Single-lead Electrocardiography Research Articles

Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography

Although screening and early diagnosis is critical for preventing irreversible progression of pulmonary hypertension (PH) and preventable mortality, there are no suitable tools for this purpose. We developed and validated an artificial intelligence (AI) algorithm for predicting PH using electrocardiography (ECG). This retrospective cohort study included data from two hospitals. An AI algorithm was developed using 56,670 ECGs from 24,202 patients. Internal validation of the algorithm was performed with 3174 patients from one hospital, while external validation was performed with 10,865 patients from another hospital. The endpoint was the diagnosis of PH, confirmed by echocardiography. We used demographic information, features, and 500-Hz ECG raw data as predictive variables. Additionally, we identified which region had the most significant effect on the decision-making of the AI algorithm using a sensitivity map. During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm using a 12-lead ECG for detecting PH was 0.859 and 0.902, respectively, while that using a single-lead ECG was 0.824 and 0.832, respectively. In the 2939 non-PH individuals at initial echocardiography, those patients that the AI defined as high risk had a significantly higher chance of development of PH than the low risk group (31.5% vs. 5.9%, p <0.001) during the follow-up period. The sensitivity map showed the AI algorithm focused on the S-wave, P-wave, and T-wave for each patient by QRS complex characteristics. The AI algorithm demonstrated high accuracy for PH prediction using 12-lead and single-lead ECGs.

Screening for arrhythmia with the new portable single-lead electrocardiographic device (SnapECG): an application study in community-based elderly population in Nanjing, China.

SnapECG is a new handheld single-lead electrocardiograph (ECG) device used for arrhythmia screening, it is widely used in clinical practice but not in primary care. To evaluate the arrhythmia screening value of SnapECG among a community-based population. A cross-sectional community-based study of multistage stratified cluster sampling was conducted from March 1st to April 30th 2019. The sensitivities, specificities and the area under the receiver operating characteristic (AUCROC) curves of the SnapECG and reference 12-lead ECG on arrhythmia were calculated in three age-groups [50-64years, 65-74years, and over-75years]. A total of 2263 participants took part in the arrhythmia screening, these included 1479 aged 50-64 years, 602 aged 65-74years and 182 aged over-75years. The SnapECG categorized 1828 (80.8%) as sinus rhythm, 161 (7.1%) as premature atrial/ventricular contractions (PAVs/PCVs), 32 (1.4%) as possible atrial fibrillation (AF), 56 (2.5%) as supraventricular tachycardias or sinus bradycardia (SVT/SB) and 186 (8.2%) as unreadable. SnapECG had 89% sensitivity (95% CI 0.52-1.00) and 99% specificity (95% CI 0.97-0.99) of detecting AF in the 65-74years age-group. The AUCROC to detect AF was 0.94 for the 65-74years age-group, 0.77 for over-75years, 0.62 for the 50-64years. This study is the first community screening application of SnapECG. Main limitation is the SnapECG and the 12-lead ECG were not done simultaneously. In the people aged 65-74years, AF can be detected accurately by the SnapECG with high sensitivity, specificity and large area under the ROC curve, which might have the highest screening predictive accuracy.

Artificial intelligence for detecting mitral regurgitation using electrocardiography

BackgroundScreening and early diagnosis of mitral regurgitation (MR) are crucial for preventing irreversible progression of MR. In this study, we developed and validated an artificial intelligence (AI) algorithm for detecting MR using electrocardiography (ECG). MethodsThis retrospective cohort study included data from two hospital. An AI algorithm was trained using 56,670 ECGs from 24,202 patients. Internal validation of the algorithm was performed with 3174 ECGs of 3174 patients from one hospital, while external validation was performed with 10,865 ECGs of 10,865 patients from another hospital. The endpoint was the diagnosis of significant MR, moderate to severe, confirmed by echocardiography. We used 500 Hz ECG raw data as predictive variables. Additionally, we showed regions of ECG that have the most significant impact on the decision-making of the AI algorithm using a sensitivity map. ResultsDuring the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm using a 12-lead ECG for detecting MR was 0.816 and 0.877, respectively, while that using a single-lead ECG was 0.758 and 0.850, respectively. In the 3157 non-MR individuals, those patients that the AI defined as high risk had a significantly higher chance of development of MR than the low risk group (13.9% vs. 2.6%, p < 0.001) during the follow-up period. The sensitivity map showed the AI algorithm focused on the P-wave and T-wave for MR patients and QRS complex for non-MR patients. ConclusionsThe proposed AI algorithm demonstrated promising results for MR detecting using 12-lead and single-lead ECGs.

Enhanced Automated Diagnosis of Coronary Artery Disease Using Features Extracted From QT Interval Time Series and ST–T Waveform

There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST–T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST–T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST–T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST–T segment waveforms in ECG-based automated CAD detection.

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

BackgroundThis paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.MethodsA convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects.ResultsF1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%.ConclusionThe results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Diagnostic Accuracy of a Smartphone-Operated, Single-Lead Electrocardiography Device for Detection of Rhythm and Conduction Abnormalities in Primary Care.

To validate a smartphone-operated, single-lead electrocardiography (1L-ECG) device (AliveCor KardiaMobile) with an integrated algorithm for atrial fibrillation (AF) against 12-lead ECG (12L-ECG) in a primary care population. We recruited consecutive patients who underwent 12L-ECG for any nonacute indication. Patients held a smartphone with connected 1L-ECG while local personnel simultaneously performed 12L-ECG. All 1L-ECG recordings were assessed by blinded cardiologists as well as by the smartphone-integrated algorithm. The study cardiologists also assessed all 12L-recordings in random order as the reference standard. We determined the diagnostic accuracy of the 1L-ECG in detecting AF or atrial flutter (AFL) as well as any rhythm abnormality and any conduction abnormality with the simultaneously performed 12L-ECG as the reference standard. We included 214 patients from 10 Dutch general practices. Mean ± SD age was 64.1 ± 14.7 years, and 53.7% of the patients were male. The 12L-ECG diagnosed AF/AFL, any rhythm abnormality, and any conduction abnormality in 23, 44, and 28 patients, respectively. The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for AF/AFL of 100% (95% CI, 85.2%-100%) and 100% (95% CI, 98.1%-100%). The AF detection algorithm had a sensitivity and specificity of 87.0% (95% CI, 66.4%-97.2%) and 97.9% (95% CI, 94.7%-99.4%). The 1L-ECG as assessed by cardiologists had a sensitivity and specificity for any rhythm abnormality of 90.9% (95% CI, 78.3%-97.5%) and 93.5% (95% CI, 88.7%-96.7%) and for any conduction abnormality of 46.4% (95% CI, 27.5%-66.1%) and 100% (95% CI, 98.0%-100%). In a primary care population, a smartphone-operated, 1L-ECG device showed excellent diagnostic accuracy for AF/AFL and good diagnostic accuracy for other rhythm abnormalities. The 1L-ECG device was less sensitive for conduction abnormalities.

Feasibility of a T-Shirt-Type Wearable Electrocardiography Monitor for Detection of Covert Atrial Fibrillation in Young Healthy Adults

Covert atrial fibrillation (AF) accounts for cryptogenic stroke aetiology in elderly patients and in younger populations. However, asymptomatic AF is difficult to diagnose based on a short electrocardiography (ECG) recording. We evaluated the feasibility of a self-applied continuous ECG monitoring device that can record automatically, easily, and noninvasively in a younger population. We investigated community screening for asymptomatic AF using a wireless single-lead ECG with an electrode embedded in a T-shirt. One hundred men with a CHADS2 score ≥1 who were free from AF and <65 years of age were enrolled. We instructed the participants to wear ECG monitoring devices for at least 4 days/week over 2 months. The proportion of participants with newly detected AF (NDAF) and the monitoring time were evaluated. The mean CHADS2 score was 1.43 ± 0.62. The mean patient age was 52.5 ± 5.4 years. The mean monitoring time was 222 ± 199 hours. NDAF continuing for >30 seconds was detected in 10 participants (10.0%). AF continuing for >6 minutes was detected in 2 participants (2.0%). The T-shirt-type wearable ECG monitoring system was suitable for continuous, daily long-term use among young people with high physical activity, and it had the distinct capability of identifying covert AF.

Data-driven assessment of cardiovascular ageing through multisite photoplethysmography and electrocardiography

The cardiovascular system is designed to distribute a steady flow through its elastic properties. With ageing, fatigue and fracture of elastin lamellae cause a loss of elasticity defined arterial stiffness. Arterial stiffness causes changes of the pulse wave propagation through the arterial tree, which volumetric counterpart can be assessed non-invasively through photoplethysmography (PPG). PPG may be employed in combination with electrocardiography (ECG). It is here reported an implementation of analysis of multisite PPG and single lead ECG relying on Deep Convolutional Neural Networks (DCNNs). DCNNs generate peculiar filters allowing for data-driven automated selection of the features of interest. The ability of a DCNN to predict subject's age from PPG (left and right brachial, radial and tibial arteries plus fingers) and ECG (Lead I) in a healthy male population (age range: 20–70 years) was investigated. A performance in age prediction of 7 years of root mean square error was obtained, which was superior to other feature-based procedures. The accuracy in age prediction of the machinery in the healthy population may serve for the generation of age-matched normal ranges for the identification of outliers suggesting cardiovascular diseases manifesting as fastened cardiovascular ageing which is recognized as a risk factor for ischemic diseases.

Mobile Phone-Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App.

BackgroundMobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered.ObjectiveThis study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals.MethodsA convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single‑lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12‑lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF.ResultsA total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single‑lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single‑lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single‑lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects.ConclusionsThe FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample.

Abstract P274: Association Between Atrial Fibrillation and Central Sleep Apnea in Older Japanese-American Men

Introduction: Several studies have indicated that central sleep apnea (CSA) is associated with atrial fibrillation (AF) in older populations. However, there are few studies of this association in older Asian populations. Hypothesis: The hypothesis being tested is that there is an association between AF and CSA in older Japanese-American men. Methods: The Kuakini Honolulu-Asia Aging Study is a longitudinal cohort study of Japanese-American men living in Hawaii. We did a cross- sectional analysis using data from the 1999- 2000 7th exam cycle. Participants were 718 Japanese-American men between 79 and 97 years old, who had overnight polysomnography (PSG) conducted in their homes using criteria established by the large multicenter Sleep Heart Health Study. Obstructive Apnea-Hypopnea Index (OAHI) was the measure of the number of obstructive apneas and hypopneas with &gt;4% oxygen desaturation. Additionally, the Central Apnea Index (CAI) was the measure of the number of central apneas. Obstructive sleep apnea (OSA) was categorized as none (OAHI &lt;5), mild (OAHI 5-14), moderate (OAHI 15-29) and severe (OAHI 30 or more). CSA was defined by CAI of 5 or more. Cheyne-Stokes Breathing (CSB) was defined as a minimum consecutive 10-minute period of a crescendo-decrescendo respiratory pattern associated with CSA. A board-certified physician confirmed AF by single lead electrocardiography of PSG. Results: The mean age of participants was 83.2±0.2 years. The prevalence of AF was 5.5% (39 of 709). The prevalence proportions of severe OSA, CSA, and CSB were 20.5% (145 of 709), 6.3% (43 of 681) and 3.2%, (22 of 681) respectively. In multivariable-adjusted logistic regression models, CSA and CSB were significantly associated with AF prevalence [Odds Ratio (OR) 4.78; 95% Confidential interval (CI), 1.91-11.95] and [OR 5.48; 95% CI, 1.70-17.66], respectively. In contrast, OSA was not significantly associated with AF prevalence (severe OSA [OR 1.17; 95% CI, 0.44-3.11], moderate OSA [OR 0.72; 95% CI, 0.26-2.05], mild OSA [OR 0.97; 95% CI, 0.39-2.44]). Conclusion: There is an association of AF prevalence with CSA and CSB in older Japanese-American Men. In older Japanese men, screening for CSA and CSB might be important to prevent AF.

Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger

Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction (MI) from single-lead electrocardiograph (ECG) signals. With our layer-wise training strategies, the SAE-based diagnostic feature extraction network can automatically and steadily extract the deep distinguishing diagnostic features of the single-lead ECG signals and avoid the vanishing gradient problem. This feature extraction network is formed by stacking shallow SAEs. In addition, to automatically learn the stable distinctive feature expression of the label-less input ECG signals, this feature extraction network adopts unsupervised learning. Moreover, TreeBagger classifier can optimize the results of multiple decision trees to more accurately detect and localize MI. The experiment and verification datasets include healthy controls, various types of MI with anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral, from PTB diagnostic ECG database. The evaluation results show that the new techniques can effectively and accurately detect and localize the MI pathologies. For MI detection, the accuracy, the sensitivity, and the specificity rates achieve as high as 99.90%, 99.98%, and 99.52%, respectively. For MI localization, we obtain consistent results with the accuracy of 98.88%, sensitivity 99.95%, and specificity 99.87%. The comparative studies are conducted with the state-of-the-art techniques, and significant improvements by our methods are presented in the context. Success in the development of the accurate and comprehensive tool greatly helps the cardiologists in detection and localization of the single-lead ECG signals of MI.

Identifying the Prevalence of the Life-threatening Atrial Fibrillation Using a Smartphone-Based Wireless Electrocardiography Device: An Observational Study

Background: Atrial Fibrillation (AF) is one of the most common types of arrhythmia across the world. Yet, many cases remain undiagnosed making it difficult to estimate the exact prevalence of AF. Aims: This observational study was done to understand the feasibility of a hand-held electrocardiography (ECG) device – SanketLife – in detecting AF cases. Materials and Methods: In the study, ECG data collected from the device for a period of 2 months were evaluated to detect AF cases. The device took 12-lead and single-lead ECG recordings in various health-care settings including self-monitoring done by patients at their homes and in diagnostic laboratories using the device for screening as well as hospitals using the device in their outpatient departments. The reports were saved in SanketLife Cloud and evaluated anonymously by a certified ECG expert and the findings were further confirmed by a cardiologist to evaluate AF or any other findings in the ECG. Results: SanketLife effectively captured AF cases and demonstrated the feasibility of using such a device in any care setting being clinical or home monitoring. Conclusion: The study concluded that the device can be used for regular monitoring by patients suffering from chronic AF. In health-care settings ranging from primary care to tertiary care, the device can be beneficial.

Diagnostyka elektrokardiograficzna w warunkach SOR za pomocą mobilnego jednoodprowadzeniowego urządzenia EKG

Introduction. Electrocardiography (ECG) is one of the basic diagnostic tests used in emergency departments and by emergency medical services. Life-threatening arrhythmias can be detected using a single-lead ECG. Therefore, single- -lead ECG devices can be used for arrhythmia detection, as their availability steadily increases. Kardia Mobile from Alive- Cor is an example of such a device, recording a single-lead ECG and automatically detecting atrial fibrillation (AF) — the most common complex supraventricular tachyarrhythmia. The aim of our study was to evaluate the utility of a single-lead mobile ECG device in detecting AF in medical practice of emergency services. Material and methods. Study included 118 patients (62 women and 56 men) who were hospitalized in a hospital emergency department and consented to examination with Kardia Mobile immediately after a standard 12-lead ECG. Results of both tests were subsequently compared. Ultimately, 121 different pairs of ECG recordings were analyzed (in 3 cases an additional ECG recording was performed after an electrical cardioversion). Results. Sinus rhythm was identified in 99 patients and 22 were diagnosed with AF using a 12-lead ECG (reference). Kardia Mobile correctly detected AF in 19 of 22 patients with AF (sensitivity: 86.4%) and absence of AF in 96 of 99 people without AF (specificity: 97%). Conclusions. Kardia Mobile device is effective in automated detection of AF among patients hospitalized in the emergency department.

Functional analysis of KCNH2 gene mutations of type 2 long QT syndrome in larval zebrafish using microscopy and electrocardiography

Heterologous expression systems play a vital role in the characterization of potassium voltage-gated channel subfamily H member 2 (KCNH2) gene mutations, such as E637K which is associated with long QT syndrome type 2 (LQT2). In vivo assays using zebrafish provide a means for testing genetic variants of cardiac disease; however, limited information on the role of the E637K mutation is available from in vivo systems and their utility has yet to be fully exploited in the context of LQT2. We sought to evaluate the ability of the E637K mutant channel to restore normal repolarization in larval zebrafish with a human KCNH2 orthologue, kcnh2a-knockdown. A morpholino (MO) targeting kcnh2a was injected alone or with wild type (WT) or E637K KCNH2 cRNA into zebrafish embryos at the 1-2 cell stage. Cardiac repolarization phenotypes were screened using light microscopy and the QT interval was measured by single lead electrocardiograph (ECG) analysis at 72-h post-fertilization. In the MO alone group, 17% of zebrafish had a normal phenotype; this rate increased to 60% in the WT KCNH2 cRNA injected zebrafish and to 35% in the E637K injected zebrafish. The ECG of larval zebrafish revealed that QTc was significantly prolonged in the MO alone group compared to the control group. Co-injection of WT KCNH2 cRNA shortened the QTc interval, however, that of the E637K did not. We suggest that this in vivo cardiac assay using microscopy and ECG in larval zebrafish offers a reliable approach for risk discrimination of KCNH2 mutations.

Evaluation of a Smartphone Electrocardiograph in Healthy Horses: Comparison With Standard Base-apex Electrocardiography

A good diagnostic accuracy of smartphone-based electrocardiography (ECG) in the evaluation of heart rate, heart rhythm, and ECG measurements has been reported in humans, dogs, and cats. The aim of this study was to assess the feasibility of smartphone-based electrocardiography in horses. Fifty healthy adult horses were enrolled. Standard base-apex ECG and smartphone ECG were simultaneously recorded in each horse. All ECGs were reviewed by one blinded operator, who judged whether tracings were acceptable for interpretation and performed electrocardiographic measurements. Agreement between smartphone and standard base-apex ECG in the analysis of tracings was evaluated. Smartphone ECG tracings were interpretable in 48 of 50 (96%) cases. A perfect agreement between smartphone and standard ECG tracings was found in the assessment of heart rate. Heart rate automatically measured by the smartphone application was not reliable. In terms of electrocardiographic waves and interval duration, minimal differences of no clinical value were found between smartphone and standard ECG. Agreement was found for QRS complex polarity evaluation, but not for P wave polarity. Baseline artifacts were rare but significantly higher in the smartphone ECG tracings than those in standard ECGs. The smartphone ECG can record single-lead ECG tracings of an adequate quality for interpretation in horses. The smartphone ECG could represent an additional tool for the electrocardiographic evaluation of horses but is not a substitute for the standard base-apex ECG.

Gabor Filter-Based One-Dimensional Local Phase Descriptors for Obstructive Sleep Apnea Detection Using Single-Lead ECG

Obstructive sleep apnea (OSA) is a sleep disorder that causes abnormal and periodic breath interruptions during sleep. In this article, we present a novel one-dimensional (1-D) phase descriptor (PD) based methodology for single-lead electrocardiograph (ECG) based OSA detection. Specifically, we introduce two 1-D local PDs, referred to as 1-D local phase patterns and 1-D local phase quantization, and study their discriminative ability for OSA detection. Essentially, these PDs are computed using phase information extracted from the ECG signal. In our approach, PDs extracted from the Gabor filter phase responses are used to form the feature vector, which is utilized by a least-squares support vector machine (LS-SVM) for classification. Experimental evaluations on Physionet single-lead ECG dataset suggest that the proposed approach achieves state-of-the-art performance for OSA detection. Specifically, the proposed OSA detection approach achieves an average classification accuracy of 93.31 on ECG signals of 1-min duration, thereby providing a considerable improvement over the existing methods.

The development of a novel high-precision major depressive disorder screening system using transient autonomic responses induced by dual mental tasks

12.6% of major depressive disorder (MDD) patients have suicide intent, while it has been reported that 43% of patients did not consult their doctors for MDD, automated MDD screening is eagerly anticipated. Recently, in order to achieve automated screening of MDD, biomarkers such as multiplex DNA methylation profiles or physiological method using near infra-red spectroscopy (NIRS) have been studied, however, they require inspection using 96-well DNA ELIZA kit after blood sampling or significant cost. Using a single-lead electrocardiography (ECG), we developed a high-precision MDD screening system using transient autonomic responses induced by dual mental tasks. We developed a novel high precision MDD screening system which is composed of a single-lead ECG monitor, analogue to digital (AD) converter and a personal computer with measurement and analysis program written by LabView programming language. The system discriminates MDD patients from normal subjects using heat rate variability (HRV)-derived transient autonomic responses induced by dual mental tasks, i.e. verbal fluency task and random number generation task, via linear discriminant analysis (LDA) adopting HRV-related predictor variables (hear rate (HR), high frequency (HF), low frequency (LF)/HF). The proposed system was tested for 12 MDD patients (32 ± 15 years) under antidepressant treatment from Shizuoka Saiseikai General Hospital outpatient unit and 30 normal volunteers (37 ± 17 years) from Tokyo Metropolitan University. The proposed system achieved 100% sensitivity and 100% specificity in classifying 42 examinees into 12 MDD patients and 30 normal subjects. The proposed system appears promising for future HRV-based high-precision and low-cost screening of MDDs using only single-lead ECG.

Incidence of postoperative atrial fibrillation recurrence in patients discharged in sinus rhythm after cardiac surgery: a systematic review and meta-analysis.

Postoperative atrial fibrillation (POAF) is associated with increased stroke risk and mortality post-discharge. POAF is often considered transient; however, recurrence is likely under-recognized as symptoms are an unreliable guide. Surveillance post-discharge may identify asymptomatic POAF recurrences in patients discharged in sinus rhythm. Therefore, we performed a systematic review and meta-analysis of studies investigating POAF recurrence post-discharge, in patients with new-onset POAF following cardiac surgery who reverted to sinus rhythm prior to discharge. Two independent reviewers searched medical databases, clinical trial registries, reference lists and the Internet. After screening from 6525 studies, 8 studies were identified (n = 1157 participants, mean age 66 ± 10 years and 73% men). Monitoring methods included the following: telemetry during twice-daily exercise sessions (n = 2), continuous telemetry for 3 weeks (n = 1), daily 20-s electrocardiography (ECG) using wearable event recorder (n = 1), 30-s single-lead ECG, 4 times/day (n = 1) and implanted continuous monitoring (n = 2). The incidence rate of POAF recurrence identified through non-invasive monitoring in the first 4 weeks post-discharge was 28.3% [confidence interval (CI) 23.0-33.6%]; recurring 12 ± 5 days (mean ± SD) post-surgery. The incidence rate identified through implanted continuous monitoring was 61-100% within 2 years. Between 40% and 93% of episodes were asymptomatic. In one small study reporting stroke risk, 8 of 10 patients with recurrence were guideline-indicated (CHA2DS2-VASc score ≥2) for oral anticoagulation for stroke prevention. Monitoring for POAF recurrence post-hospital discharge identifies significant numbers of early asymptomatic recurrences in patients at high risk of stroke who may benefit from anticoagulation for stroke prevention. More intense monitoring is more likely to identify POAF recurrence. Future research is required to investigate the prognostic significance of POAF recurrence, especially stroke and mortality risk.

Magnetohydrodynamic Voltage Recorder for Comparing Peripheral Blood Flow.

Blood flow is a clinical metric for monitoring of cardiovascular diseases but current measurements methods are costly or uncomfortable for patients. It was shown that the interaction of the magnetic field (B 0) during MRI and blood flow in the body, through the magnetohydrodynamic (MHD) effect, produce voltages (V MHD) observable through intra-MRI electrocardiography (ECG), which are correlated with regional blood flow. This study shows the reproducibility of V MHD outside the MRI and its application in a portable flow monitoring device. To recreate this interaction outside the MRI, a static neodymium magnet (0.4T) was placed in between two electrodes to induce the V MHD in a single lead ECG measurement. V MHD was extracted, and integrated over to obtain a stroke volume metric. A smartphone-enabled device utilizing this interaction was developed in order to create a more accessible method of obtaining blood flow measurements. The portable device displayed a <6% error compared to a commercial recorder, and was able to successfully record V MHD using the 0.4T magnet. Exercise stress testing showed a V MHD increase of 23% in healthy subjects, with an 81% increase in the athlete. The study demonstrates a new device utilizing MHD interactions with body circulation to obtain blood flow metrics.

Smartphone electrographic monitoring for atrial fibrillation in acute ischemic stroke and transient ischemic attack.

Rationale Paroxysmal atrial fibrillation is a common and preventable cause of devastating strokes. However, currently available monitoring methods, including Holter monitoring, cardiac telemetry and event loop recorders, have drawbacks that restrict their application in the general stroke population. AliveCor™ heart monitor, a novel device that embeds miniaturized electrocardiography (ECG) in a smartphone case coupled with an application to record and diagnose the ECG, has recently been shown to provide an accurate and sensitive single lead ECG diagnosis of atrial fibrillation. This device could be used by nurses to record a 30-s ECG instead of manual pulse taking and automatically provide a diagnosis of atrial fibrillation. Aims To compare the proportion of patients with paroxysmal atrial fibrillation detected by AliveCor™ ECG monitoring with current standard practice. Sample size 296 Patients. Design Consecutive ischemic stroke and transient ischemic attack patients presenting to participating stroke units without known atrial fibrillation will undergo intermittent AliveCor™ ECG monitoring administered by nursing staff at the same frequency as the vital observations of pulse and blood pressure until discharge, in addition to the standard testing paradigm of each participating stroke unit to detect paroxysmal atrial fibrillation. Study outcome Proportion of patients with paroxysmal atrial fibrillation detected by AliveCor™ ECG monitoring compared to 12-lead ECG, 24-h Holter monitoring and cardiac telemetry. Discussion Use of AliveCor™ heart monitor as part of routine stroke unit nursing observation has the potential to be an inexpensive non-invasive method to increase paroxysmal atrial fibrillation detection, leading to improvement in stroke secondary prevention.