Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study.

Background: Acute coronary syndrome (ACS), specifically ST-segment elevation myocardial infarction is a major cause of morbidity and mortality throughout Europe. Diagnosis in the acute setting is mainly based on clinical symptoms and physician's interpretation of an electrocardiogram (ECG), which may be subject to errors. ST-segment elevation is the leading criteria to activate urgent reperfusion therapy, but a clear ST-elevation pattern might not be present in patients with coronary occlusion and ST-segment elevation might be seen in patients with normal coronary arteries.

Artificial intelligence cloud platform improves arrhythmia detection from insertable cardiac monitors to 25 cardiac rhythm patterns through multi-label classification.

Background: Electrocardiogram (ECG) is the gold standard for the diagnosis of cardiac arrhythmias and other heart diseases. Insertable cardiac monitors (ICMs) have been developed to continuously monitor cardiac activity over long periods of time and to detect 4 cardiac patterns (atrial tachyarrhythmias, ventricular tachycardia, bradycardia, and pause). However, interpretation of ECG or ICM subcutaneous ECG (sECG) is time-consuming for clinicians.

Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study.

Background: Insertable cardiac monitors (ICMs) are indicated for long-term monitoring of patients with unexplained syncope or who are at risk for cardiac arrhythmias. The volume of ICM-transmitted information may result in long data review times to identify true and clinically relevant arrhythmias.

Objective: The purpose of this study was to evaluate whether artificial intelligence (AI) may improve ICM detection accuracy.

Rational and design of ST-segment elevation not associated with acute cardiac necrosis (LESTONNAC). A prospective registry for validation of a deep learning system assisted by artificial intelligence.

Background: Patients with chest pain and persistent ST segment elevation (STE) may not have acute coronary occlusions or serum troponin curves suggestive of acute necrosis. Our objective is the validation and cost-effectiveness analysis of a diagnostic model assisted by artificial intelligence (AI).

Methods: Prospective multicenter registry in two groups of patients with STE: I) coronary arteries without significant lesions and without serum troponin curve suggestive of acute necrosis, II) myocardial infarction with acute coronary occlusion.

Personalized monitoring of electrical remodelling during atrial fibrillation progression via remote transmissions from implantable devices.

Aims: Atrial electrical remodelling (AER) is a transitional period associated with the progression and long-term maintenance of atrial fibrillation (AF). We aimed to study the progression of AER in individual patients with implantable devices and AF episodes.

Methods And Results: Observational multicentre study (51 centres) including 4618 patients with implantable cardioverter-defibrillator +/-resynchronization therapy (ICD/CRT-D) and 352 patients (2 centres) with pacemakers (median follow-up: 3.

Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE).

Objective: Ventricular fibrillation (VF)-related sudden cardiac death (SCD) is a leading cause of mortality and morbidity. Current biological and imaging parameters show significant limitations on predicting cerebral performance at hospital admission. The AWAKE study (NCT03248557) is a multicentre observational study to validate a model based on spectral ECG analysis to early predict cerebral performance and survival in resuscitated comatose survivors.

QRS duration reflects underlying changes in conduction velocity during increased intraventricular pressure and heart failure.

Pressure overload and heart failure electrophysiological remodeling (HF-ER) in pigs are associated with decreased conduction velocity (CV) and dispersion of repolarization, which lead to higher risk of ventricular arrhythmia. This work aimed to establish the correlation between QRS complex duration and underlying changes in CV during increased intraventricular pressure (IVP) and/or HF-ER ex-vivo, and to determine whether QRS duration could be sensitive to an acute increase in left ventricular (LV) afterload in-vivo. HF-ER was induced in 7 pigs by high-rate ventricular pacing.

Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning.

Accurate identification of Perinatal Hypoxia from visual inspection of Fetal Heart Rate (FHR) has been shown to have limitations. An automated signal processing method for this purpose needs to deal with time series of different lengths, recording interruptions, and poor quality signal conditions. We propose a new method, robust to those issues, for automated detection of perinatal hypoxia by analyzing the FHR during labor.

Safety threshold of R-wave amplitudes in patients with implantable cardioverter defibrillator.

Objective: A safety threshold for baseline rhythm R-wave amplitudes during follow-up of implantable cardioverter defibrillators (ICD) has not been established. We aimed to analyse the amplitude distribution and undersensing rate during spontaneous episodes of ventricular fibrillation (VF), and define a safety amplitude threshold for baseline R-waves.

Methods: Data were obtained from an observational multicentre registry conducted at 48 centres in Spain.

Symmetrical compression distance for arrhythmia discrimination in cloud-based big-data services.

The current development of cloud computing is completely changing the paradigm of data knowledge extraction in huge databases. An example of this technology in the cardiac arrhythmia field is the SCOOP platform, a national-level scientific cloud-based big data service for implantable cardioverter defibrillators. In this scenario, we here propose a new methodology for automatic classification of intracardiac electrograms (EGMs) in a cloud computing system, designed for minimal signal preprocessing.