ECG Information Research Articles

Improving cardiovascular risk stratification with ECG-predicted risk factors in primary prevention

Abstract Background Cardiovascular risk- and screening scores guide clinical practice. Built upon simple risk factors, they could benefit from information on actual, subclinical cardiac changes. The ECG provides proxies for various cardiac states, which can be detected using machine learning. We systematically assess the added value of ECG-based cardiovascular risk factor estimates for six outcomes in two population-based cohorts: Study of Health in Pomerania (SHIP-Start) and UK Biobank (UKB). Methods We employed convolutional neural networks to analyze ECGs from the Hamburg City Health Study (HCHS) for the assessment of multiple risk factors, including age, weight, diabetes, atrial fibrillation (AF) and heart failure. Cox models were fitted on UKB and SHIP to predict 5 and 15 year time-to-event. Endpoints were death, cardiovascular death, AF, stroke, heart failure and myocardial infarction. Covariates were sex, age, body mass index, diabetes, hypertension and smoking (Cox-RF), ECG-predicted risk factors (Cox-AI), and a combination (Cox-RF/AI). Stratified Monte Carlo cross-validation was used as validation and groups of C-indices were compared using Mann-Whitney U tests. Results The study included 9103 HCHS participants (mean age 62.0±8.4 years, 51% women), 3546 SHIP participants (age 49.7±16.4, 51% women), and 32828 UKB participants (age 64±7.6 years, 52% women). Cox-AI performed comparable or better than Cox-RF for AF (SHIP: 0.88 vs. 0.84, UKB: 0.71 vs. 0.72) and heart failure (SHIP: 0.8 vs. 0.79, UKB: 0.76 vs. 0.75), but was less strong for the other outcomes. The combination of information on cardiovascular risk factors and ECG information (Cox-RF/AI) significantly improved the predictive ability compared to risk factors alone (Cox-RF) for AF (SHIP: 0.89 vs. 0.84, UKB: 0.76 vs. 0.72), cardiovascular death (SHIP: 0.90 vs. 0.89, UKB: 0.75 vs. 0.74) and heart failure (SHIP: 0.81 vs. 0.79, UKB: 0.80 vs. 0.75). Discussion ECG-based risk factors complement risk stratification by incorporating nuanced information on cardiac function and structure, improving risk prediction and thus possibly clinical decision making and patient outcomes.

How does lead selection impact diagnosis through AI? Implications for continuous monitoring systems using convolutional neural networks

Abstract Background and Objectives Recently, there has been a surge in non-invasive diagnosing methods leveraging the dipolar 12-lead ECG information using Convolutional Neural Networks (CNNs). However, the traditional ECG contains 12 projections of the cardiac dipole in a three-dimensional space can result in redundancy. Furthermore, in continuous monitoring systems such as Holter and wearables, not all 12 leads are consistently available. This study aims to quantify redundancy and determine the best lead configuration for improved classification results using a CNN. It evaluates the level of redundancy in ECG leads and its impact on CNN classification. Methods The degree of redundancy was quantified by means of lead entropies (H) as depicted in Figure 1.A. The standard 12-lead ECG was chosen as the baseline input. From it, various subsets of leads were selected (6, 3, 1 leads) with the objective of attaining maximal orthogonality between leads while minimizing redundancy (Figure 1.B). A 2D-CNN model was trained for multiclass classification: Hypertension (HYP), ST-T interval Changes (STTC), Conduction Disturbances (CD), Myocardial Infarction (MI) and Normal (NORM). The designed inputs were employed to evaluate how redundancy impacts the performance of the CNN (see Figure 1.C). Results Reducing redundancy in input leads led to notable improvements in model prediction for all pathological conditions in terms of Area Under the Curve (AUC). Reducing to 6 and 8 leads were the most suitable input for the CNN (Table 1) increasing by 1.18% in HYP, 0.23% in STTC, 0.52% in CD, and 0.33% in MI. However, a significant reduction in redundancy may result in the loss of essential clinical information for classification, as observed in scenarios involving only 3 or 1 leads. Conclusions When diagnosing pathologies with systems featuring fewer leads, its combinations should aim for maximum orthogonality to reduce redundancy while preserving unique information. By strategically selecting a set of leads with reduced input redundancy, the effectiveness of the standard 12-lead ECG can be improved, thereby enhancing the utility of such continuous monitoring systems for automated diagnosis and also decreasing training and prediction times.

Uncertainty and information in physiological signals: Explicit physical trade-off with log-normal wavelets

Physiological recordings contain a great deal of information about the underlying dynamics of Life. The practical statistical treatment of these single-trial measurements is often hampered by the inadequacy of overly strong assumptions. Heisenberg’s uncertainty principle allows for more parsimony, trading off statistical significance for localization. By decomposing signals into time–frequency atoms and recomposing them into local quadratic estimates, we propose a concise and expressive implementation of these fundamental concepts based on the choice of a geometric paradigm and two physical parameters. Starting from the spectrogram based on two fixed timescales and Gabor’s normal window, we then build its scale-invariant analogue, the scalogram based on two quality factors and Grossmann’s log-normal wavelet. These canonical estimators provide a minimal and flexible framework for single trial time–frequency statistics, which we apply to polysomnographic signals: EEG representations, HRV extraction from ECG, coherence and mutual information between heart rate and respiration.

Bidirectional gated recurrent unit with auto encoders for detecting arrhythmia using ECG data

Heartbeat irregularity or arrhythmia of a patient with serious congestive cardiovascular breakdown is distinguished in this article, and the class not really settled utilizing long haul ECG information. Several existing methods on arrhythmia detection are utilized, which does not accurately classify the Arrhythmia and it takes higher computational time. Morphological filtering and Extended Empirical wavelet transformation preprocess the input data to remove noises and base line. Then these pre-processed data is fed to Ternary Pattern and Discrete Wavelet Transform (TP-DWT) related feature extracting to take relevant feature. Then, the extracted features are given to Bidirectional Gated Recurrent Unit with Auto Encoder (BiGRU-AE) to classify Arrhythmia into Normal, Atrial Fibrillation (AF), First-degree atrioventricular block (1-AVB), Left Bundle Branch (LBBB), Right Bundle Branch Block (RBBB), Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), ST-segment depression (STD) and ST-segment elevation (STE). The proposed BiGRU-AE-AD-ECG is activated in Python and its efficiency is measured under certain metrics, like sensitivity, precision, f-measure, specificity, accuracy, error rate, ROC and computational time. Here, CPSC 2018 dataset is used to validate the efficiency of the BiGRU-AE-AD-ECG method. The performance of BiGRU-AE-AD-ECG method provides 23.40 %, 18.78 % and 28.05 % higher accuracy, 20.41 %, 27.07 % and 18.80 % higher precision, 32.01 %, 27.40 % and 28.95 % higher sensitivity and 28.05 %, 23.41 % and 31.34 % higher specificity analyzed with existing models, like automatic cardiac arrhythmias categorization based on CNN and attention-based RNN network (CNN-RNN-AD-ECG), a multiple lead-branch fusion network under 12-Lead ECG for multiple class arrhythmia categorization (MLBFNet-AD-ECG), analysis of ECG-based arrhythmia identification scheme using machine learning (SVM-AD-ECG)respectively.

Removing the cardiac field artifact from the EEG using neural network regression.

When EEG recordings are used to reveal interactions between central-nervous and cardiovascular processes, the cardiac field artifact (CFA) poses a major challenge. Because the electric field generated by cardiac activity is also captured by scalp electrodes, the CFA arises as a heavy contaminant whenever EEG data are analyzed time-locked to cardio-electric events. A typical example is measuring stimulus-evoked potentials elicited at different phases of the cardiac cycle. Here, we present a nonlinear regression method deploying neural networks that allows to remove the CFA from the EEG signal in such scenarios. We train neural network models to predict R-peak centered EEG episodes based on the ECG and additional CFA-related information. In a second step, these trained models are used to predict and consequently remove the CFA in EEG episodes containing visual stimulation occurring time-locked to the ECG. We show that removing these predictions from the signal effectively removes the CFA without affecting the intertrial phase coherence of stimulus-evoked activity. In addition, we provide the results of an extensive grid search suggesting a set of appropriate model hyperparameters. The proposed method offers a replicable way of removing the CFA on the single-trial level, without affecting stimulus-related variance occurring time-locked to cardiac events. Disentangling the cardiac field artifact (CFA) from the EEG signal is a major challenge when investigating the neurocognitive impact of cardioafferent traffic by means of the EEG. When stimuli are presented time-locked to the cardiac cycle, both sources of variance are systematically confounded. Here, we propose a regression-based approach deploying neural network models to remove the CFA from the EEG. This approach effectively removes the CFA on a single-trial level and is purely data-driven, providing replicable results.

MP-453088-11 CLINICAL VALIDATION OF A MOBILE, ARTIFICIAL INTELLIGENCE-GUIDED, 12-LEAD ECG DEVICE

Single-lead, mobile ECG devices have enhanced the feasibility of arrhythmia diagnosis. However, the diagnosis of other cardiac pathologies is hindered by the use of a single lead, as compared to traditional 12-lead ECG. The Alivecor Kardia 12L device is a novel device that can record 12-lead ECG. In this study, we evaluated the accuracy of Kardia 12L devices compared to a 12-lead ECG. Patients seen in an outpatient cardiology clinic were enrolled in the study. Kardia 12L (leads I, II, V2 and V4) was obtained simultaneously with a 12-lead ECG in the supine position. Kardia 12L uses a Deep Neural Network model to expand the 7 leads into a complete 12-lead ECG. Median beats from each method were compared using Pearson’s correlation. One hundred patients (mean age 57±18 years, 55% female) were enrolled. QRS duration, QT duration, R wave amplitude and ST segment amplitude showed excellent correlation between Kardia 12L and 12-lead ECG (r=0.97, r=0.98, r=0.99 and r=0.96, respectively, p<0.001; Figure). Kardia 12L data is all highly correlated with the 12-lead ECG, enabling rapid acquisition of complete ECG information. This approach has significant implications for rapid ECG diagnosis in clinical practice.

A novel weighted hybrid clinical and electrocardiographic score to predict the origin of outflow tract ventricular arrhythmias: a multicenter study

Abstract Funding Acknowledgements Type of funding sources: None. Background To correctly predict the outflow tract ventricular arrhythmia (OTVA) site of origin (SOO) before the ablation is still a procedural major step, having important implications for correct patient counseling, ablation planning, and periprocedural complication evaluation. Although multiple ECG criteria have been proposed for predicting OTVA SOO, their accuracy and usefulness are still limited as recently (1). Purpose The present study sought to prospectively evaluate the accuracy of a previously published Hybrid Score (HS) for prediction of OTVAs with LVOT origin. Besides, multiple ECG measurements of patients with V3 precordial transition (V3PT) OTVA were analyzed to identify potential variables useful to improve the accuracy of the HS (2). Methods Consecutive patients (n=105) referred for OTVA ablation were prospectively enrolled from three referral centers. Vascular access and first-mapped ventricle were decided based on the previously published HS, which includes ECG (R/S precordial transition) and clinical information (age, hypertension, and gender). Surface ECGs during the OTVA were analyzed by two independent electrophysiologists to compare the discriminative performance of HS and previous published ECG-alone criteria, to assess their inter-observer variability and to identify potential variables useful to improve the proposed model. Results Of the 105 VAs, 70.5% had an RVOT origin and 29.5% an LVOT origin. HS achieved a correct prediction in 90% patients. This rate dropped to 75% in the subgroup of patients with V3PT OTVA. The correct prediction rate of the other ECG-alone criteria in the whole population ranged from 74 to 82% and from 41 to 76% in V3PT patients, and their Cohen’s Kappa coefficient for inter-observer variability assessment ranged from 0.63 to 0.81. R-wave amplitude in V3 was the best ECG parameter for discriminating LVOT origin in V3PT patients. When this parameter was incorporated in the novel Weighted Hybrid Score (WHS) (Figure 1), it correctly classified 99 (94.2%) patients (90% sensitivity, 96% specificity, AUC: 0.97). The WHS discriminatory capacity was maintained in V3PT subgroup, (87% sensitivity, 91% specificity AUC: 0.95). WHS showed a Cohen’s kappa coefficient of 0,82. Finally, WHS was validated in an additional population of 97 patients with OTVAs referred for catheter ablation from three additional external centers; in this testing sample the WHS correctly predicted the SOO in 90% patients and proved to have 89% sensitivity and 90% specificity (AUC: 0.94) for a score ≥2 to predict a LVOT origin. Conclusions The novel simple-to-use WHS proved to accurately anticipate the PVC’s SOO and can be introduced in clinical practice for choosing the first chamber to map.

PO-639-01 ENHANCED P WAVE RECOGNITION THROUGH RA-XIPHOIDAL LEAD BY HUAWEI WATCH ECG RECORDING

ECG recording through the Huawei watch’s (GT2 pro ECG edition, Huawei Technology Co., Ltd, Shenzhen, PR China) back electrode on the left wrist and the right index finger on the crown simulates the Einthoven’s bipolar ECG lead I (Huawei Watch Lead I ECG, HWIG), which is not a good trace for arrhythmia diagnosis due to its indistinct P wave in some cases. Therefore, additional smartwatch leads such as right arm-left leg lead, right arm-precordial leads are required to get more accurate ECG information.

Toward sleep apnea detection with lightweight multi-scaled fusion network

Sleep apnea (SA) is a common sleep disorder that occurs during sleep, leading to the decrease of oxygen saturation in the blood. It would develop a variety of complications like diabetes, chronic kidney disease, depression, cardiovascular diseases, or even sudden death. Early SA detection can help physicians to take interventions for SA patients to prevent malignant events. This paper proposes a lightweight SA detection method of multi-scaled fusion network named SE-MSCNN based on single-lead ECG signals. The proposed SE-MSCNN mainly includes multi-scaled convolutional neural network (CNN) module and channel-wise attention module. In order to facilitate the SA detection performance, various scaled ECG information with different-length adjacent segments are extracted by three sub-neural networks. To overcome the problem of local concentration of feature fusion with concatenation, a channel-wise attention module with a squeeze-to-excitation block is employed to fuse the different scaled features adaptively. Furthermore, the ablation study and computational complexity analysis of the SE-MSCNN are conducted. Extensive experiment results show that the proposed SE-MSCNN has the performance superiority to the state-of-the-art methods for SA detection on the Apnea-ECG benchmark dataset. The SE-MSCNN with the merits of quick response and lightweight parameters can be potentially embedded into a wearable device to provide an SA detection service for individuals in home sleep test (HST). All codes and models are released at https://github.com/Bettycxh/Toward-Sleep-Apnea-Detection-with-Lightweight-Multi-scaled-Fusion-Network.

Non-invasive mapping reduces procedure duration for catheter ablation of premature ventricular contractions using 12 lead ECG information: results from a prospective case-control study

Abstract Background Premature ventricular complexes (PVCs) result from early depolarisation of myocardial tissue in the ventricles and can ultimately lead to PVC-induced cardiomyopathy. Catheter ablation aims to completely eliminate the PVCs and is usually carried out using sequential activation mapping techniques. We report the results of a prospective case-control trial using the non-invasive, simultaneous 3D mapping system View into Ventricular Onset (VIVO) in combination with a 12 lead Holter ECG recording for catheter ablation of PVC. Methods and results A total of 16 patients (mean age 52.1±20.8 years, all structurally normal hearts, were enrolled and underwent VIVO mapping as a preparation of their invasive PVC ablation. Pre-ablation PVC burden had a median of 18.7% (IQR; 9.94 – 24.35). All but one patient (with very low PVC burden and epicardial origin) underwent a catheter ablation attempt guided by the 3D non-invasive maps integrated as VTK files in to the EAM. In 13/15 patients, acute elimination of the PVC was achieved which resulted in a PVC burden of &amp;lt;0.1% at 3 months' time. Procedure time amounted to in median 106 minutes (IQR 92.5 – 135.5), whereas in an age- and gender-matched control cohort (2:1) procedure time was significantly longer (145 (123.0 – 190.5). Conclusion Ward-based non-invasive 3D mapping (VIVO) of the origin of PVCs in patients with structurally normal hearts successfully guided the invasive ablation procedure and significantly reduced the invasive procedure time, as compared to a contemporary age- and gender-matched control cohort. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Catheter Precision 3D non-invasive mapping concept

Prospective evaluation of a hybrid clinical and electrocardiographic algorithm to predict the origin of outflow tract PVCs. A prospective multicenter study

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Beca de la Asociación del Ritmo Cardiaco para Formación en Investigación Post-Residencia en centros españoles. Background To predict the premature ventricular complex (PVC) site of origin (SOO) before the ablation procedure has important implications. 12 lead ECG information as well clinical characteristics are related with the PVC-SOO, but there is no prospective data validation. Purpose This prospective multicenter study sought to analyze the applicability and accuracy of a simple hybrid algorithm that includes electrocardiographic and clinical information in a consecutive patient population with outflow tract (OT) PVCs undergoing catheter ablation. Methods Consecutive patients with frequent OT-PVCs and indication for catheter ablation were prospectively included in 4 centers. The vascular access (femoral vein vs femoral artery) and the first outflow tract to be mapped were decided based on a two-steps hybrid algorithm (see Figure 1) including ECG information [R/S PVC precordial transition (PT): early PT if it occurs before V3; late PT if beyond V3) and clinical information [the following variables compute 1 point in a clinical score: age (&amp;gt;59 y.o); presence of hypertension; and gender (man)]. Results 115 consecutive patients [42 (37%) man, 52 + 15 y.o] were included. Mean PVC burden at baseline was 20 ± 13% and mean LVEF was 59 ± 8%. Mean procedure time was 55 ± 26 min. Complete acute abolition of the PVC was achieved in 110 (96%) patients. 84 (73%) patients had the PVC’s SOO in the right ventricle OT (RVOT) whereas 31 (27%) in the left ventricular OT (LVOT). Compared with patients with a RVOT-SOO, those with a LVOT-SOO were more frequently man [18 (58%) vs 24 (29%), p = 0.004], more frequently suffered from hypertension [18 (58%) vs 15 (18%), p &amp;lt; 0.001) and had an early R/S PVC transition [17 (55%) early PT, 3 (10%) late PT) in the case of LVOT-SOO vs 4 (5%) early PT, 55 (65%) late PT in the case of RVOT-SOO, p &amp;lt; 0.001]. The hybrid algorithm accurately anticipated the PVC’s SOO in 101 (88%) patients. A reduction of at least 80% of PVC burden was achieved by 90% of patients at 6 months. Conclusions The hybrid algorithm, prospectively evaluated in an international multicenter study, has proved to permit to accurately anticipate the PVC’s SOO (right vs left) in a consultive population of patients with OT PVCs. Abstract Figure 1

Anticoagulant therapy in patients with atrial fibrillation and an implanted cardiac resynchronization therapy device

Aim. To study the efficacy and safety of direct oral anticoagulant (DOAC) therapy after implantation of cardiac resynchronization therapy (CRT) devices in atrial fibrillation (AF) patients with coronary artery disease (CAD) and dilated cardiomyopathy (DCM).Material and methods. The study included 93 patients followed up from 2014 to 2016 (71 men and 22 women) aged 33-85 years (59,7±10,6) with stable CAD (group 1, n=44) and DCM (group 2, n=49). All patients were diagnosed with AF. The left ventricular ejection fraction (LVEF) was 30,6±3,8%; the left ventricular end-diastolic dimension was 230,9±60,8 mm. All patients received anticoagulants for the prevention of thromboembolic events: a vitamin K antagonist (warfarin) or DOAC. The analysis of medical records, as well as ECG records, echocardiographic, 24-hour ECG monitoring data and information from implanted device was carried out. The follow-up period lasted 24 months.Results. After 24-month follow-up, positive dynamics was noted in all patients — LVEF increased from 30,6±3,7% to 39,5±5,8%). In patients with DCM, a more pronounced increase in myocardial contractile function was noted. Stroke within time interval from 12 to 24 months developed in two patients taking warfarin, from different groups. Transient ischemic attacks were observed in 6 patients: in one patient from group 1 during the period from inclusion and 12-month visit, and in 5 patients from 12 to 24 months. Out of 5 patients, two belonged to group 1 and three — to group 2, while one patient took aspirin and the other 4 — warfarin. One patient from group 1 with persistent AF and vitamin K antagonist therapy had left atrial appendage thrombosis. Hemorrhagic strokes and major bleeding have not been reported.Conclusion. Among patients taking DOAC, regardless of the underlying disease (CAD or DCM) and response to CRT, bleeding events were less often recorded, and there were no thromboembolic events.

Depression Detection Using Comparative Analysis of QRS Detection Algorithms and HRV Of ECG Signal Implemented on MATLAB and Verilog

Nowadays depression is a major health issue with critical psychological wellness, that influencing the world lives. It impacts emotionally as well as physical and physiological individuals Condition. It can also lead to suicide. Ongoing studies say 43% Indians experience depression. Depression can detect using HRV ECG signal, before getting into Severe depression because, HRV has been associated with depression. The QRS detection algorithm is utilized for calculating heart rate. In this project, comparative analysis of QRS detection algorithms is executed in MATLAB and performance compared for analyzing HRV of various MIT-BIH ECG information base acquired from physio.net The QRS detection algorithm technique has been decided for R peak detection and heart rate calculation, proposes a simple method and it doesn’t include complex numerical model. The detection design plans in Verilog to figure heart pulse. Thus, the designed code for analyzing QRS detection algorithms and HRV ECG signal simulated in MATLAB as well as on XILINXS ISE 14.7.

A Study on Smart Healthcare Monitoring Using IoT Based on Blockchain

Background/Motivation. Recently, a lot of interest in health is increasing due to the technology of the 4th industrial revolution. In particular, personal medical information through intelligent self‐diagnosis is emerging as very important. However, such personal medical information causes many problems in security and reliability. Problem/Issues. Personal medical information accidents may occur on the server, but most of all, they occur more often in information sharing and data transmission. Therefore, in this paper, blockchain technology is applied to improve the reliability of such personal information management. Research Objective/Methodology. For intelligent healthcare incorporating blockchain technology, this study utilized the blockchain‐based Internet of Things. In addition, information was accumulated using a number of measurement sensors to analyze individual ECG information. The measured biosignals were monitored for personalized diagnosis by analyzing the fused threshold. Result. In this paper, we implemented a monitoring system using measurement sensors to analyze individual biometric information. The implemented system information has improved reliability and security by incorporating blockchain technology.

Mobile Notification System for Blood Pressure and Heartbeat Anomaly Detection

In today’s fast-paced world where patients may need to be remotely monitored while they are away or out of hospitals, there is a need for mobile applications that can gather the biometric and biomedical signals from any number of devices and sensors, collecting biometric or biomedical data from a patient. This work presents the improvement of a circulatory strain and heartbeat anomaly detection and notice apparatus as an Android application that permits quick discovery of any variations from the norm in a patient's fundamental dependent on the Pan-Tompkins algorithm and reports it to the pertinent emergency clinic or clinical staff. The blood ECG information can be gotten from the health tracker sensors by means of a Bluetooth association and the patient can enter their Blood pressure esteems. In this case, the information gathered from a set of reproduced data, which is identified with a triggering notice from Firebase Cloud function. This notification is further acknowledged by the enrolled specialist (or any clinical faculty or health sector laborer having a similar application). The system's security part is represented by the fine-grained consent procedure which directs that solitary significant authorizations are required for the best possible working of the application which ought to be given to the application. An encryption method using the Blowfish algorithm is included as a feature of the developed mobile Android application to provide secure data transfer of the patient’s vital signals.

Wireless Holter monitoring system with a dual-core processor

The article discusses the principles of building a wireless automated system for heart activity Holter monitoring using a WiFi module based on a dual-core processor. The system is focused on use in a medical cardiology center or at home. The article proposes the method of wireless transmission of an electro-cardio signal based on the paralleling of processing operations and Wi-Fi transmission of an electro-cardio signal. The paper presents algorithms for wavelet compression and filtering of the transmitted electro-cardio signal that ensure high reliability of the transmitted ECG information. A semi-realistic model of the Holter monitoring system has been developed, including real-time packages of the Matlab system, a PhysioBank ATM electro-cardio signal database, a hardware interface based on the PCI-1710HG Board and an external wireless ECG information transmission subsystem. The proposed methods and algorithms will provide high-quality and multi-day wireless transmission of ECG signals, characterized by low power consumption

Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training.

Secondary injuries are common during upper limb rehabilitation training because of uncontrollable physical force and overexciting activities, and long-time training may cause fatigue and reduce the training effect. This study proposes a wearable monitoring device for upper limb rehabilitation by integrating electrocardiogram and electromyogram (ECG/EMG) sensors and using data acquisition boards to obtain accurate signals during robotic glove assisting training. The collected ECG/EMG signals were filtered, amplified, digitized, and then transmitted to a remote receiver (smart phone or laptop) via a low-energy Bluetooth module. A software platform was developed for data analysis to visualize ECG/EMG information, and integrated into the robotic glove control module. In the training progress, various hand activities (i.e., hand closing, forearm pronation, finger flexion, and wrist extension) were monitored by the EMG sensor, and the changes in the physiological status of people (from excited to fatigue) were monitored by the ECG sensor. The functionality and feasibility of the developed physiological monitoring system was demonstrated by the assisting robotic glove with an adaptive strategy for upper limb rehabilitation training improvement. The feasible results provided a novel technique to monitor individual ECG and EMG information holistically and practically, and a technical reference to improve upper limb rehabilitation according to specific treatment conditions and the users’ demands. On the basis of this wearable monitoring system prototype for upper limb rehabilitation, many ECG-/EMG-based mobile healthcare applications could be built avoiding some complicated implementation issues such as sensors management and feature extraction.

A systematic review on focal takotsubo syndrome: a not-so-small matter.

A focal contraction pattern in takotsubo syndrome (TTS) is considered rare. Due to its peculiar presentation, which includes segmental left ventricular (LV) regional wall motion abnormalities (RWMA), the focal TTS pattern may be hardly differentiable from other entities, such as myocarditis or myocardial infarction. We performed a comprehensive systematic literature review researching for works in English published in Journals indexed in Embase, available online for consultation, using the following keywords (in Title and/or Abstract): ("takotsubo" OR "broken heart" OR "apical ballooning" OR "stress cardiomyopathy") AND ("focal" OR "atypical" OR "variant" OR "segments"). Thirty-three papers were retrieved: 17 case reports, 6 case series, and 10 population studies-with a total of 166 focal TTS patients. Prevalence of focal TTS ranged between 0.1% and 14% (pooled mean: 2.8%). Mean age of onset (58years), gender distribution (80% of females), and type of triggers appeared similar to those reported in typical TTS. RWMA more frequently involved the interventricular septum and the anterolateral LV segments, with often preserved LV ejection fraction. In the majority of focal TTS reports that included adequate ECG information (n = 13), abnormalities were localized and not diffuse, always matching RWMA, and in 3 cases, reciprocal changes were observed. No in-hospital nor long-term deaths were reported. The focal TTS contraction pattern may be more prevalent than currently reported. Though possibly presenting with similar demographic background compared with typical TTS, the focal variant might be characterized by peculiar ECG modifications and better prognosis.

Early identification of patients with chest pain at very low risk of acute myocardial infarction using clinical information and ECG only.

BackgroundA considerable proportion of patients with angina‐like symptoms in an emergency department have very low pretest probability for acute myocardial infarction (AMI). Numerous algorithms exist for the exclusion of AMI, usually including laboratory tests. We aimed to investigate whether patients with very low risk can safely be identified by ECG and clinical information without biomarker testing, contributing to saving time and costs.MethodsProspective diagnostic test accuracy study. We included all consecutive patients presenting with angina at the department of emergency medicine of a tertiary care hospital during a 1‐year period. Using clinical information without biomarker testing and ECG, the “Mini‐GRACE score,” based on the well‐established GRACE‐score without using laboratory parameters was calculated. In a cohort design we compared the index test Mini‐GRACE to AMI as reference standard in the final diagnosis using standard measures of diagnostic test accuracy.ResultsWe included 2755 patients (44% female, age 44 ± 17 years). AMI was diagnosed in 103 (4%) patients, among those 44% with STEMI. Overall 2562 patients (93%) had a negative “Mini‐GRACE,” four (0.2%) of these patients had myocardial infarction, and this results in a sensitivity of 96.1% (95% CI 90.4%‐98.9%), specificity 96.5% (95.7%‐97.1%), positive predictive value 51.3% (46.3%‐56.3%) and negative predictive value 99.8% (99.6%‐99.9%). Model performance according to C statistic (0.90) and Brier score (0.0045) was excellent. In rule‐out patients 30‐day mortality was 0.3% and 1‐year mortality was 0.8%.ConclusionsPatients with very low risk of AMI can be identified with high certainty using clinical information without biomarker testing and ECG. Cardiac biomarkers might be avoided in such cases, potentially leading to a significant cost reduction.

Smart monitor of pacemaker patient by using IoT cloud in real time

People severing from heart diseases are increasing day after day, now it's more important to focus on this destructive problem and provide a good health care and remote patient monitoring .An effective monitoring system has been discovered to monitor patients who are implanting a pacemaker and need long-term follow-up. So extensive research has been done to monitor ECG and analyze it by using the free open source website and linking it with portable ECG system that are responsible for the presentation and storage of data to the doctor and patient. In this research we will develop a new method of ECG monitoring for patients who are implanting pacemakers and help them to transfer their ECG signal for long distances depending on the Internet things (IOT). Once the device is worn, it will start collecting ECG information and sent it directly via Wi-Fi to the IOT cloud. Reviewing ECG data through the web has led to the alleviation of the problem of the inability of the doctor to monitor the patient from the home as well as in the event that the doctor is being in another country ,the device also helps to discover the magnetic field that confuses the pacemaker by giving the patient a warning. The results of the test are able to detect that the system would be reliable for displaying real-time ECG data, which could help facilitate patient follow-up after implantation of the pacemaker at home with ease of use and cheaper price.