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.

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.

An Inkjet-Printed PEDOT:PSS-Based Stretchable Conductor for Wearable Health Monitoring Device Applications.

A stretchable conductor is one of the key components in soft electronics that allows the seamless integration of electronic devices and sensors on elastic substrates. Its unique advantages of mechanical flexibility and stretchability have enabled a variety of wearable bioelectronic devices that can conformably adapt to curved skin surfaces for long-term health monitoring applications. Here, we report a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)-based stretchable polymer blend that can be patterned using an inkjet printing process while exhibiting low sheet resistance and accommodating large mechanical deformations. We have systematically studied the effect of various types of polar solvent additives that can help induce phase separation of PEDOT and PSS grains and change the conformation of a PEDOT chain, thereby improving the electrical property of the film by facilitating charge hopping along the percolating PEDOT network. The optimal ink formulation is achieved by adding 5 wt % ethylene glycol into a pristine PEDOT:PSS aqueous solution, which results in a sheet resistance of as low as 58 Ω/□. Elasticity can also be achieved by blending the above solution with the soft polymer poly(ethylene oxide) (PEO). Thin films of PEDOT:PSS/PEO polymer blends patterned by inkjet printing exhibits a low sheet resistance of 84 Ω/□ and can resist up to 50% tensile strain with minimal changes in electrical performance. With its good conductivity and elasticity, we have further demonstrated the use of the polymer blend as stretchable interconnects and stretchable dry electrodes on a thin polydimethylsiloxane (PDMS) substrate for photoplethysmography (PPG) and electrocardiography (ECG) recording applications. This work shows the potential of using a printed stretchable conducting polymer in low-cost wearable sensor patches for smart health applications.

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. METHODS: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. RESULTS: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76±23.14 ms). Similarly, within the prospective, genetic heart disease-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. CONCLUSIONS: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

NeoBeat offers rapid newborn heart rate assessment.

BACKGROUND: Heart rate (HR) is used to guide interventions during delivery room (DR) neonatal resuscitation. Dry electrode ECG (NeoBeat) may detect HR more rapidly than pulse oximetry (PO) and portable ECG, but real-time comparisons of these devices are lacking. DESIGN/METHODS: PO, ECG and NeoBeat were placed sequentially on newborns in the DR. Time for device placement and time to accurate HR acquisition were noted. RESULTS: DR resuscitations of 28 preterm/term infants were observed. The NeoBeat was placed faster (ie, 3 s) than PO (20 s, p=<0.0001) and ECG (16 s, p=<0.0001). Total time from initiation of device placement to HR acquisition was fastest with NeoBeat (13 s) versus ECG (42 s, p<0.0001) and PO (105 s, p<0.0001) (duration values=median). CONCLUSIONS: These observations in a small cohort of relatively well neonates demonstrate that the NeoBeat is significantly faster to place and consistently acquires HR faster than PO and ECG.

Monitoring great ape heart health through innovative electrocardiogram technology: Training methodologies and welfare implications.

Assessing and treating cardiovascular disease (or heart disease) is a growing concern for institutions housing great apes, as it is a major cause of mortality in all four taxa managed in human care. As part of a proactive monitoring plan, zoological managers and veterinarians often elect to perform electrocardiograms (ECGs) on their great ape populations. ECGs noninvasively evaluate cardiac electrical activity, and are thereby capable of providing information regarding heart function. This electrical signature is transcribed as a visual display of waveforms, referred to as telemetry strips, and can detect irregularities in heart rhythm, also known as arrhythmia. While traditional 6- or 12-lead ECGs are recommended periodically as part of a thorough heart performance evaluation, here we discuss the KardiaMobile (KM) device as an additional primate welfare tool. KM is a small, Food and Drug Administration-cleared, clinical-grade mobile ECG monitor that requires only 30 s of pressure to flag heart rate or arrhythmic abnormalities. We detail the training process and applicability to great apes in human care.

Monitoring driver health status in real time.

Nowadays, surveillance systems have evolved significantly; hence, in order to meet the specific needs of the health sector and to monitor the patients' health conditions, intelligent systems have been proposed. These innovations represent a primordial role in road safety, which reduce the risk of traffic accidents. This paper describes an intelligent system design for remote monitoring (tele-monitoring) of a driver's health condition in real time. The measurement using new hardware and software devices is made possible through the contact between the driver contact and an intelligent steering wheel, which is coupled either to an integrated monitor or to a bluetooth link with a local Android smartphone. The driver's heart rate is calculated through the continuous collection of the electrocardiographic signal as well as the blood oxygen saturation SpO2 by using the photoplethysmographic technique. Consequently, it is necessary to monitor the two vital functions of the driver, cardiac and respiratory activity. This information is transmitted to a remote tele-vigilance center in the case of abnormalities in these functions under the transmission control protocol/internet protocol involving a 4G/3G connection. The application is associated with the system that triggers high and low alarms locally and remotely in the events of tachycardia, bradycardia, or cardiac arrhythmia. Furthermore, another alarm is also triggered in the event of respiratory decompensation.

Mobile Electrocardiogram Monitoring and Health-Related Quality of Life in Patients With Atrial Fibrillation: Findings From the iPhone Helping Evaluate Atrial Fibrillation Rhythm Through Technology (iHEART) Study.

BACKGROUND: Atrial fibrillation (AF) is associated with high recurrence rates and poor health-related quality of life (HRQOL) but few effective interventions to improve HRQOL exist. OBJECTIVE: The aim of this study was to examine the impact of the "iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology" (iHEART) intervention on HRQOL in patients with AF. METHODS: We randomized English- and Spanish-speaking adult patients with AF to receive either the iHEART intervention or usual care for 6 months. The iHEART intervention used smartphone-based electrocardiogram monitoring and motivational text messages. Three instruments were used to measure HRQOL: the Atrial Fibrillation Effect on Quality of Life (AFEQT), the 36-item Short-Form Health survey, and the EuroQol-5D. We used linear mixed models to compare the effect of the iHEART intervention on HRQOL, quality-adjusted life-years, and AF symptom severity. RESULTS: A total of 238 participants were randomized to the iHEART intervention (n = 115) or usual care (n = 123). Of the participants, 77% were men and 76% were white. More than half (55%) had an AF recurrence. Both arms had improved scores from baseline to follow-up for AFEQT and AF symptom severity scores. The global AFEQT score improved 18.5 and 11.2 points in the intervention and control arms, respectively (P < .05). There were no statistically significant differences in HRQOL, quality-adjusted life-years, or AF symptom severity between groups. CONCLUSIONS: We found clinically meaningful improvements in AF-specific HRQOL and AF symptom severity for both groups. Additional research with longer follow-up should examine the influence of smartphone-based interventions for AF management on HRQOL and address the unique needs of patients diagnosed with different subtypes of AF.

Optimal ECG Lead System for Exercise Assessment of Ischemic Heart Disease.

The diagnostic value of an ECG exercise test in diagnosis of ischemic heart disease (IHD) is limited. We investigated whether it is possible to develop a method for diagnosis of IHD which uses a low number of optimal ECG leads and has a higher diagnostic efficiency than conventional exercise ECG. This study was carried out on 43 patients. The 67-lead high-resolution ECG was recorded at rest and during exercise. The diagnostic value of ST segment depression (ΔST60) and T-wave morphology change (δT) determined in optimized ECG lead configurations was higher than for the standard 12-lead ECG. The best results were obtained for δT determined from 6 ECG electrode locations where sensitivity and specificity were 70% and 69% whereas for the standard exercise ECG were 63% and 62%, respectively. The small number of ECG leads used allows for easy hardware implementation of the methods for use in clinical settings.

Lead-I ECG for detecting atrial fibrillation in patients attending primary care with an irregular pulse using single-time point testing: A systematic review and economic evaluation.

BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is associated with increased risk of stroke and congestive heart failure. Lead-I electrocardiogram (ECG) devices are handheld instruments that can detect AF at a single-time point. PURPOSE: To assess the diagnostic test accuracy, clinical impact and cost effectiveness of single-time point lead-I ECG devices compared with manual pulse palpation (MPP) followed by a 12-lead ECG for the detection of AF in symptomatic primary care patients with an irregular pulse. METHODS: Electronic databases (MEDLINE, MEDLINE Epub Ahead of Print and MEDLINE In-Process, EMBASE, PubMed and Cochrane Databases of Systematic Reviews, Cochrane Central Database of Controlled Trials, Database of Abstracts of Reviews of Effects, Health Technology Assessment Database) were searched to March 2018. Two reviewers screened the search results, extracted data and assessed study quality. Summary estimates of diagnostic accuracy were calculated using bivariate models. Cost-effectiveness was evaluated using an economic model consisting of a decision tree and two cohort Markov models. RESULTS: Diagnostic accuracy The diagnostic accuracy (13 publications reporting on nine studies) and clinical impact (24 publications reporting on 19 studies) results are derived from an asymptomatic population (used as a proxy for people with signs or symptoms of AF). The summary sensitivity of lead-I ECG devices was 93.9% (95% confidence interval [CI]: 86.2% to 97.4%) and summary specificity was 96.5% (95% CI: 90.4% to 98.8%). Cost effectiveness The de novo economic model yielded incremental cost effectiveness ratios (ICERs) per quality adjusted life year (QALY) gained. The results of the pairwise analysis show that all lead-I ECG devices generate ICERs per QALY gained below the £20,000-£30,000 threshold. Kardia Mobile is the most cost effective option in a full incremental analysis. Lead-I ECG tests may identify more AF cases than the standard diagnostic pathway. This comes at a higher cost but with greater patient benefit in terms of mortality and quality of life. LIMITATIONS: No published data evaluating the diagnostic accuracy, clinical impact or cost effectiveness of lead-I ECG devices for the target population are available. CONCLUSIONS: The use of single-time point lead-I ECG devices in primary care for the detection of AF in people with signs or symptoms of AF and an irregular pulse appears to be a cost effective use of NHS resources compared with MPP followed by a 12-lead ECG, given the assumptions used in the base case model. REGISTRATION: The protocol for this review is registered on PROSPERO as CRD42018090375.

NFC-Powered Flexible Chest Patch for Fast Assessment of Cardiac, Hemodynamic, and Endocrine Parameters.

Cardiac-related diseases are still the number one cause of death worldwide. Methods and technologies to suppress this problem are currently being investigated by integrating electrocardiographic (ECG) monitoring with other sensing modalities in order to detect these diseases more accurately and in due course of time. In this paper, we propose a battery-less and flexible device to be worn as a chest patch for monitoring cardiac and hemodynamic parameters through electrical and acoustic measurements, combined with sweat pH level estimation and skin temperature, by swiping a smartphone over the patch area for enough time (≃5 seconds) to allow adequate acquisition and estimation of the aforementioned parameters. Fast screening of vital signals from patients in ambulatory or emergency scenarios can thus be achieved by this Near Field Communication (NFC) powered device, as well as home or office monitoring for those individuals suffering from diseases affecting the hemodynamic, cardiac and endocrine parameters detected by the proposed technology. Current consumption of the device is 1 mA for harvested levels of 1.8 V, yielding a power requirement of 1.8 mW. Within these conditions, the sensitivities achieved by each sensing modality are 42 mV/unit for pH, 0.12 °C for temperature, 48 dB SNR for ECG and -56 dBA for acoustic measurements.

Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).

A 769 µW Battery-Powered Single-Chip SoC With BLE for Multi-Modal Vital Sign Monitoring Health Patches.

An all-in-one battery powered low-power SoC for measuring multiple vital signs with wearables is proposed. All functionality needed in a typical wearable use case scenario, including dedicated readouts, power management circuitry, digital signal processing and wireless communication (BLE) is integrated in a single die. This high level of integration allows an unprecedented level of miniaturization leading to smaller component count which reduces cost and improves comfort and signal integrity. The SoC includes an ECG, Bio-Impedance and a fully differential PPG readout and can interface with external sensors (like an IMU). In a typical application scenario where all sensor readouts are enabled and key features (like heart rate) are calculated on the chip and streamed over the radio, the SoC consumes only 769 µW from the regulated 1.2 V supply.

A hierarchical, scalable architecture for a real-time monitoring system for an electrocardiography, using context-aware computing.

Heart failure is one of the most common cause of death in the world. The real-time health monitoring system with the advent of the Internet of things has attracted growing attention in the health care industry, which can help reducing the death rate of heart failure. Despite the recent success of these efforts, there have been some limitations, such as response time, scalability, latency and fault tolerance. To address these issues, in this paper, we propose a hierarchical architecture with four layers to develop health care systems. In the proposed model, the vital signs of a patient are measured by means of a body sensor network and sent to a smart health care system. Each of these layers is related to a certain level of heart failure. Therefore, in the proposed model, simple and low-risk heart failure can be detected quickly before it reaches to a dangerous level. Empirical results confirm a significant improvement in terms of response time and scalability in comparison with the state of the art techniques.

Reconstruction of the 12-lead ECG using a novel MR-compatible ECG sensor network.

PURPOSE: Current electrocardiography (ECG) devices in MRI use non-conventional electrode placement, have a narrow bandwidth, and suffer from signal distortions including magnetohydrodynamic (MHD) effects and gradient-induced artifacts. In this work a system is proposed to obtain a high-quality 12-lead ECG. METHODS: A network of N electrically independent MR-compatible ECG sensors was developed (N = 4 in this study). Each sensor uses a safe technology - short cables, preamplification/digitization close to the patient, and optical transmission - and provides three bipolar voltage leads. A matrix combination is applied to reconstruct a 12-lead ECG from the raw network signals. A subject-specific calibration is performed to identify the matrix coefficients, maximizing the similarity with a true 12-lead ECG, acquired with a conventional 12-lead device outside the scan room. The sensor network was subjected to radiofrequency heating phantom tests at 3T. It was then tested in four subjects, both at 1.5T and 3T. RESULTS: Radiofrequency heating at 3T was within the MR-compatibility standards. The reconstructed 12-lead ECG showed minimal MHD artifacts and its morphology compared well with that of the true 12-lead ECG, as measured by correlation coefficients above 93% (respectively, 84%) for the QRS complex shape during steady-state free precession (SSFP) imaging at 1.5T (respectively, 3T). CONCLUSION: High-quality 12-lead ECG can be reconstructed by the proposed sensor network at 1.5T and 3T with reduced MHD artifacts compared to previous systems. The system might help improve patient monitoring and triggering and might also be of interest for interventional MRI and advanced cardiac MR applications.

A Novel Wearable EEG and ECG Recording System for Stress Assessment.

Suffering from continuous stress can lead to serious psychological and even physical disorders. Objective stress assessment methods using noninvasive physiological responses such as heart rate variability (HRV) and electroencephalograms (EEG) have therefore been proposed for effective stress management. In this study, a novel wearable device that can measure electrocardiograms (ECG) and EEG simultaneously was designed to enable continuous stress monitoring in daily life. The developed system is easily worn by hanging from both ears, is lightweight (i.e., 42.5 g), and exhibits an excellent noise performance of 0.12 µVrms. Significant time and frequency features of HRV and EEG were found in two different stressors, namely the Stroop color word and mental arithmetic tests, using 14 young subjects. Stressor situations were classified using various HRV and EEG feature selections and a support vector machine technique. The five-fold cross-validation results obtained when using both EEG and HRV features showed the best performance with an accuracy of 87.5%, which demonstrated the requirement for simultaneous HRV and EEG measurements.

Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep.

Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse® photoplethysmography with reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute [bpm], P = 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland-Altman agreement range (2.67 bpm). The regression analysis of Bland-Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR < 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR > 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant "U" shape trend (P < 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60-80 bpm) and slightly larger error for lower (<60 bpm) and higher (>80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults.

Cost-effectiveness and screening performance of ECG handheld machine in a population screening programme: The Belgian Heart Rhythm Week screening programme.

AIMS: Overall, 40% of patients with atrial fibrillation are asymptomatic. The usefulness and cost-effectiveness of atrial fibrillation screening programmes are debated. We evaluated whether an atrial fibrillation screening programme with a handheld electrocardiogram (ECG) machine in a population-wide cohort has a high screening yield and is cost-effective. METHODS: We used a Markov-model based modelling analysis on 1000 hypothetical individuals who matched the Belgian Heart Rhythm Week screening programme. Subgroup analyses of subjects ≥65 and ≥75 years old were performed. Screening was performed with one-lead ECG handheld machine Omron® HeartScan HCG-801. RESULTS: In both overall population and subgroups, the use of the screening procedure diagnosed a consistently higher number of diagnosed atrial fibrillation than not screening. In the base-case scenario, the screening procedure resulted in 106.6 more atrial fibrillation patient-years, resulting in three fewer strokes, 10 more life years and five more quality-adjusted life years (QALYs). The number needed-to-screen (NNS) to avoid one stroke was 361. In subjects ≥65 years old, we found 80.8 more atrial fibrillation patient-years, resulting in three fewer strokes, four more life-years and five more QALYs. The NNS to avoid one stroke was 354. Similar results were obtained in subjects ≥75 years old, with a NNS to avoid one stroke of 371. In the overall population, the incremental cost-effectiveness ratio for any gained QALY showed that the screening procedure was cost-effective in all groups. CONCLUSIONS: In a population-wide screening cohort, the use of a handheld ECG machine to identify subjects with newly diagnosed atrial fibrillation was cost-effective in the general population, as well as in subjects ≥65 and subjects ≥75 years old.

Multi-dry-electrode plate sensor for non-invasive electrocardiogram and heart rate monitoring for the assessment of drug responses in freely behaving mice.

Monitoring of electrocardiogram (ECG) and heart rate (HR) is essential in a wide range of experiments. For conscious animal studies, telemetry is the preferred approach; however, it requires 1-3 weeks of recovery after surgical device-implantation. The present paper describes a novel multi-dry-electrode plate (MDEP) sensor system to monitor ECG/HR in freely behaving mice without the need for surgery for device/electrode implantation. The MDEP sensor is a rectangular plate with 15 gold-plated stripe pattern electrodes, on which a mouse can walk around freely, and detects ECG whenever ≥2 paws (footpads) come in contact with the electrodes. Here we show that the MDEP sensor detected distinct QRS complexes which, were fragmented due to locomotion and insufficient perspiration on the footpads. Nonetheless, the HR calculated from the QRS complexes were similar to the HR calculated from R-R intervals simultaneously recorded from lead-II ECG (difference = 0.0 ±â€¯0.16 ms) as part of the validation exercise. Also, the archetypal responses to isoproterenol and metoprolol injections were successfully detected as a significantly elevation (+151 ±â€¯15 bpm) and reduction (-77 ±â€¯6 bpm) in HR, respectively, compared to vehicle at 20-60 min postdose. Conversely, the P wave was rarely identifiable unless signal averaging was undertaken. These results indicate a potential utility for the MDEP-sensor system for cardiac pharmacological studies. In addition, signal averaging appeared to be effective for detection of ECG intervals such as PR and QT, although the QT cannot be measured in the mouse heart as there is no T wave.

Development and assessment of a novel precordial lead system for accurate detection of right atrial and ventricular depolarization in dogs with various thoracic conformations.

OBJECTIVE: To assess recording accuracy of right atrial and ventricular depolarization during 12-lead ECG when precordial lead V1 was positioned at each of 5 locations on the thorax of dogs with various thoracic conformations. ANIMALS: 60 healthy client-owned dogs. PROCEDURES: 20 dogs were allocated to each of 3 groups (brachymorphic, mesomorphic, or dolichomorphic) on the basis of thoracic conformation. Each dog remained unsedated and was positioned in right lateral recumbency for a series of five 12-lead surface ECGs, with V1 located adjacent to the sternum in the fifth intercostal space (ICS; control), at the costochondral junction (CCJ) of the right first ICS (1st-R), at the CCJ of the right third ICS, at the right third ICS where the thorax was the widest, and at the CCJ of the left first ICS. Electrocardiographic variables were compared among the 5 ECG tracings. RESULTS: When V1 was at the control location, the P wave was positive for all dogs; however, consistent recording of right atrial and ventricular depolarization (ie, R wave-to-S wave ratio [R/S] < 1) occurred more frequently for brachymorphic dogs (16/20) than for dolichomorphic (7/20) and mesomorphic (6/20) dogs. When V1 was at the 1st-R location, the P wave was negative for most dogs, and R/S was < 1 for the majority of dogs in the brachymorphic (19/20), mesomorphic (17/20), and dolichomorphic (16/20) groups. The median R/S for V1 at the 1st-R location was significantly lower than that for the other 4 V1 locations. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that placement of V1 at the 1st-R location provided correct evaluation of right atrial and ventricular depolarization in most dogs regardless of thoracic conformation.