of transparency that would increase the system’s usability and comprehensibility, or even at risk of generating new errors (i.e., white-box paradox). To address these usability-related issues, in this work we focus on the cognitive dimension of users’ perception of explanations and XAI systems. We investigated these perceptions in light of their relationship with users’ characteristics (e.g., expertise) through a questionnaire-based user study involved 44 cardiology residents and specialists in an AI-supported ECG reading task. Our results point to the relevance and correlation of the dimensions of trust, perceived quality of explanations, and tendency to defer the decision process to automation (i.e., technology dominance). This contribution calls for the evaluation of AI-based support systems from a human–AI interaction-oriented perspective, laying the ground for further investigation of XAI and its effects on decision making and user experience.

Influence of heart rate and heart rate variability on the feasibility of ultra-fast, high-pitch coronary photon-counting computed tomography angiography

Coronary computed tomography angiography has become a mainstay in diagnosing coronary artery disease and is increasingly used in screening symptomatic patients. Recently, photon-counting computed tomography (PCCT) has been introduced into clinical practice, offering higher spatial and temporal resolution. As the applied radiation dose is highly dependent on the choice of scan mode and is lowest using the ultra-fast high-pitch (FLASH) mode, guidelines for their application are needed. From a retrospective study investigating the properties of a novel photon-counting computed tomography, all patients who underwent FLASH-mode PCCT angiography were selected between January and April 2022. This resulted in a study population of 46 men and 27 women. We recorded pre- and intrascan ECG readings and calculated heart rate (maximum heart rate 73 bpm) as well heart rate variability (maximum HRV 37 bpm) as measured by the standard deviation of the heart rate. Diagnostic quality and motion artifacts scores were recorded for each coronary artery segment by consensus between two readers. We found a highly significant association between heart rate variability and image quality (p < 0.001). The heart rate itself was not independently associated with image quality. Both heart rate and heart rate variability were significantly associated with the presence of motion artifacts in a combined model. Scan heart rate variability—but not heart rate itself—is a highly significant predictor of reduced image quality on high-pitch coronary photon-counting computed tomography angiography. This may be due to better scanner architecture and an increased temporal resolution compared to conventional energy-integrating detector computed tomography, which has to be addressed in a comparison study in the future.

Uji Kinerja Sistem Denoising Sinyal Jantung atau EKG dengan Menggunakan Algoritma Empirical Mode Decomposition (EMD)

Electrocardiograms (EKGs) are signals created by the electrical activity of the heart muscle and displayed on the EKG device's monitor. Using the EKG recording, the primary characteristics for diagnosing the status of the human heart can be determined. The death rate of heart patients can be reduced through early identification of cardiac problems. In ECG readings, it is frequently affected by several disruptions caused by muscle contractions and electrode movement. Numerous investigations on ECG signal denoising techniques have been undertaken earlier. This article examines the testing of the performance of EKGs using the denoising technique based on the Empirical Mode Decomposition (EMD) algorithm. In this work, many metrics were utilized to evaluate the ECG signal denoising technique: mean square error (MSE), mean absolute error (MAE), and signal-to-noise ratio (SNR). In this investigation, the ECG data was contaminated with noise from muscle artifacts (MA), additive Gaussian white noise (AWGN), electrode movement (EM), and baseline wander (BW). The noise-contaminated ECG signal is subsequently subjected to the denoising process. Calculate the MSE, MAE, and SNR values of the signal after it has been denoised. This study includes a scenario for testing three thresholding techniques with four distinct types of noise. The performance of the hard thresholding method is superior for all types of noise. MSE is produced by AWGN, which is 0.15, 0.28, and 9.9 dB. MA noise generates MSE, MAE, and SNR values of 0.4, 0.033, and 41.0 dB, respectively. The EM noise has an MSE of 0.010, an MAE of 0.04, and an SNR of 30.8 dB. The MSE produced by BW noise is 0.008; the MAE and SNR values were 0.0356 and 28.5, respectively.