Electrocardiogram Device Research Articles

An Open-Access Long-Term Wearable ECG Database for Premature Ventricular Contractions and Supraventricular Premature Beat Detection

Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, introduced by the dry electrode in wearable ECG monitoring system, leads to the inefficient recognition of the existing PB detection technologies. Although many methods can achieve high recognition rate on current widely-used open-access clinical ECG databases, they still fail to work properly on dynamic ECG signals. This study presents an open-access ECG database comprises of 24-hour wearable ECG recordings. The database is used for the 3rd China Physiological Signal Challenge (CPSC 2020), where participants are expected to recognize PVC and SPB from these recordings. All the approved algorithms are evaluated by scoring standards and regulations defined in terms of PVC detection and SPB detection, respectively.

Non-invasive mid-term ECG monitoring for the detection of atrial fibrillation in an outpatient population

Abstract Background Atrial fibrillation (AF) is a common arrhythmia with significant morbidity due to an increased risk of ischemic stroke. Outpatient electrocardiogram (ECG) monitoring is an integral part of the diagnosis of AF. Conventional 24 hour Holter monitoring can be cumbersome and often fails to diagnose patients with paroxysmal AF. Spyder ECG is a non-invasive ECG monitoring device that allows wireless transmission of ECG information for analysis. It is small and comfortable, allowing for easy application for the screening and detection of AF over a mid-term duration. Purpose This study aims to evaluate the incidence of AF in patients with no prior AF and CHADsVASC score of at least 1 with the use of the Spyder ECG mid-term ECG monitoring device. Methods Patients aged 21 to 85 years old with no prior history of AF and CHADsVASC score of at least 1 were recruited from outpatient clinics of 3 large tertiary hospitals in Singapore from December 2016 to April 2019. Patients wore the Spyder ECG device for up to 2 weeks, during which continuous ECG information was uploaded onto a central cloud database and analysed. Results There were 363 patients recruited. The mean age was 61±10.0 years and 65.1% were male. There were 80.3% Chinese, 11.6% Malay, 7.5% Indian and 20.6% of other races. 68.3% of the patients were non-smokers and 74.0% of them were non-alcohol drinkers. The mean BMI of 25.5±4.7 kg/m2. The patient population had significant co-morbidities. 76.3% of the patients had hypertension, 69.4% of them had hyperlipidemia and 40.5% of them had diabetes mellitus. 10.0% of them had congestive cardiac failure and 56.7% had ischaemic heart disease. 11.3% of patients had a previous stroke and 20.4% had a prior myocardial infarction. 7.8% of the patients had asthma, 5.8% of them had thyroid disease and 9.9% of them had chronic kidney disease. They were monitored for a mean of 5.4±2.9 days each. There were 15 (4.1%) patients in whom AF was detected. The patients with AF wore the device for a mean of 5.7±2.0 SD days. The mean burden of AF was 9.0% of monitored time. 46.7% of the patients with AF had detection of AF on the first day, 26.7% on the second day, 13.3% on the third day and 13.3% on the seventh day. The mean duration of the first episode of AF was 251±325 minutes. 7 out of 15 (46.7%) of patients had first episodes of AF lasting less than 10 minutes. Conclusion Continuous mid-term ECG monitoring was able to detect AF in 15 (4.1%) of a population of 363 patients with no prior AF and CHADsVASC score of at least one, monitored for a mean of 5.4 days. Most episodes (53.3%) of AF were detected after the first day of ECG monitoring. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Duke-NUS Medical School Singapore

Screen-detected atrial fibrillation predicts mortality in elderly subjects.

AimsCurrent guidelines recommend opportunistic screening for atrial fibrillation (AF) but the prognosis of individuals is unclear. The aim of this investigation is to determine prevalence and 1-year outcome of individuals with screen-detected AF.Methods and resultsWe performed a prospective, pharmacy-based single time point AF screening study in 7107 elderly citizens (≥65 years) using a hand-held, single-lead electrocardiogram (ECG) device. Prevalence of AF was assessed, and data on all-cause death and hospitalization for cardiovascular (CV) causes were collected over a median follow-up of 401 (372; 435) days. Mean age of participants was 74 ± 5.9 years, with 58% (N = 4130) of female sex. Automated heart rhythm analyses identified AF in 432 (6.1%) participants, with newly diagnosed AF in 3.6% of all subjects. During follow-up, 62 participants (0.9%) died and 390 (6.0%) were hospitalized for CV causes. Total mortality was 2.3% in participants with a screen-detected AF and 0.8% in subjects with a normal ECG [hazard ratio (HR) 2.94; 95% confidence interval (CI) 1.49–5.78; P = 0.002]; hospitalization for CV causes occurred in 10.6% and 5.5%, respectively (HR 2.08; 95% CI 1.52–2.84; P < 0.001). Compared with subjects without a history of AF at baseline and a normal ECG, participants with newly diagnosed or known AF had a significantly higher mortality risk with HRs of 2.64 (95% CI 1.05–6.66; P = 0.04) and 2.68 (95% CI 1.44–4.97; P = 0.002), respectively. After multivariable adjustment, screen-detected AF remained a significant predictor of death or hospitalization for CV causes.ConclusionPharmacy-based, automated AF screening in elderly citizens identified subjects with unknown AF and an excess mortality risk over the next year.

Validation of an equine fitness tracker: heart rate and heart rate variability

The adoption of fitness tracker devices to monitor training in the equine market is in full expansion. However, the validity of most of these devices has not been assessed. The aim of this study was to examine the validity of heart rate (HR) and heart rate variability (HRV) measurements during high-intensity exercise by an integrated equine fitness tracker with an electrocardiogram (ECG) (Equimetre) in comparison to an ECG device (Televet). Twenty Thoroughbred racehorses were equipped with the two devices and completed a training session at the track. Data from 18 horses was readable to be analysed. Equimetre HR was compared to Televet HR derived from the corrected Televet ECG. HRV parameters were computed in a dedicated software (Kubios) on uncorrected and manually corrected ECG from both devices, and compared to the Televet corrected data. The HR was recorded on the entire training session and HRV parameters were calculated during the exercise and recovery periods. A strong correlation between the Equimetre HR and Televet HR on corrected data was found (Pearson correlation: r=0.992, P&lt;0.001; root mean square error = 4.06 bpm). For HRV, the correlation was good for all parameters when comparing corrected Equimetre to corrected Televet data (Lin’s coefficient = 0.998). When comparing data obtained from uncorrected Equimetre data to the corrected Televet data, the correlation for HR was still good (Lin’s coefficient = 0.995) but the correlation for all HRV parameters was poor, except for the triangular index (Lin’s coefficient = 0.995). However, correlation between the uncorrected Televet HRV data and the corrected Televet data was equally poor (Lin’s coefficient &lt;0.9). In conclusion, the integrated equine fitness tracker Equimetre satisfies validity criteria for HR monitoring in horses during high intensity exercise. When using corrected ECG data, it provides accurate HRV parameters as well.

Care time Delays in Acute Coronary Syndromes with Persistent St Elevation (stem) and the Delaying Factors: Prospective STUDY About 50 Cases in the Cardiology Department of Aristide le Dante Hospital.

The care of acute coronary syndrome with persistent ST-elevation (STEMI) is a time-trial race: ‘‘time is myocardium”. The treatment relies on myocardial reperfusion by percutaneous coronary intervention (PCI) or fibrinolysis as promptly as possible. The main objective of this work was to assess the care time delays and the delaying factors during STEMI. We conducted a prospective, descriptive and analytic study over a 6 months’ time period. Were included all the patients received for STEMI. We have studied the care time delays and the delaying factors. We have compiled 50 patients. The mean age was 58.4 years and the sex-ratio M/F 2.5. The chest pain was typical in 39 patients. The mean time elapsed between the beginning of the pain and the first medical contact was 12 h 16 min. Transport (76%) and self-medication (70%) were the significant delaying factors found (p = 0.0001). The mean time elapsed between the first medical contact and the electrocardiogram was 9 h 57 min. The main factors delaying the diagnosis were the unavailability of the electrocardiogram device and the absence of electrocardiogram prescription (p = 0.001). The mean time elapsed between the electrocardiogram and the admission in the cardiology department was 3 h 02 min. The transport was the principal factor lengthening that time delay (p = 0.0001). Among the patients admitted directly in cardiology department, the mean time delay to perform the ECG was 30 min. The mean time delay of fibrinolysis was 2 h 11 min. Streptokinase shortage was the most frequent delaying factor (p = 0.001). The mean time delay between the qualifying ECG and the PCI completion was 2 h 42 min. The unavailability of the medical team was the first factor lengthening that time delay (p = 0.0001). The care time delays were lengthened enough in our context. This testifies to the lack of a codified strategy for STEMI care. It is essential to develop pre-hospital emergency medicine and sensitize the population and healthcare professionals.

Effectiveness of a single lead AliveCor electrocardiogram application for the screening of atrial fibrillation: A systematic review.

Increasing prevalence of atrial fibrillation has a significant impact on health, society, and healthcare resource utilization, due to increased morbidity, mortality, risk of stroke, and reduction in quality of life. Early diagnosis allows for treatment initiation, a reduction in complications and associated costs, and so innovation to improve screening and enable easy access are needed Developments in digital technology have significantly contributed to the availability of screening tools. The single-lead electrocardiogram AliveCor (Mountainview, CA) device offers the opportunity to provide heart rhythm screening and has been used extensively in clinical practice and research studies. This review investigates the feasibility, validity, and utility of the AliveCor device as a tool for atrial fibrillation detection in clinical practice and in wider research. Databases searched included PUBMED, CINAHL, MEDLINE, and World of Science, plus grey literature search. Search terms related to atrial fibrillation, screening, and AliveCor with adults >18 years. Feasibility metrics were applied including process, resource, management, and scientific outcomes. Studies not written in the English language were excluded. Validity of AliveCor was explored by extracting sensitivity and specificity data from eligible studies and overall effectiveness analyzed by incorporating the above, with wider issues surrounding screening approaches, cost effectiveness and appropriateness of AliveCor as a screening tool. The AliveCor device screening was reviewed in 11 studies matching inclusion criteria. Atrial fibrillation detection rates ranged from 0.8% to 36% and this largely correlated to the study population, where wider age inclusion and mass/population screening represented lower atrial fibrillation detection. Recruitment from higher-risk groups (older age, targeted localities, chronic disease) identified higher numbers with atrial fibrillation. Feasibility metrics demonstrated AliveCor as an effective tool of choice in terms of process, resources, and management. Duration of screening time had an impact on rates of atrial fibrillation detection. There was however significant heterogeneity between studies reviewed. The AliveCor device offers a convenient, valid, and feasible means of monitoring for atrial fibrillation. Further analysis of electrocardiograms produced by AliveCor may be necessary in some circumstances. The AliveCor electrocardiogram device can be successfully implemented into both opportunistic and systematic screening strategies for atrial fibrillation.

An Open-Access Arrhythmia Database of Wearable Electrocardiogram

PurposeHeart arrhythmias result from any disturbance in the rate, regularity, and site of origin or conduction of the cardiac electric pulse. Sporadic and underappreciated characteristics make diagnosis less timely, leading to stroke, heart failure, or even sudden death. Wearable electrocardiogram (ECG) devices are gradually becoming the main trend of intelligent diagnosis alongside the improvement of information transmission and computation power of the hardware. Therefore, a database for arrhythmia detection was planned to construct.MethodsCollaborating with hospital, this study presents a bipolar limb two-lead wearable device by collecting a large amount of real-time data and giving rise to readily available ECG databases.ResultsIn total, the database contains 2000 30-s recordings of sinus, atrial and ventricular arrhythmias collected from more than 200 voluntary patients who had been diagnosed with heart diseases, ranging in age from 18 to 82. Meanwhile, manual annotations by cardiologists were proposed to benefit and instruct non-medical researchers to design the algorithm reasonably.

Comparison of General Anesthesia (Sevoflurane) and Spinal Anesthesia (Levobupivacaine) Methods on QT Dispersion in Inguinal Hernia Operations.

IntroductionArrhythmias are one of the most frequently seen issues during surgical operations. In this study, we investigated and compared the effects on the QT dispersion of patients when using a method of volatile inhalation mask anesthesia with sevoflurane (VIMA: Group I) and when spinal anesthesia was performed with levobupivacaine (Group II).MethodsThe study included 40 patients who had American Society of Anesthesiology scores of I-II (ASA I-II), were aged from 18 to 65 years, and were scheduled for inguinal hernia operations. Approval of the university ethics committee was obtained before the study began. All patients had measurements taken for non-invasive blood pressure, including systolic arterial pressure (SAP), diastolic arterial pressure (DAP), and mean arterial pressure (MAP), heart rate (HR), and oxygen saturation (SO2) values. The QT intervals were measured using the 12-derivation electrocardiogram (ECG) device (Cardiofax V). Our study was performed with randomization using the closed envelope method.ResultsWhen the percentage differences of the HR values from the initial period in both groups were compared, we observed significant differences between the groups, with increases in the VIMA group at the second period as well as increases in the VIMA group at the fourth, fifth, sixth, seventh, and ninth periods but decreases in the spinal anesthesia group for these periods. There were statistically significant differences between the two groups at the third and fifth periods when the percentage differences of the QTc values from the initial period were compared. We observed increases in the spinal anesthesia group.ConclusionIn our study, we suggest that the tendency toward arrhythmia may be reduced by choosing general anesthesia with sevoflurane rather than levobupivacaine in patients with cardiac complaints who are undergoing regional anesthesia and/or taking medication that affects QT intervals.

Denoising Wearable Armband ECG Data Using the Variable Frequency Complex Demodulation Technique.

We propose a novel electrocardiogram (ECG) denoising technique using the variable frequency complex demodulation (VFCDM) algorithm. We used VFCDM to perform the sub-band decomposition of the noise-contaminated ECG to remove the noise components so that accurate QRS complexes could be identified. The ECG quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. The proposed method was validated on the MIT-BIH arrhythmia database (MITDB) and wearable armband ECG data. For the former, we added Gaussian white noise to the ECG signals at different signal-to-noise ratios and the denoising performance of the proposed method was compared with other denoising techniques. The proposed approach showed superior denoising performance compared to the other methods. We compared the QRS complex detection performance of the noisy to the denoised armband ECG. The performance of the proposed denoising method on the armband ECG resulted in comparable QRS complex detection as that obtained when using Holter monitor ECG signals. This demonstrates that the proposed algorithm can significantly increase the amount of usable armband ECG data, which would otherwise have been discarded due to electromyogram contamination especially during arm movements. Hence, the proposed algorithm has the potential to enable long-term monitoring of atrial fibrillation using the armband without the discomfort of skin irritation often experienced with Holter monitors.Clinical Relevance- The proposed ECG denoising method can significantly increase the ECG quality of wearable ECG devices, which are more susceptible to muscle and motion artifacts.

Compressive Sampling Based Multi-Spectrum Deep Learning for Sub-Nyquist Pacemaker ECG Analysis.

Automatic electrocardiogram (ECG) analysis for pacemaker patients is crucial for monitoring cardiac conditions and the effectiveness of cardiac resynchronization treatment. However, under the condition of energy-saving remote monitoring, the low-sampling-rate issue of an ECG device can lead to the miss detection of pacemaker spikes as well as incorrect analysis on paced rhythm and non-paced arrhythmias. To solve the issue, this paper proposed a novel system that applies the compressive sampling (CS) framework to sub-Nyquist acquire and reconstruct ECG, and then uses multi-dimensional feature-based deep learning to identify paced rhythm and non-paced arrhythmias. Simulation testing results on ECG databases and comparison with existing approaches demonstrate its effectiveness and outstanding performance for pacemaker ECG analysis.

Detection of atrial fibrillation with implantable loop recorders in horses.

BackgroundCardiac arrhythmias in horses are diagnosed by auscultation or electrocardiogram (ECG), which results in a low sensitivity for detecting arrhythmias that occur sporadically. Implantable loop recorders (ILRs) are small ECG devices placed subcutaneously, to automatically detect arrhythmias in human patients.ObjectivesTo test ILRs ability to detect atrial fibrillation (AF) in horses. Furthermore, we hypothesised that anatomical location of the implant site might influence signal quality. Signal quality was evaluated both during exercise and over time.Study designExperimental study.MethodsIn five Standardbred mares, eleven ILRs were implanted subcutaneously in up to three different positions (Front: pectoral region, Left‐6: sixth left intercostal space and Ventral: xiphoid region) and AF induced. The R‐ and T‐wave amplitudes were measured in all positions over time during AF. AF burden automatically registered by the ILRs over a 2‐month period was compared with selected Holter ECG recordings.ResultsAll three positions had stable R‐ and T‐wave amplitudes during the study period and were of sufficient quality to allow AF detection at rest. The position Left‐6 showed significantly higher R‐ and T‐wave amplitudes compared with the other positions. During submaximal exercise only the Left‐6 position was able to record ECG signals of diagnostic quality. No position yielded diagnostic signals at maximum exercise due to artefacts.Main limitationsFew horses and ILRs included and no spontaneous AF episodes were studied.ConclusionsThis preliminary study indicates that ILRs can be used for AF detection in horses, but the anatomical location is important for optimal ECG quality. Despite insufficient quality during exercise, ILRs were suitable for AF detection at rest. Therefore, the ILR may be a valuable diagnostic tool for detecting paroxysmal AF in horses.

Pharmacists detecting atrial fibrillation in general practice: a qualitative focus group study.

BackgroundAtrial fibrillation (AF) affects up to 10% of people aged ≥65 years, yet a third of all cases remain undetected. Practice-based pharmacists are in an ideal position to facilitate opportunistic AF screening, while increasing general practice capacity at a time of workforce crisis.AimTo explore the perspectives of three stakeholder groups involved in the ‘Pharmacists Detecting Atrial Fibrillation’ (PDAF) study to elucidate the facilitators and barriers to pharmacist-led AF screening in general practice.Design & settingA qualitative study took place, comprising homogeneous focus groups with stakeholders in Kent, UK.MethodThe stakeholder groups — patients, general practice staff (GPS), and clinical pharmacists (CPs) — were recruited using convenience sampling. Audio-recordings were transcribed verbatim and analysed using a deductive Theoretical Domains Framework (TDF) approach.ResultsTwenty-five patients, four pharmacists, and nine practice staff participated in six focus groups. Three main themes were identified: knowledge and awareness; prioritisation of resources; and environmental considerations. The public’s lack of awareness of AF-related risks and pharmacist-led screening services was highlighted. Practice-based pharmacists were perceived as an underutilised educational resource which, together with novel electrocardiogram devices, enabled convenient access to screening while reducing GPs’ workload. Participants agreed that AF screening should be incorporated into personalised health checks and at-risk groups should be prioritised, such as care home residents. Patients favoured the general practice environment over the community pharmacy where concerns of privacy, staffing, and commercialisation were raised.ConclusionThe findings of this study support the introduction of pharmacist-led AF screening programmes in general practice surgeries. Commissioners should consider the added value of utilising CPs and focus on the delivery of AF screening within an integrated service.

Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network.

Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In this study, we designed the hardware for a three-lead patch-type ECG device and employed a long short-term memory (LSTM) network that can overcome the limitations of the linear regression algorithm used for ECG reconstruction. The LSTM network can overcome the issue of reduced horizontal components of the vector in the electric signal obtained from the patch-type device attached to the anterior chest. The reconstructed 12-lead ECG that uses the LSTM network was tested against a standard 12-lead ECG in 30 healthy subjects and ECGs of 30 patients with pathologic findings. The average correlation coefficient of the LSTM network was found to be 0.95. The ability of the reconstructed ECG to detect pathologic abnormalities was identical to that of the standard ECG. In conclusion, the reconstruction of a standard 12-lead ECG using a three-lead patch-type device is feasible, and such an ECG is an equivalent alternative to a standard 12-lead ECG.

57Accuracy of automated atrial fibrillation detection of a single-lead electrocardiogram device in a pharmacy-based mass screening

Abstract Funding Acknowledgements The investigators received an unrestricted research grant by Pfizer/BMS. Pfizer/BMS was not involved in the planning, conduct, analysis, or interpreta Background Recently, various devices specifically designed to automatically detect atrial fibrillation, became available. Utility of automated atrial fibrillation detection in mass-screening remains unclear, because accuracy and validation of automated AF detection were mostly tested retrospectively in preselected cohorts with high atrial fibrillation prevalence. Purpose The aim of this analysis was to determine the accuracy of automated atrial fibrillation detection by a single-lead hand-held ECG device in a prospective population-based screening approach with several thousand subjects. Methods For automated atrial fibrillation detection, we investigated the MyDiagnostick a 30 cm long hand-held ECG-stick. All customers aged 65 years or older in the pharmacies were eligible to participate. After informed consent was obtained, a one-minute single-lead ECG was recorded by trained staff. Five levels of human ECG expert validation were performed to determine heart rhythm. All recorded unfiltered ECGs (Figure 1) were analyzed at least by two independent, blinded investigators, in case of an automated AF detection by at least 3 independent, blinded investigators. In case of contradiction in the first three levels of heart rhythm assessment, ECGs were analyzed in level 4 by two independent cardiologists. In level 5 all single-lead ECGs were unblinded and any single-lead ECG with a contradiction between the human ECG expert results and the automated ECG-Stick result were analyzed by a rhythm consortium consisting of 3 cardiologists. Final results for heart rhythm and ECG quality were stipulated by a hierarchical approach, whereas results in higher levels of analysis vote down results in earlier levels of analysis in case of a contradiction. Results In total, 7209 single-lead ECGs were available for analysis. The mean age was 74 ± 6 years, 42% (N = 3049) of participants were male, mean CHA2DS2-VASc score was 3.3 ± 1.4. We identified 5.3% (N = 383) patients with atrial fibrillation identified by human investigators. The ECG-Stick diagnosed 79% (N = 304) of the atrial fibrillation patients correctly. In 21% (N = 79/383) the result was false-negative, and in 1% (N = 72/7209) false-positive. In another 1% (N = 81/7209) single-lead ECG recordings were uninterpretable by expert investigators, although the automated analysis provided a result (SR: 27, atrial fibrillation: 54). Using final level results by expert ECG reading as reference, automated ECG-Stick analysis showed good prediction in receiver operating calculation of atrial fibrillation with an area under the curve of 0.89, sensitivity: 80%, specificity: 98%, and a good interclass correlation coefficient of 0.75, 95%CI (0.74; 0.76)). Conclusions The results encourage the implementation of single-lead ECG devices for automated atrial fibrillation screening. A rhythm diagnosis by single-lead ECG seems possible for recordings with excellent ECG signal quality. Abstract Figure. Single-lead ECG example (5 of 60 sec)

Using Biosensors and Digital Biomarkers to Assess Response to Cardiac Rehabilitation: Observational Study.

BackgroundCardiac rehabilitation (CR) is known for its beneficial effects on functional capacity and is a key component within current cardiovascular disease management strategies. In addition, a larger increase in functional capacity is accompanied by better clinical outcomes. However, not all patients respond in a similar way to CR. Therefore, a patient-tailored approach to CR could open up the possibility to achieve an optimal increase in functional capacity in every patient. Before treatment can be optimized, the differences in response of patients in terms of cardiac adaptation to exercise should first be understood. In addition, digital biomarkers to steer CR need to be identified.ObjectiveThe aim of the study was to investigate the difference in cardiac response between patients characterized by a clear improvement in functional capacity and patients showing only a minor improvement following CR therapy.MethodsA total of 129 patients in CR performed a 6-minute walking test (6MWT) at baseline and during four consecutive short-term follow-up tests while being equipped with a wearable electrocardiogram (ECG) device. The 6MWTs were used to evaluate functional capacity. Patients were divided into high- and low-response groups, based on the improvement in functional capacity during the CR program. Commonly used heart rate parameters and cardiac digital biomarkers representative of the heart rate behavior during the 6MWT and their evolution over time were investigated.ResultsAll participating patients improved in functional capacity throughout the CR program (P<.001). The heart rate parameters, which are commonly used in practice, evolved differently for both groups throughout CR. The peak heart rate (HRpeak) from patients in the high-response group increased significantly throughout CR, while no change was observed in the low-response group (F4,92=8.321, P<.001). Similar results were obtained for the recovery heart rate (HRrec) values, which increased significantly over time during every minute of recuperation, for the high-response group (HRrec1: P<.001, HRrec2: P<.001, HRrec3: P<.001, HRrec4: P<.001, and HRrec5: P=.02). The other digital biomarkers showed that the evolution of heart rate behavior during a standardized activity test differed throughout CR between both groups. These digital biomarkers, derived from the continuous measurements, contribute to more in-depth insight into the progression of patients’ cardiac responses.ConclusionsThis study showed that when using wearable sensor technology, the differences in response of patients to CR can be characterized by means of commonly used heart rate parameters and digital biomarkers that are representative of cardiac response to exercise. These digital biomarkers, derived by innovative analysis techniques, allow for more in-depth insights into the cardiac response of cardiac patients during standardized activity. These results open up the possibility to optimized and more patient-tailored treatment strategies and to potentially improve CR outcome.

Evaluation of left ventricular systolic function using synchronized analysis of heart sounds and the electrocardiogram

Heart failure is a major health concern and often requires echocardiography to confirm the diagnosis. We introduce a new method that uses a wearable heart sound and electrocardiogram (ECG) device that can be used in the outpatient setting. The purpose of this study was to determine the value of synchronized analysis of heart sounds and ECG in identifying patients with depressed left ventricular ejection fraction (dLVEF) <50%. One hundred eighty-nine patients (76 with dLVEF; 113 with normal ejection fraction) were enrolled. All were admitted to the hospital because of dyspnea or chest discomfort. N-Terminal pro-B-type natriuretic peptide (NT-proBNP) was measured in all patients. LVEF was determined by echocardiography. Heart sound and ECG signals were simultaneously recorded using the wearable synchronized phonocardiogram and ECG device. Heart sound and ECG signals were automatically analyzed using wavelet analysis and utilized to determine electromechanical activation time (EMAT), EMAT/RR, S1-S2 time, and S1-S2/RR. EMAT in the dLVEF group was significantly higher than that in the control group (159.82 ± 83 ms vs 91.58 ± 28 ms). Pearson correlation test showed a negative correlation between EMAT and LVEF (r = -0.449; P <.001). Receiver operating characteristic curve analysis demonstrated that the sensitivity and specificity of EMAT ≥104 ms for the diagnosis of EF <50% were 92.1% and 92%, respectively. Patients with intermediate NT-proBNP values were identified as dLVEF by EMAT ≥104 ms, with sensitivity of 93.5% and specificity of 92.8%. The heart sound and ECG signal index EMAT contributes to the diagnosis of EF <50% and is especially helpful in patients with an inconclusive NT-proBNP value.

A comparative study to evaluate the clinical utility and performance of a new hand-held mobile electrocardiogram device

Background: Electrocardiogram (ECG) is a non-invasive test which can provide clue for the presence of cardiac diseases. Simple, handheld devices, sufficiently miniaturized are useful for a widespread use. New devices, however, need to be compared with the standard ones for their performance in the real-world practice. Here in we report clinical utility of a handheld device.Methods: KardioscreenTM is a mobile and handheld device. It’s been approved for safety and performance standards and it has been certified for ‘Conformite Europeenne’ (CE). Using this device, a comparative blinded study with a conventional and commercially available standard 12 lead ECG machine was one. 604 ECGs recorded from 302 patients with various clinical disorders were coded and analyzed by two blinded observers. A third cardiologist adjudicated the reports. The reports were then correlated for the ECG patterns generated and with the clinical diagnosis. Computer generated measurements of various durations and intervals were also analyzed and compared. Regression analysis was used to compare the values. SPSS 21 software was used to analyze the data.Results: Kardioscreen device could provide recordings to diagnose including ST elevation (99%), non-ST elevation myocardial infarction (94.1%), chamber-hypertrophy (87%), conduction blocks (99%), and arrhythmias (96.4%), with good correlations with the comparator for pattern recognition. Also, computer generated measurements were significantly correlated with the comparator (R=0.96 for HR, R=0.82 for QRSd, R=0.86 for QT/QTc, R=0.76 for PR).Conclusions: The Kardioscreen device is a reliable tool for electrocardiographic diagnosis of common clinical cardiac disorders.

Use of Smart Technology for the Early Diagnosis of Complications After Cardiac Surgery: The Box 2.0 Study Protocol.

BackgroundAtrial fibrillation (AF), sternal wound infection, and cardiac decompensation are complications that can occur after cardiac surgery. Early detection of these complications is clinically relevant, as early treatment is associated with better clinical outcomes. Remote monitoring with the use of a smartphone (mobile health [mHealth]) might improve the early detection of complications after cardiac surgery.ObjectiveThe primary aim of this study is to compare the detection rate of AF diagnosed with an mHealth solution to the detection rate of AF diagnosed with standard care. Secondary objectives include detection of sternal wound infection and cardiac decompensation, as well as assessment of quality of life, patient satisfaction, and cost-effectiveness.MethodsThe Box 2.0 is a study with a prospective intervention group and a historical control group for comparison. Patients undergoing cardiac surgery at Leiden University Medical Center are eligible for enrollment. In this study, 365 historical patients will be used as controls and 365 other participants will be asked to receive either The Box 2.0 intervention consisting of seven home measurement devices along with a video consultation 2 weeks after discharge or standard cardiac care for 3 months. Patient information will be analyzed according to the intention-to-treat principle. The Box 2.0 devices include a blood pressure monitor, thermometer, weight scale, step count watch, single-lead electrocardiogram (ECG) device, 12-lead ECG device, and pulse oximeter.ResultsThe study started in November 2018. The primary outcome of this study is the detection rate of AF in both groups. Quality of life is measured with the five-level EuroQol five-dimension (EQ-5D-5L) questionnaire. Cost-effectiveness is calculated from a society perspective using prices from Dutch costing guidelines and quality of life data from the study. In the historical cohort, 93.9% (336/358) completed the EQ-5D-5L and patient satisfaction questionnaires 3 months after cardiac surgery.ConclusionsThe rationale and design of a study to investigate mHealth devices in postoperative cardiac surgery patients are presented. The first results are expected in September 2020.Trial RegistrationClinicalTrials.gov NCT03690492; http://clinicaltrials.gov/show/NCT03690492International Registered Report Identifier (IRRID)DERR1-10.2196/16326

Fully Untethered Battery-free Biomonitoring Electronic Tattoo with Wireless Energy Harvesting

Bioelectronics stickers that interface the human epidermis and collect electrophysiological data will constitute important tools in the future of healthcare. Rapid progress is enabled by novel fabrication methods for adhesive electronics patches that are soft, stretchable and conform to the human skin. Yet, the ultimate functionality of such systems still depends on rigid components such as silicon chips and the largest rigid component on these systems is usually the battery. In this work, we demonstrate a quickly deployable, untethered, battery-free, ultrathin (~5 μm) passive “electronic tattoo” that interfaces with the human skin for acquisition and transmission of physiological data. We show that the ultrathin film adapts well with the human skin, and allows an excellent signal to noise ratio, better than the gold-standard Ag/AgCl electrodes. To supply the required energy, we rely on a wireless power transfer (WPT) system, using a printed stretchable Ag-In-Ga coil, as well as printed biopotential acquisition electrodes. The tag is interfaced with data acquisition and communication electronics. This constitutes a “data-by-request” system. By approaching the scanning device to the applied tattoo, the patient’s electrophysiological data is read and stored to the caregiver device. The WPT device can provide more than 300 mW of measured power if it is transferred over the skin or 100 mW if it is implanted under the skin. As a case study, we transferred this temporary tattoo to the human skin and interfaced it with an electrocardiogram (ECG) device, which could send the volunteer’s heartbeat rate in real-time via Bluetooth.

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibrillation (AF) with special emphasis on the combination of deep learning based algorithm and modified patch-based ECG lead. Fifty-five consecutive patients were monitored for AF in around 24 hours by patch-based ECG devices along with a standard 12-lead Holter. Catering to potential positional variability of patch lead, four typical positions on the upper-left chest were proposed. For each patch lead, the performance of automated algorithms with four different convolutional neural networks (CNN) was evaluated for AF detection against blinded annotations of two clinicians. A total of 349,388 10-second segments of AF and 161,084 segments of sinus rhythm were detected successfully. Good agreement between patch-based single-lead and standard 12-lead recordings was obtained at the position MP1 that corresponds to modified lead II, and a promising performance of the automated algorithm with an R-R intervals based CNN model was achieved on this lead in terms of accuracy (93.1%), sensitivity (93.1%), and specificity (93.4%). The present results suggest that the optimized patch-based ECG lead along by deep learning based algorithms may offer the possibility of providing an accurate, easy, and inexpensive clinical tool for mass screening of AF.