Seinale prozesaketan eta ikasketa automatikoan oinarritutako ekarpenak bihotz-erritmoen analisirako bihotz-biriketako berpiztean

Tesis inglés 218 p. -- Tesis euskera 220 p.Out-of-hospital cardiac arrest (OHCA ) is characterized by the sudden loss of the cardiac function, andcauses around 10% of the total mortality in developed countries. Survival from OHCA depends largelyon two factors: early defibrillation and early cardiopulmonary resuscitation (CPR). The electrical shock isdelivered using a shock advice algorithm (SAA) implemented in defibrillators. Unfortunately, CPR mustbe stopped for a reliable SAA analysis because chest compressions introduce artefacts in the ECG. Theseinterruptions in CPR have an adverse effect on OHCA survival. Since the early 1990s, many efforts havebeen made to reliably analyze the rhythm during CPR. Strategies have mainly focused on adaptive filtersto suppress the CPR artefact followed by SAAs of commercial defibrillators. However, these solutionsdid not meet the American Heart Association¿s (AHA) accuracy requirements for shock/no-shockdecisions. A recent approach, which replaces the commercial SAA by machine learning classifiers, hasdemonstrated that a reliable rhythm analysis during CPR is possible. However, defibrillation is not theonly treatment needed during OHCA, and depending on the clinical context a finer rhythm classificationis needed. Indeed, an optimal OHCA scenario would allow the classification of the five cardiac arrestrhythm types that may be present during resuscitation. Unfortunately, multiclass classifiers that allow areliable rhythm analysis during CPR have not yet been demonstrated. On all of these studies artefactsoriginate from manual compressions delivered by rescuers. Mechanical compression devices, such as theLUCAS or the AutoPulse, are increasingly used in resuscitation. Thus, a reliable rhythm analysis duringmechanical CPR is becoming critical. Unfortunately, no AHA compliant algorithms have yet beendemonstrated during mechanical CPR. The focus of this thesis work is to provide new or improvedsolutions for rhythm analysis during CPR, including shock/no-shock decision during manual andmechanical CPR and multiclass classification during manual CPR

ECG Rhythm Analysis during Manual Chest Compressions Using an Artefact Removal Filter and Random Forest Classifiers

Interruptions in cardiopulmonary resuscitation (CPR) decrease the chances of survival. However, CPR must be interrupted for a reliable rhythm analysis because chest compressions (CCs) induce artifacts in the ECG. This paper introduces a double-stage shock advice algorithm (SAA) for a reliable rhythm analysis during manual CCs. The method used two configurations of the recursive least-squares (RLS) filter to remove CC artifacts from the ECG. For each filtered ECG segment over 200 shock/no-shock decision features were computed and fed into a random forest (RF) classifier to select the most discriminative 25 features. The proposed SAA is an ensemble of two RF classifiers which were trained using the 25 features derived from different filter configurations. Then, the average value of class posterior probabilities was used to make a final shock/no-shock decision. The dataset was comprised of 506 shockable and 1697 non-shockable rhythms which were labelled by expert rhythm resuscitation reviewers in artifact-free intervals. Shock/no-shock diagnoses obtained through the proposed double-stage SAA were compared with the rhythm annotations to obtain the Sensitivity (Se), Specificity (Sp) and balanced accuracy (BAC) of the method. The results were 93.5%, 96.5% and 95.0%, respectively.Peer reviewe

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018