[en] In out-of-hospital cardiac arrest (OHCA) episodes, the patient's chance of survival can be increased if an immediate defibrillation is performed when necessary. In these situations the use of automatic external defibrillators is essential. These devices record and automatically analyze the patient's electrocardiogram (ECG) and, in case of detecting a shockable rhythm, they advise and apply defibrillation. The aim of this project was to carry out a defibrillation recommendation algorithm (DRA) based on machine learning. The work database was created using 4212 5-second-long ECG segments belonging to 855 patients from the Basque Autonomous Community. The work database was divided into two subgroups. The training subset was formed by 60% of the patients and the test subset by the remaining 40%. The ECG segments were band-pass-filtered (1 -30 Hz), features were extracted and, based on these, the DRA was developed. This DRA had two stages. The first one was an asystolic (AS) detector, which decided wheter the ECG segment belonged to an AS or not by analyzing the power of the ECG. If it did not, the segment was analyzed by the ventricular fibrillation (VF) detector, which constituted the second stage. The FV detector was based on a hidden Markov model (HMM) and classified the rhythms as defibrillable or non-defibrillable based on the extracted features. The optimization of the algorithm was done by using the training subset. The optimum power threshold of the AS detector was obtained after an exhaustive search in a specific interval. The optimization of the FV detector, was developed by approaching the progressive envelope that used a 10-iteration cross validation. Using the test subset, the performance of the algorithm was calculated according to sensitivity (SE) and specificity (SP) metrics. The training/test procedure was repeated 50 times in order to estimate the performance metrics' distribution. The results obtained were compared with those obtained by a commercial algorithm. The performance of the algorithm was of mean (standard deviation) SE=97.7% (1.0) and SP=99.1% (0.4), while the performance of the commercial algorithm, SE=94.2% (1.3) and SP=99.8 (0.1). Both methods met the minimum conditions required by the American Heart Association (SE>90% and SP>95%). However, the DRA presents an improvement of 3.5 points in the SE metric and only a 0.7 point disappovement in SP when compared with the commercial algorithm. Therefore, it is concluded that a defibrillation recommendation algorithm based on the HMM is capable of accurately classifying both defibrillable and non-defibrillable rhythms in episodes of OHCA and could definitely improve the performance of commercial algorithms.

[eu] Ospitalez kanpoko bihotz-biriketako geldialdi (OKBBG) episodioetan pazienteen biziraupen probabilitatea handitu daiteke baldin eta beharrezkoa den kasuetan desfibrilazioa goiztiarra ematen bada. Egoera hauetan kanpoko desfibriladore automatikoak funtsezko tresnak dira. Gailu hauek pazientearen elektrokardiograma (EKG) erregistratu, automatikoki aztertu eta desfibrilagarria den erritmoren bat detektatuz gero, desfibrilazioa gomendatu eta aplikatzen dute. Proiektuaren helburua, ikasketa automatikoan oinarritutako desfibrilazioa gomendatzeko algoritmoaren (DGA) garapena izan zen. Lanerako datu-basea, OKBBGa jasan zuten Euskal Autonomia Erkidegoko 885 pazienteeen 5 segunduko 4212 EKG segmentuekin eratu zen. Lanerako datu-basea bi azpimultzotan banatu zen. Entrenamenduko azpimultzoa, pazienteen %60az osoturik eta ebaluaketarako azpimultzoa pazienteen %40az eraturik. EKG segmentuak banda pasa iragazi ziren (1-30 Hz), EKGtik ezaugarriak erauzi ziren eta horietan oinarritutako DGA garatu zen. Bi etapaz osoturik zegoen. Lehenengoa, asistolia (AS) detektorea zeinak EKGaren potentzia aztertuz erabakitzen zuen EKGa AS bati zegokion ala ez. AS bezala ez detektatzekotan, segmentua DGAren bigarren etapan, fibrilazio bentrikular (FB) detektorera pasatzen zen. FB detektorea Markoven eredu ezkutuetan (MEE) oinarritutako algoritmo batean oinarrituta zegoen eta segmentuak desfibrilagarri ala ez-desfibrilagarri bezala sailkatzen zituen erauzitako EKG ezaugarrien arabera. DGAren optimizazioa entrenamenduko azpimultzoarekin egin zen. Optimizazioak AS detektorearen potentzia atalasea finkatzea eta FB detektoreak erabilitako ezaugarrien aukeraketa egitea zuen helburu. AS detektorearen potentzia atalase optimoa tarte espezifiko batean bilaketa eginez egin zen. FB detektorearena, 10 iteraziodun baliozkatze gurutzatua erabiltzen zuen aurreranzko inguratzailearen hurbilketaren bidez egin zen. Ebaluaketarako azpimultzoa erabiliz, DGAren errendimendua sentsibilitate (SE) eta espezifizitate (SP) metriken arabera egin zen. Entrenamendu/ebaluaketa prozedura 50 aldiz errepikatu zen errendimendu metriken banaketa estimatzeko. Lortutako emaitzak algoritmo komertzial batek lortutakoen aurka konparatu ziren. DGAren errendimendua, batazbestekoa (desbideraketa estandarra) SE=97.7% (1.0) eta SP=99.1% (0.4) izan ziren eta algoritmo komertzialak SE=%94.2 (1.3) eta SP=%99.8 (0.1). Biek Amerikako Bihotz Elkarteak eskatutako baldintza minimoak (SE>%90 eta SP>%95) bete zituzten. Hala ere, DGAk SE metrikari dagokionez 3.5 puntuko hobekuntza aurkezten du eta soilik 0.7 puntuko jaitsiera algoritmo komertzialari dagokionez. Beraz, MEEan oinarritutako DGA batek zehaztasunez sailkatzen ditu erritmo desfibrilagarri eta ez-desfibrilagarriak OKBBG episodioetan eta algoritmo komertzialen errendimendua hobetu dezake.