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Introduction The National Clinical Programme for Acute Coronary Syndrome in Ireland has successfully improved outcomes for patients presenting with STEMI since its introduction in 2010.1 Appropriate activation of the PPCI protocol is crucial to ensure timely reperfusion for patient with STEMI, while ensuring resources are not over-utilised for patients not requiring urgent PPCI. The aim of this review therefore was to establish the current rate of appropriate activation and false activation for the PPCI service in Galway. Methods Phone records from the dedicated STEMI PPCI telephone number for the Galway PPCI centre were compared to simultaneously transmitted ECGs over a period from 01/01/2019 to 31/03/2019 inclusive (figure 1, table 1). A phone call record corresponding with a transmitted ECG for a new patient was considered activation of the PPCI protocol. These records were compared to the local STEMI database for patients transferred direct to the cardiology team for PPCI. All activations had the corresponding ECG assessed for STEMI criteria as per the ESC guidelines. Results 171 activations of the STEMI PPCI service were recorded from 01/01/2019 to 31/03/2019 inclusive and 71 (42%) were accepted direct to Cardiology for management: 58 patients (34%) were correctly diagnosed with STEMI, 13 patients (7%) were transferred to the direct care of the cardiology team. Five patients (3%) underwent immediate angiography and had no coronary artery disease. Of the 100 patients not transferred directly to cardiology, 61 (36%) patients had ECGs which met ESC ECG criteria2 for STEMI but did not have clinical symptoms consistent with STEMI: LBBB (n=30, 18%), RBBB (n=17, 10%), ST segment elevation (n=8, 5%), paced rhythms (n=6, 4%). 39 patients (23%) had neither ECGs consistent with STEMI nor symptoms requiring urgent transfer for PPCI: Non-specific changes or T-wave inversions (n=30, 18%), normal sinus rhythm (n=7, 5%) and Wellen’s pattern (n=2, 1%). 67 of 100 false activations occurred during on-call hours. The national ambulance service activated the PPCI protocol for 127 cases, of which 39 (31%) cases were correctly diagnosed as STEMI. 52 (39%) patients had ECGs consistent with ESC ECG criteria[2] for STEMI without symptoms and 29 (23%) had neither ECG criteria nor symptoms. In total 44 activations were received from peripheral hospitals, 19 of which were correctly diagnosed as STEMI (43%). 9 (21%) patients had ECG changes consistent with ESC ECG criteria[2] for STEMI without symptoms, and 10 patients(23%) had neither ECG criteria nor symptoms. Conclusion This review identifies important subgroups of patients who were referred for PPCI, in particular 23% of patients who did not require discussion, and 36% of patients who should have discussion with cardiology services before initiating transfer to the catheterisation laboratory.

Introduction Electrode misplacement can adversely affect Electrocardiogram (ECG) morphology and as a consequence, it can affect interpretation and patient diagnosis. Lead misplacement in ECG acquisition can occur in 40% to 60% of the time; which can result. in false positives (Type-I errors) and false negatives (Type-II errors). V1 and V2 electrodes are commonly misplaced high and wide of their correct positions. Vertically misplaced leads V1 and V2 can conceal real STEMI (ST Elevation Myocardial Infarction) events or emulate a false positive. Lead misplacement detection plays an important role to improve ECG data quality and clinical decision making in cardiac care. The aim of the current work was to develop a computer model using deep learning to detect misplacement of leads V1 and V2 for three different types of patients: 1) old Myocardial Infarction (MI) patients, 2) Left Ventricular Hypertrophy (LVH) Patients and 3) normal sinus rhythm patients, and to find in which patient type the ‘general’ patient-type-agnostic computer model achieves the highest performance. Methods 12-lead ECGs for 453 subjects, (normal n=151, LVH n=151, MI n=151) were extracted from body surface potential maps. V1 and V2 leads acquired from the second intercostal space (ICS) are considered as incorrect placement and leads from the fourth (IC) are considered as correct. The computer model was developed using deep learning algorithm (bidirectional long short-term memory network (BLSTM)) to detect V1 and V2 misplacement. The computer model was trained on three datasets together and tested on each dataset separately. In each dataset, data were divided into 67% for training and 33% for testing. The prevalence was 50% for correct and incorrect leads. Results Computer model accuracy using BLSTM of V1 and V2 misplacement detection was 93.0% in normal dataset, 93.0% in MI dataset while the accuracy was 91.0% in LVH dataset. Conclusion Computer model got a high performance to detect V1 and V2 misplacement in normal and MI ECGs datasets, while the performance decreased in LVH dataset to 91.0%. In future we need to consider MI infarct sites such as anterior, inferior etc to increase accuracy.

Atrial fibrillation (AF) is the most common form of arrhythmia with accelerated and irregular heart rate (HR), leading to both heart failure and stroke and being responsible for an increase in cardiovascular morbidity and mortality. In spite of its importance, the direct effects of AF on the arterial hemodynamic patterns are not completely known to date. Based on a multiscale modelling approach, the proposed work investigates the effects of AF on the local arterial fluid dynamics. AF and normal sinus rhythm (NSR) conditions are simulated extracting 2000 $\mathrm{RR}$ heartbeats and comparing the most relevant cardiac and vascular parameters at the same HR (75 bpm). Present outcomes evidence that the arterial system is not able to completely absorb the AF-induced variability, which can be even amplified towards the peripheral circulation. AF is also able to locally alter the wave dynamics, by modifying the interplay between forward and backward signals. The sole heart rhythm variation (i.e., from NSR to AF) promotes an alteration of the regular dynamics at the arterial level which, in terms of pressure and peripheral perfusion, suggests a modification of the physiological phenomena ruled by periodicity (e.g., regular organ perfusion)and a possible vascular dysfunction due to the prolonged exposure to irregular and extreme values. The present study represents a first modeling approach to characterize the variability of arterial hemodynamics in presence of AF, which surely deserves further clinical investigation., Comment: 17 pages, 8 figures

Differences in successive R-R intervals (RRIs) were normalized by RRIs before and after the indexing beats (normalized DRs) in individuals with normal sinus rhythm (NSR) and 98.89% of normalized DRs were found to distribute within mean ± 0.100 (≒mean ± 3SD), whereas 73.47% were out of this range in atrial fibrillation (AF). When 7 out 20 normalized DRs fell outside of 0.000 ± 0.100, NSR (n = 129) and AF (n = 108) could be discriminated with high sensitivity, specificity, and predictive values (>99.0% for all). This method will be used in detecting AF candidates from a small number of heart beats or arterial pulses.

Acute inferior myocardial infarction (MI) is often associated with heart blocks as acute occlusion of the right coronary artery (RCA) compromises flow to sinoatrial nodal and atrioventricular nodal branches that arise from the proximal and distal parts of RCA, respectively. This phenomenon can happen in up to 20% of the cases, is usually transient and rarely requires pacemaker implantation. Still, when present, it raises alarms and poses significant monitoring and management challenges to the physicians. We report a similar case where the patient presented with inferior MI complicated by complete heart block. The patient went through varying degrees of advanced heart blocks before finally reverting to normal sinus rhythm. Subsequent coronary angiogram showed total thrombotic occlusion of proximal RCA. To the best of our knowledge, such a case has not been described before in literature where a complete array of heart blocks is reported in the context of acute MI. This case illustrates the importance of recognizing advanced heart blocks in acute MI. Although usually transient, these arrhythmias need careful monitoring during the initial days after MI.

Background: Even though left atrial (LA) size and function are intimately related to left ventricular (LV) diastolic dysfunction, the role of LA with regard to LV systolic function is less clear. Consequently, we examined the potential association that might exist between measures of longitudinal LV systolic shortening and LA dilation using LA volume index (LAVI). Methods: In this retrospective analysis, data from 75 echocardiograms (mean age 53 ± 14; range 24 - 89 years; mean body surface area (BSA) 2.0 ± 0.3) were analyzed. Results: Peak global longitudinal (PGLS) correlated best with LV mass index (LVMI) followed by mitral annular systolic excursion (MAPSE), and age. Similar results were obtained when analyzing the best variables that correlated with LAVI. Finally, MAPSE correlated best with PGLS, then with MA tissue Doppler systolic velocity, BSA, and LAVI in that order. All patients had normal LV ejection fraction (LVEF) and normal sinus rhythm when studied. Conclusions: LAVI does not directly affect LV systolic function and longitudinal measures of LV shortening are mainly dependent on LV mass. Additional studies are now required to determine how these associations vary when different degrees of LV dilatation and systolic dysfunction are included in the analysis. Cardiol Res. 2018;9(1):1-6 doi: https://doi.org/10.14740/cr637w Â

Arrhythmias are abnormal heart rhythms that can be life-threatening. Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Supraventricular Tachycardia (SVT), Sinus Tachycardia (ST), and Sinus Bradycardia (SB) are common arrhythmias that affect a growing number of patients. In this paper we describe a method to detect these arrhythmias in RR interval signals. We propose a deep learning algorithm to discriminate these fife arrhythmias and Normal Sinus Rhythm (NSR). The deep learning model was trained and tested with data from 10093 subjects. We used 10-fold cross-validation to establish the performance results. The overall accuracy for the six-class problem was 98.37%. When considering the binary problem of arrhythmia versus NSR, where the arrhythmia group is formed by combining the data from all fife arrythmias, the performance results are: Accuracy (ACC) = 98.55%, Sensitivity (SEN) = 99.40%, Specificity (SPE) = 94.30%. These results indicate that it is possible to discriminate RR interval sequences from SVT, ST, SB, AFIB, AFL, and NSR subjects with minimal error. Furthermore, the proposed model can provide a robust and independent second opinion when it comes to a decision if arrhythmia is present or not. Another positive aspect of the proposed arrhythmia detection algorithm is economic viability. RR interval signals are cost-effective to measure, communicate, and process. The discriminate powers of the proposed algorithm together with the advent of wearable technology and m-health infrastructure might lead to pervasive long-term arrhythmia monitoring. The detection results can support early diagnosis which helps to reduce the burden of the disease.

Background: Ventricular fibrillation and atrial fibrillation are well-known arrhythmias in patients with Brugada syndrome. This study evaluated the characteristics of the atrial arrhythmogenic substrate using the signal-averaged electrogram (SAECG) in patients with Brugada syndrome. Methods: SAECGs were performed during normal sinus rhythm in 23 normal volunteers (control group), 21 patients with paroxysmal atrial fibrillation (PAF; PAF group), and 21 with Brugada syndrome (Brugada group). Results: The filtered P wave duration (fPd) in the control, Brugada, and PAF groups was 113.9 ± 12.9 ms, 125.3 ± 15.0 ms, and 137.1 ± 16.3 ms, respectively. The fPd in the PAF group was significantly longer compared to that in the control and Brugada groups (p

Electromechanical wave imaging (EWI) is an ultrasound-based technique that can non-invasively map the transmural electromechanical activation in all four cardiac chambers in vivo. The objective of this study was to determine the reproducibility and angle independence of EWI for the assessment of electromechanical activation during normal sinus rhythm (NSR) in healthy humans. Acquisitions were performed transthoracically at 2000 frames/s on seven healthy human hearts in parasternal long-axis, apical four- and two-chamber views. EWI data was collected twice successively in each view in all subjects, while four successive acquisitions were obtained in one case. Activation maps were generated and compared (i) within the same acquisition across consecutive cardiac cycles; (ii) within same view across successive acquisitions; and (iii) within equivalent left-ventricular regions across different views. EWI was capable of characterizing electromechanical activation during NSR and of reliably obtaining similar patterns of activation. For consecutive heart cycles, the average 2-D correlation coefficient between the two isochrones across the seven subjects was 0.9893, with a mean average activation time fluctuation in LV wall segments across acquisitions of 6.19%. A mean activation time variability of 12% was obtained across different views with a measurement bias of only 3.2 ms. These findings indicate that EWI can map the electromechanical activation during NSR in human hearts in transthoracic echocardiography in vivo and results in reproducible and angle-independent activation maps.

SOURCE CITATION Stiell IG, Sivilotti MLA, Taljaard M, et al. A randomized, controlled comparison of electrical versus pharmacological cardioversion for emergency department patients with acute atrial flutter. CJEM. 2021;23:314-24. 33959925.

Objective This paper presents the performance of an atrial fibrillation (AF) detection algorithm in a lead-I mobile ECG setting. Our aim is to demonstrate that false positive cases occurring due to non-AF arrhythmias can be significantly reduced by taking atrial activity into account in addition to heart rhythm. Methods AF detection was carried out in two ways: only by heart rhythm assessment based on Poincare plot of RR intervals (A1), and by also performing P wave analysis on the average majority cycle (A2). Results The algorithm was tested on the PhysioNet MIT-BIH Atrial Fibrillation Database, Long-Term AF Database, MIT-BIH Arrhythmia Database, and MIT-BIH Normal Sinus Rhythm Database. A1 produced an average sensitivity (Se) of 97.64 % and specificity (Sp) of 93.06 %. A2 resulted Se = 96.52 % and Sp = 99.12 %. Additionally, clinical data acquisition was performed by the Sanatmetal WIWE mobile ECG system. On the latter records, Se = 100 % and Sp = 95.31 % were obtained with A1, while A2 resulted Se = 100 % and Sp = 100 %. Conclusion Atrial activity analysis significantly increased Sp in records with frequent premature beats (by almost 12 %) and marked sinus arrhythmia (by 7.5 %). Based on our results and data from the literature, we suggest that AF detection methods should be tested more carefully on non-AF arrhythmia cases. Applied in WIWE, the A2 version of our algorithm outperforms the AF detectors of current single-channel mobile ECG systems. Significance The results contribute to the significant reduction of false positive AF detections in lead-I mobile ECG technology.

Introduction Improvement in preservation and transportation conditions has the potential to enhance organ quality at the time of transplant, optimize patient outcomes, and prolong the acceptable maximum allograft ischemic time. Herein, we describe our initial successful experience with the static hypothermic preservation system (Paragonix) for a donor heart. Our storage period and allograft ischemic time are the longest reported in the literature. Case Report A 68-year-old woman with HF was listed for transplantation. A donor heart was procured from a hospital located 3.5 hours away. The donor allograft was secured and packaged using the SherpaPak™ device (Fig. 1); total storage period was 283 mins. Heart transplantation was performed in standard fashion. AV conduction and normal sinus rhythm were regained after 7 mins of reperfusion. Total ischemic time was 330 mins, including a warm ischemic time of 20 minutes. The recipient CPB time and aortic cross clamp time were 153 and 50 mins, respectively. The patient recovered well and was discharged on postoperative day 10. At three months post-transplant, the patient continues to have excellent graft function without any evidence of rejection. Summary Historically, Shumway et al. initially obtained donor hearts from operating rooms immediately adjacent to a recipient. Over time, donor distance and allograft ischemic times have increased with advances in cardiac preservation. However, long cold ischemic times may induce PGD, cardiac allograft vasculopathy, and contribute to increased length of stay. Maintaining storage temperatures within the narrow range of 4-8°C is optimal for the preservation of high energy phosphate stores and avoids the risk of cold ischemic injury at temperatures below 2°C. By expanding geographic boundaries and total ischemic times, we believe that regulated static hypothermic preservation is a useful addition to a growing armamentarium of devices and strategies to increase access to transplantation.

The heart rate variability (HRV) is an important physiological signal used either to assess the risk of cardiac death or to model the cardiovascular regulatory dynamics. Asymmetries in HRV data have been observed using 2D Poincare plots, which have been linked to a non-equilibrium operation of the cardiac autonomic system. This work further explores the presence of asymmetries but in the serial correlations of the dynamics of HRV data. To this end, detrended fluctuation analysis (DFA) was used to estimate the Hurst exponent both when the heart rate is accelerating and when it is decelerating. The analysis is conducted using data collected from subjects under normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation (AF) . For the NSR cases, it was found that correlations are stronger ( p 0 . 05 ) when the heart rate is accelerating than when it is decelerating over different scales in the range 20–40 beats. In contrast, the opposite behavior was detected for the CHF and AF patients. Possible links between asymmetric correlations in the dynamics and the mechanisms controlling the operation of the heart rate are discussed, as well as their implications for modeling the cardiovascular regulatory dynamics.

Atrial fibrillation (AF) monitoring and diagnosis require automatic AF detection methods. In this paper, a novel image-based AF detection method was proposed. The map was constructed by plotting changes of RR intervals (△RR) into grid panes. First, the map was divided into grid panes with 20 ms fixed resolution in y-axes and 15-60 s step length in x-axes. Next, the blank pane ratio (BPR), the entropy and the probability density distribution were processed using linear support-vector machine (LSVM) to classify AF and non-AF episodes. The performance was evaluated based on four public physiological databases. The Cohen's Kappa coefficients were 0.87, 0.91 and 0.64 at 50 s step length for the long-term AF database, the MIT-BIH AF database and the MIT-BIH arrhythmia database, respectively. Best results were achieved as follows: (1) an accuracy of 93.7%, a sensitivity of 95.1%, a specificity of 92.0% and a positive predictive value (PPV) of 93.5% were obtained for the long-term AF database at 60 s step length. (2) An accuracy of 95.9%, a sensitivity of 95.3%, a specificity of 96.3% and a PPV of 94.1% were obtained for the MIT-BIH AF database at 40 s step length. (3) An accuracy of 90.6%, a sensitivity of 94.5%, a specificity of 90.0% and a PPV of 55.0% were achieved for the MIT-BIH arrhythmia database at 60 s step length. (4) Both accuracy and specificity were 96.0% for the MIT-BIH normal sinus rhythm database at 40 s step length. In conclusion, the intuitive grid map of delta RR intervals offers a new approach to achieving comparable performance with previously published AF detection methods.

Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n =23), the MIT-BIH Normal Sinus Rhythm (n =18), the BIDMC Congestive Heart Failure (n =13) and the Congestive Heart Failure RRI databases (n =25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n =42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.

In a case of calcific aortic stenosis with complete heart block, normal sinus rhythm returned following aortic valve replacement. A fifty nine years old with calcific aortic stenosis and complete heart block was completely cured by AVR only. From that we suggested that complete heart block in association with calcific aortic stenosis may be reversible and should be considered as early indication for aortic valve surgery.

The rare and potentially fatal complication of asystole during direct laryngoscopy is linked to direct vagal stimulation. This case describes asystole in an 85-year-old female who underwent suspension microlaryngoscopy with tracheal dilation for subglottic stenosis. Quick recognition of this rare event with immediate cessation of laryngoscopy resulted in the return of normal sinus rhythm. This incident emphasizes the implications of continued vigilance during laryngoscopy and the importance of communication between the anesthesia and surgical staff to identify and treat this rare complication. The case was successfully concluded by premedication with an anticholinergic and by increasing the depth of anesthesia.

Characterization and mapping of arrhythmias is currently performed through invasive insertion and manipulation of cardiac catheters. Electromechanical wave imaging (EWI) is a non-invasive ultrasound-based imaging technique, which tracks the electromechanical activation that immediately follows electrical activation. Electrical and electromechanical activations were previously found to be linearly correlated in the left ventricle, but the relationship has not yet been investigated in the three other chambers of the heart. The objective of this study was to investigate the relationship between electrical and electromechanical activations and validate EWI in all four chambers of the heart with conventional 3D electroanatomical mapping. Six (n = 6) normal adult canines were used in this study. The electrical activation sequence was mapped in all four chambers of the heart, both endocardially and epicardially using the St Jude's EnSite 3D mapping system (St. Jude Medical, Secaucus, NJ). EWI acquisitions were performed in all four chambers during normal sinus rhythm, and during pacing in the left ventricle. Isochrones of the electromechanical activation were generated from standard echocardiographic imaging views. Electrical and electromechanical activation maps were co-registered and compared, and electrical and electromechanical activation times were plotted against each other and linear regression was performed for each pair of activation maps. Electromechanical and electrical activations were found to be directly correlated with slopes of the correlation ranging from 0.77 to 1.83, electromechanical delays between 9 and 58 ms and R 2 values from 0.71 to 0.92. The linear correlation between electrical and electromechanical activations and the agreement between the activation maps indicate that the electromechanical activation follows the pattern of propagation of the electrical activation. This suggests that EWI may be used as a novel non-invasive method to accurately characterize and localize sources of arrhythmias.

Grading severity of AS in AF is complicated by varying stroke volumes associated with fluctuating maximum velocities and pressure gradients across the aortic valve. Current guidelines recommend averaging five continuous-wave peak velocity and mean gradient (MG) Doppler signals across the aortic valve when estimating severity of AS in AF. However, it is unknown when grading severity of AS how the average of multiple Doppler signals vs the highest Doppler signal in AF compares to the Doppler signals when the patient is in normal sinus rhythm. We present a series of patients with AS who had two echocardiograms performed within 2-4 months of each other, one when in normal sinus rhythm and one when in AF, and compare the aortic valve hemodynamics associated with the two rhythms.

Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders with increasing prevalence. Mechanisms sustaining these arrhythmias are different, and subsequently, the required treatments options differ. Although many algorithms have been developed for differentiating fibrillation from normal sinus rhythm, very few methods exist to differentiate between different forms of AF and VF from surface electrocardiogram (ECG). To address the issue, we propose a novel ECG classification method to differentiate fibrillation that is completely chaotic from forms where it is organized with key driving sites. Differentiating fibrillation organisation from ECGs may aid patient selection, and identify those who may benefit from targeted ablation treatment. Evaluation using real-world data sets based on rat VF model shows that the proposed method could recognise the correct Fibrillation subtype from the single-channel electrocardiogram with an accuracy of 88.89\(\%\).

Sample Entropy (SampEn) is a popular method for assessing the regularity of physiological signals. Prior to the entropy calculation, certain common parameters need to be initialized: Embedding dimension m, tolerance threshold r and time series length N. Nevertheless, the determination of these parameters is usually based on expert experience. Improper assignments of these parameters tend to bring invalid values, inconsistency and low statistical significance in entropy calculation. In this study, we proposed a new tolerance threshold with physical meaning ( r p ), which was based on the sampling resolution of physiological signals. Statistical significance, percentage of invalid entropy values and ROC curve were used to evaluate the proposed r p against the traditional threshold ( r t ). Normal sinus rhythm (NSR), congestive heart failure (CHF) as well as atrial fibrillation (AF) RR interval recordings from Physionet were used as the test data. The results demonstrated that the proposed r p had better stability than r t , hence more adaptive to detect cardiovascular diseases of CHF and AF.

We present a case of electrical cardioversion used to treat a hemodynamically unstable wide complex tachycardia (WCT). The patient returned to normal sinus rhythm after being cardioverted with 100 joules (J) on the first attempt. He was admitted to the hospital for cardiac evaluation and ultimately discharged home on flecainide and nebivolol after a negative cardiac workup.

A man without cardiac symptoms was found to have a slow irregular pulse, and an electrocardiogram revealed sinus bradycardia with escape-capture bigeminy. He was taking verapamil, clonidine, and hydralazine for hypertension. The verapamil was discontinued; he returned to normal sinus rhythm and was discharged on the second hospital day.

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the convolution layers to classify the electrocardiogram (ECG) beats into a normal sinus rhythm, premature ventricular contraction, atrial premature contraction, and right/left bundle branch block arrhythmia. Based on testing MIT-BIH arrhythmia benchmark databases, the scope of training/test ECG data was configured by covering at least three and seven R-peak features, and the proposed extended-GoogLeNet architecture can classify five distinct heartbeats; normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), and left bundle brunch block(LBBB), with an accuracy of 95.94%, an error rate of 4.06%, a maximum sensitivity of 96.9%, and a maximum positive predictive value of 95.7% for judging a normal or an abnormal beat with considering three ECG segments; an accuracy of 98.31%, a sensitivity of 88.75%, a specificity of 99.4%, and a positive predictive value of 94.4% for classifying APC from NSR, PVC, APC beats, whereas the error rate for misclassifying APC beat was relative low at 6.32%, compared with previous research efforts.

Trained cardiologists detect atrial fibrillation by visual interpretation of certain segments of electrocardiogram (EKG) lines known as the QRS complex. Similarly, available EKG-software also evaluates anomalies in the signals from the EKG-leads that produce the traces/lines in order to flag for atrial fibrillation. We and others have previously shown that machine learning methods successfully identify patients with paroxysmal atrial fibrillation (PAF) based on their EKG during normal sinus rhythm. In this work, we go beyond the earlier black-box approaches and identify specific patterns in the QRS complex of a normal sinus rhythm that are associated with atrial fibrillation. We implemented frequent pattern mining on discretized waveform raw EKG data to determine patterns that are specific to patients with PAF based on 1-minute Lead 1 EKG recordings sampled at 128 Hz from 25 patients with PAF and 50 healthy subjects from a Physionet data repository. We discretized the down-sampled (16 Hz) EKG traces with seven symbols corresponding to various degrees of local variability within the traces and selected from the existing unique 1,306 4-symbol patterns the 850 patterns occurring at least 5 times (to mitigate sparsity related problems). The resulting 75x850 pattern frequency matrix represented the frequency of each pattern within each of the 75 patients and PAF subjects were distinguished based on a binary Least Absolute Shrinkage and Selection Operator regression with 5-fold cross validation that selected 50 of the patterns (AUC = 0.95; 95% C.I. 0.88-1.00; 94% specificity, 88% sensitivity). These 50 patterns are candidates to elicit the “fingerprint” of PAF within normal sinus rhythm: e.g., one of the selected patterns in Figure 1 was observed in 76% of PAF patients while it was present only in 30% of healthy patients. Our study is a proof of concept that machine learning and artificial intelligence techniques are not restricted to black-box approaches and can be used to derive interpretable insights that could lead to novel biomarkers associated with certain health conditions.

BackgroundAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG). ObjectiveWe investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG. MethodsA total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. ResultsIn all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99). ConclusionsThe algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population. Trial RegistrationChinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6)

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients&rsquo, health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

A 62-year-old man was admitted to the emergency department (ED) with the complaint of intense hematemesis. He was admitted to intensive care unit because of acute esophageal variceal hemorrhage. He underwent sclerotherapy followed by a slow infusion of intravenous octreotide. Complete heart block occured in the patient during octreotide infusion and infusion was stopped. Temporary pacemaker was placed in the patient's heart. Normal sinus rhythm was observed in the follow up one day later and the pacemaker was removed from patient. He was discharged upon recommendation.

High-frequency muscle tremor can mimic atrial flutter. Unnecessary therapies and aggressive interventions may have devastating consequences in these cases. We present a case of a patient with global pseudo-atrial flutter waves in the setting of essential tremor strictly confined to the arms. Two-dimensional transthoracic echocardiography (2D TTE) was used to discriminate normal sinus rhythm from pseudo-atrial flutter waves.

The increasing disease burden caused by atrial fibrillation has led to new interventions aimed at restoring normal sinus rhythm and preventing embolic complications. Cardiac CT can help identify suitable candidates, plan the intervention, and diagnose complications. In some institutions, this application has become the most common indication for the clinical use of cardiac CT. Consequently, expertise in making the pertinent observations outlined in this chapter is of critical importance for the success of any cardiac CT program and offers a tremendous opportunity for interdisciplinary collaboration.

Heart attacks had been for many years one of the primary public health issues. According to the World Health Organization, ischemic heart diseases are in the top of ten leading causes of death. Thus, automatic detection of abnormal heart conditions may provide a necessary hospitalization, for patients suffering from heart diseases making it possible to save their lives. In the current article, we present a simple but efficient deep learning algorithm based on simple convolutional units and time frequency presentations. The proposed model can classify three types of Electrocardiogram (ECG) signals related to three different cases; namely patients with Congestive Heart Failure, patients with Arrhythmia and others with a normal heart beats. The 162 recordings used in this algorithm was taken from Physio Net databases; 96 of them taken from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, 36 from the MIT-BIH normal sinus rhythm database and 30 from the Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. The simplicity and flexibility of the proposed approach would present a real potential for real time monitoring with 93.75% accuracy.

The weighted integrated multi-scale entropy(WIMSE) is analyzed for the 135 about ten minutes RR interval series preceding the onset of ventricular tachycardia and ventricular fibrillation(called VT/VF series),and the change of WIMSE is discussed for the data samples of significant increase of heart rate (called SI_HR group) and no significant change of heart rate (called nSI_HR group) preceding the onset of VT/VF events. Results show that the WIMSE of VT/VF series has significantly reduction compared with normal sinus rhythm (scale:1--30, p

In this paper, we propose to apply spatio-temporal filtering to atrial activity enhancement, prior to the detection of possible atrial arrhythmias. During normal sinus rhythm, the atrial activity is well synchronized with the ventricular one. The distances between ventricular QRS complexes and the preceding atrial P waves are approximately constant. However, during atrial arrhythmias such a synchronization does not exist. Although both atrial fibrillation (AF) and atrial flutter (AFL) are also characterized by irregularity of RR intervals, nevertheless it is this lack of atrioventricular synchronization and the associated irregularity of atrial activity (AA) that is the most straightforward symptom of atrial arrhythmias. In AFL episodes, the atrial activity tends to be more regular, whereas in AF it is almost completely unpredictable. Our objective is to enhance this activity to facilitate discrimination between the two arrhythmias. Spatio-temporal filtering (STF) was developed for detection of fetal QRS complexes in an ECG signal recorded from the abdomen of a pregnant woman. The filter can easily be applied to enhance the P waves in regular ECG signals. In this paper, however, we modify the learning phase of STF, to make it useful also for enhancement of abnormal atrial activity. The STF ability to enhance the atrial flutter waves is presented. An algorithm is proposed that allows for simple but effective discrimination between the two types of atrial irregular activity: AFL and AF. Tested on a database containing the cases of both atrial arrhythmias, the algorithm allows for their almost faultless recognition. © 2019 Association for Computing Machinery. Ministry of Science and Higher Education of the Russian Federation: BK-RAu-3/2018 European Social Fund, ESF: POWR.03.02.00-00-I029 European Commission, EU International Academy of Computing Technology (IACT) This work was partially supported by the Ministry of Science and Higher Education Funding (BK-RAu-3/2018) and co-financed by the European Union through the European Social Fund (grant POWR.03.02.00-00-I029).

A 65-year-old diabetic and hypertensive woman was admitted for evaluation of syncope. Her baseline electrocardiogram revealed normal sinus rhythm with left anterior hemiblock. Her treadmill test, holter monitoring and external loop recorder examinations were normal. Right sided carotid sinus massage (CSM) revealed 2:1 atrioventricular block (AV) (Mobitz II type) while left sided CSM had no effect. Permanent pacemaker implantation was performed (DDIR, St Jude Medical, USA). Mobitz II type AV block due to CSM reflects enhanced vagal efferent tone on AV node and his bundle fibre.

The goal of pulmonary vein ablation for atrial fibrillation (AF) is returning to normal sinus rhythm; nevertheless it success is limited in part by uncertainly in the mechanisms that sustain AF. 43 AF patients were submitted to an ablation procedure and monitored after ablation. Dominant frequency from intra-atrial recordings obtained before the intervention were analysed in order to predict AF recurrence. The novelty of this study is that dominant frequency was calculated from reconstructed signal by singular spectrum analysis (SSA) application. Patients that maintained sinus rhythm and patients with recurrence in AF showed differences in left atrium dominant frequencies, with higher values in the recurrent AF group than in group without recurrence in the arrhythmia (p=0.02). Moreover, differences between both atria were found in the non-recurrent group, with 5.76 ± 1.31 Hz in the left atrium vs. 6.25±1.23 Hz in the right atrium (p=0.03). These findings show the potential of SSA as a preprocessing method and results are congruent with previous studies.

Atrial fibrillation (AF) is characterised by disorganised atrial electrical activity and contraction. The complications of atrial fibrillation is varied, so it shows several modalities in the Electrocardiogram (ECG). According to our statistics, the beat features and duration time of AF are different from person to person. The distribution of available annotated source ECG date is not as the target. Due to the rapid development of portable monitor, the ECG data explodes. The existing algorithms for the detection of AF perform well only in the training database. Training a general used model requires a great deal of labeled data for every user, this is a huge amount of work that is almost impossible. In order to make full use of the limited labeled ECG data and training a general model, we propose an adaptive latent space domain transfer method for the detection of AF. The model learned from the source data is automatically adapted with little annotated or none in the target data. The MIT-BIH Atrial Fibrillation Database is regard as standard reference for classifier. Then we carry the experimental verification on the MIT-BIH Normal Sinus Rhythm Database and the Long-Term AF Database. The transfer method shows better performance than directly applied. It does make sense for detection and analysis of clinical dynamic electrocardiogram and individual ECG monitoring.

The aim of this proposed study is to investigate the discriminant power of Hjorth Descriptor in classification of three categorized groups of subjects’ ECG measurement, which are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF) and Congestive Heart Failure (CHF). This feature has been previously employed to measure the healthiness in persons via their ECG recordings. The algorithm was designed and implemented to extract the Hjorth features and evaluate the performance of classification made on those features by comparing all classifications made among those three databases. Each categorized group included thirty subjects evenly and only three complete QRS complexes of each record in our databases were selected, segmented and extracted for their Hjorth descriptor estimators. In this work three different classifiers were selected, which are Least-Squares (LS), Maximum likelihood (ML) and Support Vector Machine (SVM) for performance evaluation and accuracy comparison. The experimental results from our study showed that the most effective classifier was found to be ML with a mean accuracy of 84.89%, SE of 88.82% and SP of 99.75%, as compared to LS which was found to be the second effective classifier with 88.22% accuracy, and finally SVM with 76.94%. These findings suggested that the promisingly dominant ECG based Hjorth descriptor is capable of class separation among cardiac arrhythmia patient groups.

Refractory ventricular fibrillation is a rare condition seen in both in-hospital and out-of-hospital cardiac arrest. A 56-year-old male was identified to have refractory ventricular fibrillation after an in-hospital cardiac arrest with multiple unsuccessful standard defibrillation attempts that was converted with dual-sequential defibrillation (DSD) to normal sinus rhythm. Advanced cardiac life support (ACLS) is the most widely used algorithmic treatment approach for various cardiopulmonary emergencies but has yet to provide recommendations for the treatment of refractory ventricular fibrillation. DSD may be a viable treatment strategy for refractory ventricular fibrillation when ACLS recommendations are ineffective.

Atrial fibrillation (AF) is the most common chronic arrhythmia affecting nearly nine million people in the European Union only. Ischemic stroke is the most feared condition caused by AF with a 4.8 times higher risk of occurrence among AF patients compared to rest of population. The asymptomatic nature of AF causes many people to be left untreated. Previous studies have shown that detecting AF using mechanocardiography, e.g. recording mechanical movement caused by cardiac activity with motion sensors, is possible. We propose a custom Bluetooth enabled wearable device for measuring mechanocardiographic signals. On-board real-time signal processing is applied in order to determine whether the patient is suffering from AF. Preclinical measurement results suggest that the algorithm is able to distinguish between AF and normal sinus rhythm.

Guidelines for continuous cardiac monitoring (CCM) have focused almost exclusively on cardiac diagnoses, thus limiting their application to a general medical population. In this study, a retrospective chart review was performed to identify the reasons that general medical patients, cared for on hospitalist-led inpatient teaching teams between April 2017 and February 2018, were initiated and maintained on CCM, and to determine the incidence of clinically significant arrhythmias in this patient population. The three most common reasons for telemetry initiation were sepsis (24%), arrhythmias (12%), and hypoxia (10%). Most patients remained on telemetry for more than 48 hours (62%) and a significant number of patients were on telemetry until they were discharged from the hospital (39%). Of the cumulative total of more than 20,573 hours of CCM provided to this patient population, 37% of patients demonstrated only normal sinus rhythm and 3% had a clinically significant arrhythmia that affected management.