Feasibility and performance of a device for automatic self-detection of symptomatic acute coronary artery occlusion in outpatients with coronary artery disease : a multicentre observational study

Background Time delay between onset of symptoms and seeking medical attention is a major determinant of mortality and morbidity in patients with acute coronary artery occlusion. Response time might be reduced by reliable self-detection. We aimed to formally assess the proof-of-concept and accuracy of self-detection of acute coronary artery occlusion by patients during daily life situations and during the very early stages of acute coronary artery occlusion. Methods In this multicentre, observational study, we tested the operational feasibility, specificity, and sensitivity of our RELF method, a three-lead detection system with an automatic algorithm built into a mobile handheld device, for detection of acute coronary artery occlusion. Patients were recruited continuously by physician referrals from three Belgian hospitals until the desired sample size was achieved, had been discharged with planned elective percutaneous coronary intervention, and were able to use a smartphone; they were asked to perform random ambulatory selfrecordings for at least 1 week. A similar self-recording was made before percutaneous coronary intervention and at 60 s of balloon occlusion. Patients were clinically followed up until 1 month after discharge. We quantitatively assessed the operational feasibility with an automated dichotomous quality check of self-recordings. Performance was assessed by analysing the receiver operator characteristics of the ST difference vector magnitude. This trial is registered with ClinicalTrials.gov, number NCT02983396. Findings From Nov 18, 2016, to April 25, 2018, we enrolled 64 patients into the study, of whom 59 (92%) were eligible for self-applications. 58 (91%) of 64 (95% CI 81.0-95.6) patients were able to perform ambulatory self-recordings. Of all 5011 self-recordings, 4567 (91%) were automatically classified as successful within 1 min. In 65 balloon occlusions, 63 index tests at 60 s of occlusion in 55 patients were available. The mean specificity of daily life recordings was 0.96 (0.95-0.97). The mean false positive rate during daily life conditions was 4.19% (95% CI 3.29-5.10). The sensitivity for the target conditions was 0.87 (55 of 63; 95% CI 0.77-0.93) for acute coronary artery occlusion, 0.95 (54 of 57; 0.86-0.98) for acute coronary artery occlusion with electrocardiogram (ECG) changes, and 1.00 (35 of 35) for acute coronary artery occlusion with ECG changes and ST-segment elevation myocardial infarction criteria (STEMI). The index test was more sensitive to detect a 60 s balloon occlusion than the STEMI criteria on 12-lead ECG (87% vs 56%; p<0.0001). The proportion of total variation in study estimates due to heterogeneity between patients (I-2) was low (12.6%). The area under the receiver operator characteristics curve was 0.973 (95% CI 0.956-0.990) for acute coronary artery occlusion at different cutoff values of the magnitude of the ST difference vector. No patients died during the study. Interpretation Self-recording with our RELF device is feasible for most patients with coronary artery disease. The sensitivity and specificity for automatic detection of the earliest phase of acute coronary artery occlusion support the concept of our RELF device for patient empowerment to reduce delay and increase Survival without overloading emergency services. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd

Clinical determinants of the PR interval duration in Swiss middle-aged adults: The CoLaus/PsyCoLaus study.

Prolonged PR interval (PRi) is associated with adverse outcomes. However, PRi determinants are poorly known. We aimed to identify the clinical determinants of the PRi duration in the general population. Some clinical data are associated with prolonged PRi. Cross-sectional study conducted between 2014 and 2017. Electrocardiogram-derived PRi duration was categorized into normal or prolonged (&gt;200 ms). Determinants were identified using stepwise logistic regression, and results were expressed as multivariable-adjusted odds ratio (OR) (95% confidence interval). A further analysis was performed adjusting for antiarrhythmic drugs, P-wave contribution to PRi duration, electrolytes (kalemia, calcemia, and magnesemia), and history of cardiovascular disease. Overall, 3655 participants with measurable PRi duration were included (55.6% females; mean age 62 ± 10 years), and 330 (9.0%) had prolonged PRi. Stepwise logistic regression identified male sex (OR 1.41 [1.02-1.97]); aging (65-74 years: OR 2.29 [1.61-3.24], and ≥ 75 years: OR 4.21 [2.81-6.31]); increased height (per 5 cm, OR 1.15 [1.06-1.25]); hypertension (OR 1.37 [1.06-1.77]); and hs troponin T (OR 1.67 [1.15-2.43]) as significantly and positively associated, and high resting heart rate (≥70 beats/min, OR 0.43 [0.29-0.62]) as negatively associated with prolonged PRi. After further adjustment, male sex, aging and increased height remained positively, and high resting heart rate negatively associated with prolonged PRi. Hypertension and hs troponin T were no longer associated. In a sample of the Swiss middle-aged population, male sex, aging and increased height significantly increased the likelihood of a prolonged PRi duration, whereas a high resting heart rate decreased it

Diagnosis of right bundle branch block: a concordance study

Bundle branch block; ConcordanceBloqueig de branca; ConcordançaBloqueo de rama; ConcordanciaBACKGROUND: Right bundle branch block is one of the most common electrocardiographic abnormalities. Most cases of right bundle branch block are detected in asymptomatic patients in primary care, so a correct interpretation of electrocardiograms (ECGs) at this level is necessary. The objective of this research is to determine the degree of concordance in the diagnosis of incomplete and complete right bundle branch block between four primary care researchers and a cardiologist. METHODS: The research design is a retrospective cohort study of patients over 18 years of ages of patients over 18 years of ages who underwent an ECG for any reason and were diagnosed with right bundle branch block by their physician. The physicians participating, 4 primary care researchers and a cardiologist were specialized in interpreting electrocardiographic records. The diagnosis of incomplete and complete right bundle branch block was recorded and other secondary variables were analysed. In case of diagnostic discordance between the researchers, the ECGs were reviewed by an expert cardiologist, who interpreted them, established the diagnosis and analysed the possible causes for the discrepancy. RESULTS: We studied 160 patients diagnosed with right bundle branch block by their general practise. The patients had a mean age of 64.8 years and 54% of them were men. The concordance in the diagnosis of incomplete right bundle branch block showed a Fleiss' kappa index (k) of 0.71 among the five researchers and of 0.85 among only the primary care researchers. The k for complete right bundle branch block was 0.93 among the five researchers and 0.96 among only the primary care researchers. CONCLUSION: The interobserver agreement in the diagnosis of right bundle branch block performed by physicians specialized in ECG interpretation (primary care physicians and a cardiologist) was very good. The variability was greater for the diagnosis of incomplete right bundle branch block

Optimal Multi-Stage Arrhythmia Classification Approach

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F1-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F1-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F1-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources

Automatic detection of early repolarization in ECG signal

Abstract. The early repolarization is one form of heart’s electrical disorder. The scope of this thesis is to develop an algorithm, which detects the marks of the early repolarization from the electrocardiography data. The definition of the early repolarization was fine-tuned in 2015 and the updated definition is used in this thesis. The implementation of the algorithm is done with Matlab. The theory part of this work includes description of the heart’s structure, physiology and review of the most common heart diseases. The heart’s electrical functionality is explained in more detail and the principle of the electrocardiography is viewed, including the main precepts of the analysis of electrocardiography data. The definition of the early repolarization is presented in detail and the significance of this phenomenon is evaluated based on the research data. This work also includes short survey of some of the existing methods for detecting the early repolarization from the electrocardiography data. Thesis includes also the description of the algorithm developed in this work and the analysis of the results. The performance of the algorithm is evaluated with manually classified ECG test set. The sensitivity of the algorithm is 94.0% and the specificity is 92.2%. The correlation to mortality was also studied for few different versions of the algorithm with the Health 2000 data. The correlation to mortality is found with two algorithm versions. The algorithm version with slightly relaxed early repolarization definition shows increased risk for all-cause-mortality in inferior leads, when the slur detection is deactivated. The algorithm version with precise thresholds of the early repolarization definition shows increased risk for all-cause-mortality and for cardiac death in inferior leads, when the slur detection is deactivated.Tiivistelmä. Tämä diplomityö käsittelee sydämen sähköisen toiminnan häiriötilaa, jota kutsutaan aikaiseksi repolarisaatioksi. Työn tavoitteena on kehittää algoritmi havaitsemaan aikaisen repolarisaation merkit sydämen elektrokardiografia mittausdatasta. Tämä työ perustuu vuonna 2015 tarkennettuun aikaisen repolarisaation määritelmään. Työssä kehitetty algoritmi on toteutettu Matlabilla. Diplomityön teoriaosuudessa käydään läpi sydämen rakennetta, fysiologiaa ja yleisimpiä sydänsairauksia. Työssä tutustutaan tarkemmin sydämen sähköiseen toimintaan, elektrokardiografian tuottamaan dataan ja siihen, miten tätä dataa voidaan tulkita. Aikainen repolarisaatio käsitellään omana osionaan, jossa käydään läpi sen tarkka määritelmä, arvioidaan tutkimuksiin pohjautuen ilmiön merkitsevyyttä sekä esitellään muutamia olemassa olevia menetelmiä aikaisen repolarisaation havaitsemiseen elektrokardiografia datasta. Työ sisältää myös kehitetyn algoritmin esittelyn ja tulosten analysointia. Algoritmin suorituskyky todettiin testisetillä, joka sisältää manuaalisesti luokiteltuja elektrokardiografia signaaleita. Algoritmin sensitiivisyys on 94,0% ja spesifisyys 92,2%. Tämän lisäksi ajettiin testejä kuolleisuus korrelaation selvittämiseksi muutamalla algoritmin variaatiolla Terveys 2000 datalle. Korrelaatio kuolleisuuteen löytyi kahdella algoritmivariaatiolla. Algoritmiversio hieman väljennetyillä aikaisen repolarisaation kynnysarvoilla ennustaa kohonnutta riskiä kokonaiskuolleisuuteen inferiorisissa signaaleissa, slur-tunnistuksen ollessa pois käytöstä. Algoritmiversio aikaisen repolarisaation määritelmän mukaisilla tarkoilla kynnysarvoilla ennustaa kohonnutta riskiä sekä kokonaiskuolleisuuteen että sydänperäiseen kuolemaan inferiorisissa signaaleissa, slur-tunnistuksen ollessa pois käytöstä