Articles tagged with Electrocardiography
Researchers developed DeepHHF, an AI model that identifies patients at high risk of heart failure up to five years in advance. The model analyzes standard ECG recordings and detects subtle abnormalities that are often imperceptible to the human eye.
A system developed by Kaunas University of Technology enables continuous heart rhythm monitoring, allowing for more detailed ECG recordings and remote transmission to doctors. The technology aims to address the diagnostic gap in cardiovascular diseases, particularly for post-stroke patients at risk of ischaemic stroke.
Researchers at Viva Bem have developed an AI method that identifies states of anxiety with high accuracy using smartwatch data. The technology aims to provide a layer of proactive monitoring for users, alerting them to recurring anxiety episodes and recommending specialist consultation.
Researchers developed AI models using electronic health records and electrocardiograms to identify individuals at elevated risk for sudden cardiac arrest. The combined EHR-EKG model correctly predicted 153 of 228 high-risk patients who experienced a cardiac arrest, bringing theoretical risk into focus.
Researchers created a new polymer electrode that conforms to the skin, is comfortable, and can pick up ECG signals without gel or adhesives. The technology performed comparably to existing sensors in proof-of-concept testing, showcasing its potential for practical and cost-effective health monitoring applications.
A new multicenter study by Kumamoto University and National Cerebral and Cardiovascular Center found that working-age adults with shorter sleep durations are more likely to develop atrial fibrillation. In contrast, retirees showed no significant association between sleep duration and AF risk.
Scientists have developed a novel computational framework for real-time, personalized heart rate variability monitoring. The framework provides robust, adaptive alerts and improves the accuracy of HRV analysis by excluding artifact-prone periods.
Researchers at Mount Sinai have developed an AI-powered ECG analysis tool that shows promise in detecting Chronic Obstructive Pulmonary Disease (COPD) early. The model achieved high accuracy rates across diverse populations, including a subgroup with irregular heartbeat and smoking exposure.
Researchers developed an AI model that can detect coronary microvascular dysfunction using a common electrocardiogram, outperforming previous models in diagnostic tasks. The model can accurately identify a condition often missed in emergency department visits, providing a cost-effective and non-invasive way to diagnose serious heart co...
A new report from Northwestern University offers six practical strategies to improve the doctor-patient bedside encounter in an era dominated by AI. By employing these strategies, clinicians can strengthen patient-physician relationships, combat inequities, and reduce burnout, ultimately leading to better patient outcomes.
A new study shows that a multi-step effort to reduce unneeded pre-operative testing, using a tailored program for each hospital, can lead to significant reductions in wasteful tests. The findings have already spurred an expansion of the program to 16 more Michigan hospitals.
A new smartphone app, ECHAS, can help people determine if they are suffering from a heart attack or stroke and need medical attention. The app, developed by experts at UVA Health and other leading institutions, proved effective in identifying patients with cardiac and neurological emergencies.
A study evaluated AI-ECG and AI-powered digital stethoscope tools for detecting early signs of heart dysfunction in women aged 18-49. The findings showed promising results, with a high negative predictive value and low risk of false positives, indicating potential for quick and cost-effective screening during primary care visits.
Researchers at Mayo Clinic developed AI-ECG tools that can detect heart muscle weakness in women of childbearing age, allowing for earlier identification and management. The tools demonstrated high diagnostic performance, with an area under the curve of .94 for AI-ECG and .98 for AI digital stethoscope.
A new AI algorithm, Viz HCM, can quickly and specifically identify high-risk heart patients with hypertrophic cardiomyopathy (HCM) and provide individualized risk assessments. The algorithm's findings can help doctors prioritize the highest-risk patients for earlier appointments and treatment.
A new AI algorithm has been calibrated to quickly identify patients with hypertrophic cardiomyopathy (HCM) and provide individualized risk assessments. The tool can help prioritize high-risk patients for earlier appointments and treatment, leading to better patient outcomes.
A noise-adapted AI model using lead I ECGs estimated heart failure risk with high accuracy, suggesting a potential strategy for early detection. The study's results highlight the promise of wearable and portable ECG devices in identifying at-risk patients.
Researchers found that introducing moderate natural light into the bedroom before waking improves wakefulness and reduces sleepiness. The study suggests controlling natural light in the sleep environment may help promote a more comfortable awakening.
Researchers at the University of Cambridge have developed a handheld device that can accurately record heart sounds without requiring precise placement on the chest. This innovative device, which uses six sensors to capture clearer heart sounds, has the potential to transform heart disease screening and diagnosis.
Patients with severe ischemic stroke show impaired gastric motility and autonomic dysfunction, with reduced normal gastric slow waves and increased sympathetic activity. These findings suggest a link between gastric myoelectrical rhythms and autonomic function in ischemic stroke.
Researchers developed an innovative model, ECG-LM, that leverages large language models to interpret complex ECG signals. The model improves the accuracy and speed of heart-related diagnostics, particularly in resource-limited environments.
A new study at U-M Health shows that hospitals can focus the use of preoperative tests on high-risk patients, while safely reducing unnecessary testing in others. The result is less wasted money and resources, as well as less time wasted for patients and clinical staff.
A new wireless EKG patch developed by MG Medical Products has been shown to be as accurate as traditional EKG machines in a recent University of Cincinnati study. The patch eliminates electrode misplacement errors and reduces hospital readmission rates, particularly in skilled nursing facilities and correctional institutions.
An interdisciplinary study found associations between exposure to environmental phenols like BPA and triclocarban and altered cardiac electrical activity, particularly in women with higher body mass indexes. Researchers identified moderate changes to cardiac electrical activity that could exacerbate existing heart disease or arrhythmias.
Researchers at USC developed an approach that matches polysomnography using a single-lead echocardiogram, allowing anyone to create their own low-cost, DIY sleep-tracking device. The software significantly outperformed other EEG-less models and assesses sleep stages at the highest level.
A team of scientists from Scripps Research has developed an AI tool that can diagnose heart conditions, including heart attacks, using just three electrodes and a simpler electrocardiogram technology. The tool was tested on 238 ECGs and showed accurate clinical assessment results similar to those from original 12-lead ECGs.
Researchers built more accurate cardiovascular risk models using machine learning, finding that women are underdiagnosed due to sex-neutral criteria. The study used the UK Biobank dataset and found that electrocardiogram (EKG) tests were most effective in improving detection of cardiovascular disease in both men and women.
Research published in Heart Rhythm suggests that the three-month blanking period after atrial fibrillation (AF) ablation may not be necessary, as early AF recurrence is a predictor of late recurrence. The authors propose shortening the blanking period to one month, citing studies that show higher risks of long-term recurrence for patie...
A clinical trial found that a nasal spray called etripamil effectively and safely treated recurrent episodes of paroxysmal supraventricular tachycardia (PSVT) in patients. The study showed that two-thirds of participants experienced relief within an hour, with an average time needed for symptom relief being 17 minutes.
Researchers have demonstrated the feasibility of using wearable technology to measure the emotional responses of coffee experts during tastings. The study found significant correlations between biomedical signals and data from conventional questionnaires, confirming the viability of this approach for enhancing coffee quality assessment.
A novel prescription wristwatch uses photoplethysmography to detect atrial fibrillation with high accuracy, even in participants with darker skin tones. The Verily Study Watch bridges the gap between long-term monitoring and consumer devices, enabling clinicians to effectively use wearable data for Afib management.
A new study has provided neurophysiological evidence for videoconference fatigue, a feeling of tiredness and alienation caused by prolonged video-based communication. The study found that video conference-based lectures exhausted test subjects more than traditional in-person lectures.
A new study by UC San Francisco found that poor sleep is significantly associated with a 15% greater risk of experiencing atrial fibrillation the next day. The researchers suggest strategies like going to bed at a reasonable time, avoiding alcohol and caffeine before bedtime, and exercising regularly to improve general sleep quality.
Researchers developed a novel wearable ECG patch with active dry electrodes that improves upon traditional Ag/AgCl electrodes by increasing user comfort, reducing skin irritation, and enhancing diagnostic accuracy. The compact and lightweight design enables continuous monitoring and remote sensing capabilities.
A new wearable ECG device weighs only 10 grams and has just three 'dry' electrodes that are almost invisibly thin, capturing the heart's electrical activity with comparable precision to market devices. The device can be used for continuous monitoring and is ideal for patients in remote healthcare and ambulatory care settings.
A new study published in eClinicalMedicine suggests that ECG-AI can flag some risks years sooner than current risk calculator equations by identifying signs of coronary artery disease, such as calcification and blockages. The technology has the potential to save more lives by identifying people who do not know they have coronary disease.
Northwestern University engineers developed a nanoelectronic device that can perform accurate machine-learning classification tasks in real time with reduced power consumption. The device can be deployed directly in wearable electronics for real-time detection and data processing, enabling more rapid intervention for health emergencies.
A recent study from Mayo Clinic found that exercise test abnormalities, such as low functional aerobic capacity, predicted non-cardiovascular causes of death like cancer, alongside cardiovascular-related deaths. The study's findings suggest that clinicians should focus on data beyond ECG results to identify patients at risk.
Researchers at Brigham and Women's Hospital have developed an AI model that screens electrocardiograms (ECG) for signs of atrial septal defects (ASD), a common adult congenital heart disease. The model correctly detected ASD in 93.7% of cases, outperforming traditional methods.
A new machine learning model developed by University of Pittsburgh researchers uses electrocardiogram (ECG) readings to diagnose and classify heart attacks faster and more accurately than current approaches. The model improves risk assessment, helping patients receive appropriate care without delay.
Researchers at Mount Sinai have developed an AI model called HeartBEiT that can analyze electrocardiograms as language, enabling more accurate diagnoses. The model outperformed established methods in comparison tests and demonstrated improved performance with lower sample sizes.
Researchers at the University of Texas at Austin developed an ultrathin, lightweight electronic tattoo for continuous, mobile heart monitoring. The device provides two key measurements: electrocardiogram and seismocardiogram, giving clinicians a better chance to catch red flags for heart disease early.
A new study published in The European Heart Journal – Digital Health found that smart watch data can predict a higher risk of developing heart failure and irregular heart rhythms. Researchers used machine learning to analyze ECG recordings from wearable devices and identified extra beats as indicators of increased cardiovascular risk.
Researchers used artificial intelligence to analyze electrocardiogram results from 1.6 million patients, predicting mortality risk with 85% accuracy. The study aims to improve individual care and create a learning health-care system that feeds data back into the healthcare system.
Researchers found that the heart influences our sense of time, causing it to stretch or shrink with each heartbeat. The study used electrocardiography to measure heart activity and showed that temporal wrinkles in time perception are synchronized with heartbeats.
Researchers at the University of Missouri have designed a soft and breathable material that can be worn on the skin without causing discomfort. The material, made from liquid-metal elastomer composite, has integrated antibacterial and antiviral properties to prevent the formation of harmful pathogens.
Researchers from TIBI have developed an advanced electronic skin patch that provides simultaneous, continuous monitoring of multiple bodily parameters. The new E-skin patch offers enhanced flexibility, thermal cooling abilities, and fluid absorption over conventional substrates while demonstrating excellent biocompatibility and biodegr...
Researchers validate non-invasive ECGi technique to detect atrial fibrillation, providing detailed information about the electrical activity of the heart. This breakthrough reduces patients' exposure to ionising radiation and costs, making it more universal for clinical practice.
Researchers at Washington State University have developed a new screen-printing method to create stretchable and durable wearable electronics. The process uses a multi-step layering technique to create snake-like electrode structures that can be transferred onto fabric or worn directly on human skin.
Researchers from Florida Atlantic University have developed a prototype of a novel wearable device that can continuously monitor physiological parameters associated with heart failure in real-time. The device uses sensors embedded in a lightweight belt to track thoracic impedance, electrocardiogram, heart rate, and motion activity, pro...
Researchers from USTC achieved contactless ECG monitoring through a millimeter-wave radar system, demonstrating high accuracy and reliability for diagnosing cardiovascular diseases. The new method showed a median timing error of less than 14 milliseconds and a morphology accuracy higher than 90% compared to conventional ECGs.
Researchers at the University of Missouri are developing a wearable heart monitor using a breathable material with antibacterial and antiviral properties. The device will track heart health via dual signals, providing continuous monitoring for early detection of heart disease.
The use of smartwatches to screen for faulty heart rhythms needs more evaluation, according to a cardiologist. Smartwatch detection of AF can often point to cases of the disorder, but substantial work is needed to integrate this consumer-received information into optimized care. Recent studies have reported low rates of irregular heart...
Researchers found a longer PR interval and prolonged QT duration in genetically modified pig hearts after transplantation into humans, indicating signs of electrical disease. The study provides a foundation for future research to better understand xenotransplantation's effects on the heart's electrical system.
A study published in the Canadian Journal of Cardiology found that smartwatch health apps detecting atrial fibrillation generated a high rate of false positives and inconclusive results, especially in patients with certain cardiac conditions. Better algorithms and machine learning may help improve the accuracy of these devices.
Researchers found that using the Bazett formula to assess heartbeat rhythms can lead to inaccurate chemotherapy modifications, potentially affecting patient care. Standardizing formulas with electrocardiograms can reduce this risk and improve treatment outcomes.
A new study published in Current Biology found that carrying crying infants for 5 minutes can promote sleep and reduce crying. The technique, known as the Transport Response, involves steady walking followed by sitting before laying the baby down to sleep. This method offers an immediate solution for parents of newborns struggling with...
Researchers analyzed data from over 3,000 participants and found no association between electrocardiogram measures and telomere length. The study suggests that ECG prolongation is age-dependent but not a marker of biological aging.
Researchers developed an AI algorithm using individual ECG heartbeats to accurately predict diabetes and pre-diabetes. The DiaBeats algorithm detected the diseases with an overall accuracy of 97%, irrespective of factors like age, gender, and coexisting metabolic disorders.
Researchers developed an AI-driven device that detects and predicts hemodynamic instability using a single ECG lead, outperforming traditional vital sign measurements with nearly 97% sensitivity. The technology has the potential to provide continuous dynamic monitoring capabilities in patients with intermittent static vital sign measur...