Validation of a Handheld 6-Lead Device for QT Interval Monitoring in Resource-Limited Settings.

Importance: Rifampin-resistant tuberculosis treatment regimens require electrocardiographic (ECG) monitoring due to the use of multiple QTc-prolonging agents. Formal 12-lead ECG devices represent a significant burden in resource-constrained clinics worldwide and a potential barrier to treatment scale-up in some settings. Objective: To evaluate the diagnostic accuracy of a handheld 6-lead ECG device within resource-constrained clinics. Design, Setting, and Participants: This diagnostic study was performed within a multicenter, pragmatic (broad eligibility criteria with no exclusions for randomized participants), phase 3 rifampin-resistant tuberculosis treatment trial (BEAT Tuberculosis [Building Evidence for Advancing New Treatment for Tuberculosis]) in South Africa. A total of 192 consecutive trial participants were assessed, and 191 were recruited for this substudy between January 21, 2021, and March 27, 2023. A low proportion (3 of 432 [0.7%]) of all screened trial participants were excluded due to a QTc interval greater than 450 milliseconds. Triplicate reference standard 12-lead ECG results were human calibrated with readers blinded to 6-lead ECG results. Main Outcomes and Measures: Diagnostic accuracy, repeatability, and feasibility of a 6-lead ECG device. Results: A total of 191 participants (median age, 36 years [IQR, 28-45 years]; 81 female participants [42.4%]; 91 participants [47.6%] living with HIV) with a median of 4 clinic visits (IQR, 3-4 visits) contributed 2070 and 2015 12-lead and 6-lead ECG assessments, respectively. Across 170 participants attending 489 total clinic visits where valid triplicate QTc measurements were available for both devices, the mean 12-lead QTc measurement was 418 milliseconds (range, 321-519 milliseconds), and the mean 6-lead QTc measurement was 422 milliseconds (range, 288-574 milliseconds; proportion of variation explained, R2 = 0.4; P < .001). At a QTc interval threshold of 500 milliseconds, the 6-lead ECG device had a negative predictive value of 99.8% (95% CI, 98.8%-99.9%) and a positive predictive value of 16.7% (95% CI, 0.4%-64.1%). The normal expected range of within-individual variability of the 6-lead ECG device was high (±50.2 milliseconds [coefficient of variation, 6.0%]) relative to the 12-lead ECG device (±22.0 milliseconds [coefficient of variation, 2.7%]). The mean (SD) increase in the 12-lead QTc measurement during treatment was 10.1 (25.8) milliseconds, with 0.8% of clinic visits (4 of 489) having a QTc interval of 500 milliseconds or more. Conclusions and Relevance: This study suggests that simplified, handheld 6-lead ECG devices are effective triage tests that could reduce the need to perform 12-lead ECG monitoring in resource-constrained settings.

Diagnostic accuracy of digital technologies compared with 12-lead ECG in the diagnosis of atrial fibrillation in adults: A protocol for a systematic review.

BACKGROUND: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in the world. AF increases the risk of stroke 5-fold, though the risk can be reduced with appropriate treatment. Therefore, early diagnosis is imperative but remains a global challenge. In low-and middle-income countries (LMICs), a lack of diagnostic equipment and under-resourced healthcare systems generate further barriers. The rapid development of digital technologies that are capable of diagnosing AF remotely and cost-effectively could prove beneficial for LMICs. However, evidence is lacking on what digital technologies exist and how they compare in regards to diagnostic accuracy. We aim to systematically review the diagnostic accuracy of all digital technologies capable of AF diagnosis. METHODS: MEDLINE, Embase and Web of Science will be searched for eligible studies. Free text terms will be combined with corresponding index terms where available and searches will not be limited by language nor time of publication. Cohort or cross-sectional studies comprising adult (≥18 years) participants will be included. Only studies that use a 12-lead ECG as the reference test (comparator) and report outcomes of sensitivity, specificity, the diagnostic odds ratio (DOR) or the positive and negative predictive value (PPV and NPV) will be included (or if they provide sufficient data to calculate these outcomes). Two reviewers will independently assess articles for inclusion, extract data using a piloted tool and assess risk of bias using the QUADAS-2 tool. The feasibility of a meta-analysis will be determined by assessing heterogeneity across the studies, grouped by index device, diagnostic threshold and setting. If a meta-analysis is feasible for any index device, pooled sensitivity and specificity will be calculated using a random effect model and presented in forest plots. DISCUSSION: The findings of our review will provide a comprehensive synthesis of the diagnostic accuracy of available digital technologies capable for diagnosing AF. Thus, this review will aid in the identification of which devices could be further trialed and implemented, particularly in a LMIC setting, to improve the early diagnosis of AF. TRIAL REGISTRATION: Systematic review registration: PROSPERO registration number is CRD42021290542. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290542.

[Wearable Smart Cardiac Care: Take Vipasyana for Example].

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the Departments of Electrical Engineering and Computer Science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

Wearable electrocardiogram devices in patients with congenital long QT syndrome: The SMART-QT study.

BACKGROUND: In patients with congenital long QT syndrome (LQTS), the risk of ventricular arrhythmia is correlated with the duration of the corrected QT interval and the changes in the ST-T wave pattern on the 12-lead surface electrocardiogram (12L-ECG). Remote monitoring of these variables could be useful. AIM: To evaluate the abilities of two wearable electrocardiogram devices (Apple Watch and KardiaMobile 6L) to provide reliable electrocardiograms in terms of corrected QT interval and ST-T wave patterns in patients with LQTS. METHODS: In a prospective multicentre study (ClinicalTrials.gov identifier: NCT04728100), a 12L-ECG, a 6-lead KardiaMobile 6L electrocardiogram and two single-lead Apple Watch electrocardiograms were recorded in patients with LQTS. The corrected QT interval and ST-T wave patterns were evaluated manually. RESULTS: Overall, 98 patients with LQTS were included; 12.2% were children and 92.8% had a pathogenic variant in an LQTS gene. The main genotypes were LQTS type 1 (40.8%), LQTS type 2 (36.7%) and LQTS type 3 (7.1%); rarer genotypes were also represented. When comparing the ST-T wave patterns obtained with the 12L-ECG, the level of agreement was moderate with the Apple Watch (k=0.593) and substantial with the KardiaMobile 6L (k=0.651). Regarding the corrected QT interval, the correlation with 12L-ECG was strong for the Apple Watch (r=0.703 in lead II) and moderate for the KardiaMobile 6L (r=0.593). There was a slight overestimation of corrected QT interval with the Apple Watch and a subtle underestimation with the KardiaMobile 6L. CONCLUSIONS: In patients with LQTS, the corrected QT interval and ST-T wave patterns obtained with the Apple Watch and the KardiaMobile 6L correlated with the 12L-ECG. Although wearable electrocardiogram devices cannot replace the 12L-ECG for the follow-up of these patients, they could be interesting additional monitoring tools.

Comparison Between a Single-Lead ECG Garment Device and a Holter Monitor: A Signal Quality Assessment.

Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.

Mobile Electrocardiograms in the Detection of Subclinical Atrial Fibrillation in High-Risk Outpatient Populations: Protocol for an Observational Study.

BACKGROUND: Single-lead, smartphone-based mobile electrocardiograms (ECGs) have the potential to provide a noninvasive, rapid, and cost-effective means of screening for atrial fibrillation (AFib) in outpatient settings. AFib has been associated with various comorbid diseases that prompt further investigation and screening methodologies for at-risk populations. A simple 30-second sinus rhythm strip from the KardiaMobile ECG (AliveCor) can provide an effective screen for cardiac rhythm abnormalities. OBJECTIVE: The aim of this study is to demonstrate the feasibility of performing Kardia-enabled ECG recordings routinely in outpatient settings in high-risk populations and its potential use in uncovering previous undiagnosed cases of AFib. Specific aim 1 is to determine the feasibility and accuracy of performing routine cardiac rhythm sampling in patients deemed at high risk for AFib. Specific aim 2 is to determine whether routine rhythm sampling in outpatient clinics with high-risk patients can be used cost-effectively in an outpatient clinic without increasing the time it takes for the patient to be seen by a physician. METHODS: Participants were recruited across 6 clinic sites across the University of Florida Health Network: University of Florida Health Nephrology, Sleep Center, Ophthalmology, Urology, Neurology, and Pre-Surgical. Participants, aged 18-99 years, who agreed to partake in the study were given a consent form and completed a questionnaire regarding their past medical history and risk factors for cardiovascular disease. Single-lead, 30-second ECGs were taken by the KardiaMobile ECG device. If patients are found to have newly diagnosed AFib, the attending physician is notified, and a 12-lead ECG or standard ECG equivalent will be ordered. RESULTS: As of March 1, 2024, a total of 2339 participants have been enrolled. Of the data collected thus far, the KardiaMobile rhythm strip reported 381 abnormal readings, which are pending analysis from a cardiologist. A total of 78 readings were labeled as possible AFib, 159 readings were labeled unclassified, and 49 were unreadable. Of note, the average age of participants was 61 (SD 10.25) years, and the average self-reported weight was 194 (SD 14.26) pounds. Additionally, 1572 (67.25%) participants report not regularly seeing a cardiologist. Regarding feasibility, the average length of enrolling a patient into the study was 3:30 (SD 0.5) minutes after informed consent was completed, and medical staff across clinic sites (n=25) reported 9 of 10 level of satisfaction with the impact of the screening on clinic flow. CONCLUSIONS: Preliminary data show promise regarding the feasibility of using KardiaMobile ECGs for the screening of AFib and prevention of cardiological disease in vulnerable outpatient populations. The use of a single-lead mobile ECG strip can serve as a low-cost, effective AFib screen for implementation across free clinics attempting to provide increased health care accessibility. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52647.

An open-access simultaneous electrocardiogram and phonocardiogram database.

Objective.The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels.Approach.We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics.Main results.The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis.Significance.The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.

Tracking Tidal Volume From Holter and Wearable Armband Electrocardiogram Monitoring.

A novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.

Supermarket/hypermarket opportunistic screening for atrial fibrillation (SHOPS-AF) using sensors embedded in the handles of supermarket trolleys: A feasibility study.

BACKGROUND: Atrial fibrillation (AF) increases the risk of death, stroke, heart failure, cognitive decline, and healthcare costs but is often asymptomatic and undiagnosed. There is currently no national screening program for AF. The advent of validated hand-held devices allows AF to be detected in non-healthcare settings, enabling screening to be undertaken within the community. METHOD AND RESULTS: In this novel observational study, we embedded a MyDiagnostick single lead ECG sensor into the handles of shopping trolleys in four supermarkets in the Northwest of England: 2155 participants were recruited. Of these, 231 participants either activated the sensor or had an irregular pulse, suggesting AF. Some participants agreed to use the sensor but refused to provide their contact details, or consent to pulse assessment. In addition, some data were missing, resulting in 203 participants being included in the final analyses. Fifty-nine participants (mean age 73.6 years, 43% female) were confirmed or suspected of having AF; 20 were known to have AF and 39 were previously undiagnosed. There was no evidence of AF in 115 participants and the remaining 46 recordings were non-diagnostic, mainly due to artefact. Men and older participants were significantly more likely to have newly diagnosed AF. Due to the number of non-diagnostic ECGs (n = 46), we completed three levels of analyses, excluding all non-diagnostic ECGs, assuming all non-diagnostic ECGs were masking AF, and assuming all non-diagnostic ECGs were not AF. Based on the results of the three analyses, the sensor's sensitivity (95% CI) ranged from 0.70 to 0.93; specificity from 0.15 to 0.97; positive predictive values (PPV) and negative predictive values (NPV) ranged from 0.24 to 0.56 and 0.55 to 1.00, respectively. These values should be interpreted with caution, as the ideal reference standard on 1934 participants was imperfect. CONCLUSION: The study demonstrates that the public will engage with AF screening undertaken as part of their daily routines using hand-held devices. Sensors can play a key role in identifying asymptomatic patients in this way, but the technology must be further developed to reduce the quantity of non-diagnostic ECGs.

Simplified electrophysiological approach combining a point-of-care nerve conduction device and an electrocardiogram produces an accurate diagnosis of diabetic polyneuropathy.

AIMS/INTRODUCTION: This study aimed to investigate the diagnostic potential of two simplified tests, a point-of-care nerve conduction device (DPNCheck™) and a coefficient of variation of R-R intervals (CVR-R), as an alternative to traditional nerve conduction studies for the diagnosis of diabetic polyneuropathy (DPN) in patients with diabetes. MATERIALS AND METHODS: Inpatients with type 1 or type 2 diabetes (n = 167) were enrolled. The study population consisted of 101 men, with a mean age of 60.8 ± 14.8 years. DPN severity was assessed using traditional nerve conduction studies, and differentiated based on Baba's classification (BC). To examine the explanatory potential of variables in DPNCheck™ and CVR-R regarding the severity of DPN according to BC, a multiple regression analysis was carried out, followed by a receiver operating characteristic analysis. RESULTS: Based on BC, 61 participants (36.5% of the total) were categorized as having DPN severity of stage 2 or more. The multiple regression analysis yielded a predictive formula with high predictive power for DPN diagnosis (estimated severity of DPN in BC = 2.258 - 0.026 × nerve conduction velocity [m/s] - 0.594 × ln[sensory nerve action potential amplitude (µV)] + 0.528In[age(years)] - 0.178 × ln[CVR-R], r = 0.657). The area under the curve in receiver operating characteristic analysis was 0.880. Using the optimal cutoff value for DPN with severer than stage 2, the predictive formula showed good diagnostic efficacy: sensitivity of 83.6%, specificity of 79.2%, positive predictive value of 51.7% and negative predictive value of 76.1%. CONCLUSIONS: These findings suggest that DPN diagnosis using DPNCheck™ and CVR-R could improve diagnostic efficiency and accessibility for DPN assessment in patients with diabetes.

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring applications. The detector is featured with a 1.5-bit non-feedback delta quantizer (DQ) based feature extractor, followed by a multiplier-less convolutional neural network (CNN) engine, which eliminates the traditional high-resolution analog-to-digital converter (ADC) in conventional signal processing systems. The DQ uses a computing-in-capacitor (CIC) subtractor to quantize the sample-to-sample difference of ECG signal into 1.5-bit ternary codes, which is insensitive to low-frequency baseline wandering. The subsequent event-driven classifier is composed of a low-complexity coarse detector and a systolic-array-based CNN engine for ECG anomaly detection. The DQ and the digital CNN are fabricated in 65-nm and 180-nm CMOS technology, respectively, and the two chips are integrated on board through wire bonding. The measured detection accuracy is 90.6% ∼ 91.3% when tested on the MIT-BIH arrhythmia database, identifying three different ECG anomalies. Operating at 1 V and 1.4 V power supplies for the DQ and the digital CNN, respectively, the measured long-term average power consumption of the core circuits is 36 nW, which makes the detector among those state-of-the-art always-on cardiac anomaly detection devices with the lowest power consumption.

Doxorubicin cardiotoxicity and Camellia sinensis cardioprotection determined by speckle-tracking echocardiography

Abstract Doxorubicin (Dox) is a medication used in the treatment of cancerous tumors and hematologic malignancies with potentially serious side effects, including the risk of cardiotoxicity. Flavonoids are plant metabolites with antioxidant properties and can be extracted from Camellia sinensis (CS). The aim of this study is to evaluate the possible cardioprotective effect of CS against injuries induced by Dox in rats. A total of 32 animals were distributed into four groups: (1) control - intraperitoneal injection (I.P.) of 0.5 mL saline weekly and 1.0 mL water by gavage daily; (2) CS - 0.5 mL saline I.P. weekly and 200 mg/kg CS by gavage daily; (3) Dox - 5.0 mg/kg Dox I.P. weekly and 1.0 mL water by gavage daily; and (4) Dox+CS -5.0 mg/kg Dox I.P. weekly and 200 mg/kg CS by gavage daily. Clinical examinations, blood profiles, electrocardiograms, echocardiograms, and histological analyses of hearts were performed over 25 days. The animals in the Dox group showed changes in body weight and in erythrogram, leukogram, electrocardiography, and echocardiography readings. However, animals from the dox+CS group had significantly less change in body weight, improved cardiac function, and showed more preserved cardiac tissue. This study demonstrated that CS prevents dox-induced cardiotoxicity, despite enhancing the cytotoxic effect on blood cells

Cardioneuroablación como tratamiento del síncope reflejo: reporte de un caso

Resumen: El síncope mediado neuralmente es un trastorno causado por un reflejo autónomo anormalmente amplificado que involucra componentes tanto simpáticos como parasimpáticos. Es la causa más frecuente de síncope en personas jóvenes y su tratamiento sigue siendo un desafío, ya que no se ha demostrado que alguna terapia farmacológica prevenga por completo su recurrencia. En los últimos años ha surgido una técnica denominada cardioneuroablación, que consiste en la ablación por radiofrecuencia de los plexos ganglionares (PG) parasimpáticos, con buenos resultados a corto y largo plazo en la prevención de síncope recurrente, según los diferentes grupos de investigación. Presentamos el primer caso en Chile de un hombre joven con síncopes mediados neuralmente recurrentes que fue tratado con esta técnica en el Hospital Regional de Concepción.

Abstract: Cardioneuroablation is a novel method that can be used to treat reflex syncope. Although the experience with this technique is relatively limited it provides a more physiological way to treat this condition. The first case in Chile is herein reported along with a discussion of the subject.

A Clinician's Guide to Smartwatch "Interrogation".

PURPOSE OF REVIEW: Wearable technology is rapidly evolving and the data that it can provide regarding an individual's health is becoming increasingly important for clinicians to consider. The purpose of this review is to help inform health care providers of the benefits of smartwatch interrogation, with a focus on reviewing the various parameters and how to apply the data in a meaningful way. RECENT FINDINGS: This review details interpretation of various parameters found commonly in newer smartwatches such as heart rate, step count, ECG, heart rate recovery (HRR), and heart rate variability (HRV), while also discussing potential pitfalls that a clinician should be aware of. Smartwatch interrogation is becoming increasingly relevant as the continuous data it provides helps health care providers make more informed decisions regarding diagnosis and treatment. For this reason, we recommend health care providers familiarize themselves with the technology and integrate it into clinical practice.

Wearable technology for electrocardiogram and vectocardiogram using the Dower Transformation / Tecnologia vestível para exames de eletrocardiograma e vetocardiograma utilizando a transformada de Dower / Tecnología vestible para exámenes de electrocardiograma y vetocardiograma utilizando de la transformada de Dower

Objectives: Thousands of people suffer from cardiovascular diseases. Even though the electrocardiogram is an exam consolidated. The lack of methodological observation in the placement of sensors can compromise the results. This article proposes a wearable vest capable of conditioning cardiac signals from three simultaneous channels, reducing the chance of failures in the exam due to the smaller number of electrodes attached to the patient's body. Methods: It adds the vectorcardiogram technique to the electrocardiogram wearable, which consists of three orthonormal derivations Vx, Vy, and Vz, measuring dynamic components of the heart vector. Results: The display of the cardiac biopotential in the web-mobile application represents the visualization of the twelve derivations synthesized from the Dower transform and the spatial projections of the cardiac loop under a three-dimensional view. Conclusion: Feasibility of integrating the vectorcardiogram with the electrocardiogram exam.

Objetivos: Milhares de pessoas sofrem com doenças cardiovasculares, apesar do Eletrocardiograma ser um exame consolidado, a falta de observação metodológica na colocação dos sensores pode comprometer os resultados. O presente artigo propõe um colete vestível capaz de condicionar sinais cardíacos de três canais simultâneos, reduzindo a chance de falhas na execução do exame em função da menor quantidade de eletrodos fixados ao corpo do paciente. Métodos: Acrescenta a técnica do vetocardiograma ao vestível de eletrocardiograma, que consiste em três derivações ortonormais Vx, Vy e Vz, medindo componentes dinâmicos do vetor coração. Resultados: Exibição do biopotencial cardíaco na aplicação web-mobile representa de forma satisfatória a visualização das doze derivações sintetizadas a partir da transformada de Dower, bem como, as projeções espaciais do loop cardíaco sob uma visão tridimensional. Conclusão: Viabilidade de integração do vetocardiograma ao exame de eletrocardiograma.

Objetivos: Miles de personas padecen enfermedades cardiovasculares, a pesar de que el electrocardiograma es un examen consolidado, la falta de observación metodológica en la colocación de sensores puede comprometer los resultados. Este artículo propone una tecnología vestible capaz de acondicionar las señales cardíacas de tres canales simultáneos, reduciendo la posibilidad de fallas en el examen por la menor cantidad de electrodos adheridos al cuerpo del paciente. Métodos: Agrega la técnica del vetocardiograma al electrocardiograma vestible, que consta de tres derivaciones ortonormales Vx, Vy y Vz, midiendo los componentes dinámicos del vector cardíaco. Resultados: La visualización del biopotencial cardíaco en la aplicación web-móvil representa satisfactoriamente la visualización de las doce derivaciones sintetizadas a partir de la transformada de Dower, así como las proyecciones espaciales del bucle cardíaco bajo una vista tridimensional. Conclusión: Viabilidad de integrar el vetocardiograma con el examen electrocardiográfico.

Novel tech throws knock-out punch to ECG improving GP referral decisions to cardiology.

PURPOSE: In a comparator study, designed with assistance from the Food and Drug Administration, a State-of-the-Art (SOTA) ECG device augmented with automated analysis, the comparator, was compared with a breakthrough technology, Cardio-HART (CHART). METHODS: The referral decision defined by physician reading biosignal-based ECG or CHART report were compared for 550 patients, where its performance is calculated against the ground truth referral decision. The ground truth was established by cardiologist consensus based on all the available measurements and findings including echocardiography (ECHO). RESULTS: The results confirmed that CHART analysis was far more effective than ECG only analysis: CHART reduced false negative rates 15.8% and false positive (FP) rates by 5%, when compared with SOTA ECG devices. General physicians (GP's) using CHART saw their positive diagnosis rate significantly increased, from ~10% to ~26% (260% increase), and the uncertainty rate significantly decreased, from ~31% to ~1.9% (94% decrease). For cardiology, the study showed that in 98% of the cases, the CHART report was found to be a good indicator as to what kind of heart problems can be expected (the 'start-point') in the ECHO examination. CONCLUSIONS: The study revealed that GP use of CHART resulted in more accurate referrals for cardiology, resulting in fewer true negative or FP-healthy or mildly abnormal patients not in need of ECHO confirmation. The indirect benefit is the reduction in wait-times and in unnecessary and costly testing in secondary care. Moreover, when used as a start-point, CHART can shorten the echocardiograph examination time.

Fructus Xanthii-Inspired Low Dynamic Noise Dry Bioelectrodes for Surface Monitoring of ECG.

The microstructured surfaces of bioelectrical dry electrodes are important aspects of dry electrode design. However, traditional surfaces for microstructured bioelectrical dry electrodes are costly to produce and require complex fabrication methods. In this study, a novel stacked-template method is proposed for the first time, rapidly producing microstructured dry electrodes at a low cost and with a large surface area. Three types of microstructured Ag/AgCl thermoplastic polyurethane (TPU) electrodes with a Fructus xanthii-inspired barb structure (FXbs) are prepared using this method; then, the dynamic friction, hair interference resistance, electrochemical, and electrocardiogram (ECG) signal acquisition performance of the electrodes are tested, and the dynamic noise characteristics of the electrodes are comprehensively evaluated with simulated instruments. Compared to the plate structure, the dynamic friction coefficient of the FXbs electrode improved by about 38.8%, exhibiting strong hair interference resistance. In addition, the FXbs electrode exhibits low dynamic noise and comparable performance to the wet electrode, in terms of signal acquisition, when it is tested using simulated instruments. Therefore, the prepared FXbs electrode increases the friction coefficient between the electrode and the skin, which effectively resolves issues related to dynamic noise in bioelectrical signals, making it suitable for dynamic measurements.

Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort.

BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested. OBJECTIVE: To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%. METHODOLOGY/PRINCIPAL FINDINGS: This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms. Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3-128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874. CONCLUSION: The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.

Diagnostic performance of a wearing dynamic ECG recorder for atrial fibrillation screening: the HUAMI heart study.

BACKGROUND: Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia with high morbidity and mortality rate. Evidence shows that in every three patients with AF, one is asymptomatic. The asymptomatic and paroxysmal nature of AF is the reason for unsatisfactory and delayed detection using traditional instruments. Research indicates that wearing a dynamic electrocardiogram (ECG) recorder can guide accurate and safe analysis, interpretation, and distinction of AF from normal sinus rhythm. This is also achievable in an upright position and after exercises, assisted by an artificial intelligence (AI) algorithm. METHODS: This study enrolled 114 participants from the outpatient registry of our institution from June 24, 2020 to July 24, 2020. Participants were tested with a wearable dynamic ECG recorder and 12-lead ECG in a supine, an upright position and after exercises for 60 s. RESULTS: Of the 114 subjects enrolled in the study, 61 had normal sinus rhythm and 53 had AF. The number of cases that could not be determined by the wristband of dynamic ECG recorder was two, one and one respectively. Case results that were not clinically objective were defined as false-negative or false-positive. Results for diagnostic accuracy, sensitivity, and specificity tested by wearable dynamic ECG recorders in a supine position were 94.74% (95% CI% 88.76-97.80%), 88.68% (95% CI 77.06-95.07%), and 100% (95% CI 92.91-100%), respectively. Meanwhile, the diagnostic accuracy, sensitivity and specificity in an upright position were 97.37% (95% CI 92.21-99.44%), 94.34% (95% CI 84.03-98.65%), and 100% (95% CI 92.91-100%), respectively. Similar results as those of the upright position were obtained after exercise. CONCLUSION: The widely accessible wearable dynamic ECG recorder integrated with an AI algorithm can efficiently detect AF in different postures and after exercises. As such, this tool holds great promise as a useful and user-friendly screening method for timely AF diagnosis in at-risk individuals.