[en] Sudden death due to cardiac arrest (SCA) is the main cause of death worldwide and according to some studies, due to the global increase in cardiovascular diseases, the probability of suffering SCA will increase. Although there are progresses in recovery techniques, the probability of surviving a cardiac arrest is low. Nowadays, the probability of suffering from SCA outside the hospital is high, having a lower probability of survival than if it occurred inside the hospital (<% 10). In cardiorespiratory stops it is essential to have an early detection and the knowledge of the different stages of the protocols to follow for their recovery. In a SCA, the basic clinical information provided by the electrocardiogram (ECG) is essential; therefore, it is important to develop specific and solid methods of automatic identification of the ECG for several applications: rhythm diagnosis, rhythm detection, thermodynamically characterization of the patients, ... Therefore, the detection of QRS waves is necessary because that contain the energy of the heartbeat; however, it must be taken into account that there is no regulation of the start and end of the waves on the ECG. In a SCA, very irregular and aberrant rhythms arise; above all, the so-called PEA rhythms (Pulseless electrical activity), with a very unique and versatile QRS. Therefore, the main objective of the project is the proposal of an algorithm capable of segmenting the QRS in a short time and organized rhythm ECG, which has as an additional application the power to classify the rhythms PR (Pulse-generating rhythm) and PEA. To do this, we analyze the algorithm that has been developed and published for stable rhythms. It is also considered as the basis for the creation of the algorithm whose objective is the project. With the help of the graphical interfaces of MATLAB, the database and the algorithm of stable rhythms have been managed. With the reference segmentation marked in the database a statistical evaluation was carried out to obtain the most optimal algorithm possible. Likewise, the QRS metrics have been used for the classification capacity of PR and PEA. Analyzing the results of the function, it is concluded that it has good segmentation capabilities, but a poor capacity to classify. This occurs by the criteria used in reference QRS segmentation.

[eu] Mundu mailako heriotza arrazoi nagusia bat-bateko bihotz-heriotza (BBH) da eta ikerketetan oinarrituz, BBH jasateko probabilitatea handitu egingo da gaixotasun kardiakoen igoera globala emango delako. Bihotz geldialdi baten aurrean bizi irauteko probabilitatea baxua da, nahiz eta suspertze tekniketan aurrerapena egon. Gaur egun BBH ospitale kanpo izateko probabilitatea handia da, bizi irauteko aukera ospitale barruan gertatutako geldialdi batena baino txikiagoa izanik (<% 10). Ospitale kanpoko bihotz-biriketako geldialdietan (BBG) ezinbestekoa da antzemate goiztiarra izatea eta jarraitu beharreko protokoloaren etapa desberdinak ezagutzea. BBGtan oinarrizkoa da elektrokardiogramek (EKG) emandako informazioa klinikoa; beraz, EKGa automatikoki definitzeko metodo zehatzak eta sendoak garatzea garrantzitsua da hainbat aplikazioetarako: erritmo diagnosirako, pultsua detektatzeko, hemodinamikoki pazientea ezaugarritzeko, ... Horretarako, QRS uhinaren detekzioa beharrezkoa da bertan bihotz taupaden energia dagoelako; hala ere, esan beharra dago EKGtako uhinen hasiera eta bukaera kokatzeko araurik ez dagoela. Oso irregularrak eta aberranteak diren erritmo organizatuak ageri dira BBGan; batez ere, PEA (Pulseless electrical activity) deituriko erritmoak, QRS oso berezi eta aldakorrak dituztenak. Beraz, proiektuaren helburu nagusia denbora laburreko eta erritmo organizatuko EKGen QRSa segmentatzeko algoritmo egoki baten proposamena da, zeinak aplikazio gehigarri modura PR (Pulse-generating rhythm) eta PEA erritmoen sailkatzailea izatea den. Horretarako, erritmo egonkorrentzat garatu eta argitaratu den algoritmoa aztertuko da. Era berean, proiektuaren helburu den algoritmoa sortzeko oinarri bezala hartuko da. Prozesua gauzatzeko MATLABen interfaze grafikoen laguntzaz datu-basea eta erritmo egonkorrentzako algoritmoa kudeatu ahal izan dira. Datu-basean markatutako erreferentzia segmentazioarekin ebaluaketa estatistikoa egin da ahalik eta algoritmo optimoa lortzeko. Era berean, QRS metrikak erabili dira PR eta PEA sailkapena egiteko. Funtzioaren emaitzei erreparatuz segmentazio ahalmena ona daukala ikus daiteke baina sailkapena egiteko gaitasun txarra. Honako hau erreferentzia moduan erabilitako QRS segmentazio irizpideengatik gertatuz.