[en] Abstract: This document is based on the use and development of a non-invasive technique for the classification of cardiac resuscitation rhythms; specifically, its objective is the development of an algorithm based on automatic learning. In recent years, the use of Machine Learning (ML) has been prevailed because of the ability to achieve meaningful conclusions from a set of data. In this case, a classifier based on Supervised learning and known as Random Forest (RF) has been used, taking advantage of the different characteristics offered by the electrocardiogram (ECG) signals. Cardiovascular diseases have grown significantly becoming one of the leading causes of mortality worldwide. In fact, the World Health Organization reports that cardiovascular diseases represent half of all deaths in developed countries and that it is one of the leading causes of death in many developing countries [1]. Therefore, the promotion of research related to this biomedical scientific area is very important. This project allows automatic recording of cardiac resuscitation rhythms to facilitate the necessary study and analysis of ECG signals, with the aim of achieving improvements in the quality of treatments, as well as to pursue the evaluation of therapies on patients that are in the presence of the mentioned cardiovascular arrest situations; in this way, being a support and help for expert doctors, this project offers greater reliability on the correct and appropriate objective diagnoses that are given. Not only that, both in the continuous monitoring of patients and when making critical decisions in an emergency situation, this project offers a great help. To achieve the project's objective, we have worked with a database of 857 patients. Based on a developed graphic interface, the analysis, annotation and segmentation of each of the signals corresponding to each patient was carried out, finally achieving a database of 3803 segments with an average duration of approximately 10-20 seconds. From there, segments of 4 and 8 seconds of ECG signal have been used for processing the algorithm. For each segment, 33 characteristics have been calculated, to then feed the ML algorithm and evaluate it with cross-validation. To evaluate the performance and efficiency of each of the algorithms created based on the different parameters offered by the RF algorithm, the UMS (Unweighted Mean of Sensitivities) and MulAcc (Multiway Accuracy) metrics have been used to offer generic values for multiple classes classifiers. In the performance obtained by the two different durations of signal segments employed, differences have been found differences, achieving more efficient results with the signal segments of 8 seconds in this case. In general, the accuracy and sensitivity average of the classifier have been 96.5% and 88.0%, respectively; our results show, therefore, that the identification and automatic classification of cardiac resuscitation rhythms is possible.

[eu] Laburpena Dokumentu hau berpizteko bihotz erritmoak sailkatzeko teknika ez-inbaditzaileen erabileran eta garapenean oinarrituta dago; zehatzago, ikasketa automatikoan oinarritutako algoritmo baten garapena dugu helburu. Ikasketa automatikoaren erabilera, ingelesez Machine Learning (ML) gisa ezaguna, datu multzo batetik abiatuta ondorio esanguratsuak lortzeko aurkezten duen gaitasuna dela medio, asko nagusitu da azken urteotan. Kasu honetan, seinaleen elektrokardiogramek (EKG) aurkezten dituzten ezaugarri ezberdinez baliatuz, Supervised learning (Ikasketa gainbegiratua) deritzon ikasketan funtsatutako Random Forest (RF) izeneko sailkatzailea erabili da. Bihotz-biriketako gaixotasunak nabarmen hazi dira mundu mailako heriotza-tasa kausarik altuenak izatera ailegatuz. Izatez, Munduko Osasun Erakundearen esanetan, herrialde garatuen heriotzen %50a eta garapen bidean dauden herrialde askoren heriotza kausa nagusietako bat gaixotasun kardiobaskularrak dira [1]. Horrenbestez, biomedikuntza esparru zientifiko honekin loturiko ikerkuntza oro sustatzeak berebiziko garrantzia hartu du. Proiektu honek, egoera hauei aurre egiteko egun existitzen diren terapiek pazienteekiko duten eragina ebaluatzeko bai eta tratamenduen kalitatea hobetzeko ezinbestekoa den EKG seinaleen analisia eta azterketa errazteko berpizte bihotz erritmoen anotatze automatikoa ahalbidetzen du; era horretan, adituak diren medikuentzako euskarri edota laguntza izanik, diagnostiko objektibo zuzenak eta egokiak emateko ziurtasun handiagoa eskaintzen da. Ez hori bakarrik, proiektu honek etengabeko kontrolean egon behar duten pazienteen monitorizaziorako, zein larrialdi egoera baten aurrean hartu beharreko erabaki kritikoak azkarrak eta egokiak izateko ere laguntza handia ematen du. Proiektuaren xedea erdiesteko, 857 pazientez osaturiko datu-base batekin egin da lan. Garaturiko interfaze grafikoan oinarriturik, paziente bakoitzaren seinaleen analisia, anotazioa eta segmentazioa burutu da; eta, azkenik, 10-20 segundo bitarteko 3803 segmentuz osaturiko datu-basea lortu da. Hortik abiaturik, algoritmoaren prozesaketarako 4 eta 8 segundotako EKG seinale segmentuak erabili dira. Segmentu bakoitzeko 33 ezaugarri kalkulatu dira; eta, ondoren, ML algoritmoa elikatu da eta balioztatze gurutzatuarekin ebaluatu da. RF algoritmoak eskuragarri dituen parametro ezberdinen araberako algoritmoak sorturik, hauen errendimendua eta eraginkortasuna ebaluatzeko klase anitzeko sailkatzaileetan balio orokorra eskaintzen duten UMS (Unweighted Mean of Sensitivities) eta MulAcc (Multiway Accuracy) metrikak erabili dira. Seinale segmentu iraupen ezberdinen artean errendimendu aldeak ikusi dira, emaitza hobeak 8 segundotako seinale segmentuekin lortu direlarik. Orokorrean, sailkatzailearen zehaztasun maila %96.5koa izan da eta sentikortasun maila %88.0koa izan da, batez beste; gure emaitzek, beraz, berpizte bihotz erritmoak automatikoki identifikatzea eta sailkatzea posiblea dela baieztatu dute.