Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

[EN] Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.This work received financial support from Spanish Ministerio de Economia y Competitividad and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), projects TEC2015-64678-R and DPI2017-83952-C3; from UPV/EHU through the grant PIF15/190 and through project GIU17/031; from the Basque Government through grant PRE-2016-1-0012; and from Junta de Comunidades de Castilla-La Mancha through SBPLY/17/180501/000411.Chicote, B.; Irusta, U.; Aramendi, E.; Alcaraz, R.; Rieta, JJ.; Isasi, I.; Alonso, D.... (2018). 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Heart rate variability as predictive factor for sudden cardiac death. Aging, 10(2), 166-1

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 μV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.This work received financial support from Spanish Ministerio de Economia y Competitividad, projects TEC2013-31928 and TEC2014-52250-R, and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), project TEC2015-64678-R; from Junta de Comunidades de Castilla La Mancha, project PPII-2014-026-P; and from UPV/EHU through the grant PIF15/190 and through its research unit UFI11/16.Chicote, B.; Irusta, U.; Alcaraz, R.; Rieta, JJ.; Aramendi, E.; Isasi, I.; Alonso, D.... (2016). Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy. 18(9):1-17. https://doi.org/10.3390/e18090313S11718

Aprendizaje máquina para la predicción del éxito de la desfibrilación mediante el análisis de la forma de onda de la fibrilación ventricular

Predecir el éxito de la terapia de desfibrilación eléctrica para revertir la fibrilación ventricular (FV) permitiría mejorar la supervivencia de los pacientes en parada cardiorrespiratoria extra-hospitalaria (PCREH). Los métodos de predicción se basan en el análisis de la forma de onda del electrocardiograma (ECG) de la FV usando predictores individuales. El objetivo del trabajo es desarrollar modelos de aprendizaje máquina para mejorar la precisión de los métodos. Se dispuso de una base de 1630 casos de PCREH anotados por clínicos, que incluían 3836 intentos de desfibrilación (1081 exitosos). Se diseñaron modelos predictores del éxito de la desfibrilación caracterizando con 27 parámetros el intervalo ECG pre-shock de 2.05s de duración. Se evaluaron los modelos en términos de sensibilidad (SE, éxito), especificidad (SP, no éxito) y la media de ambas (precisión balanceada, PB). Los modelos se ajustaron y evaluaron mediante validación cruzada en particiones por paciente y estratificadas, repitiendo el proceso 100 veces para caracterizar estadísticamente las métricas. Se comparó un modelo de regresión logística mono- paramétrico con varios modelos de aprendizaje máquina: regre- sión logística multiparamétrica, bosques de árboles y máquinas de vectores soporte (SVM) con kernel gaussiano. El mejor mode- lo monoparamétrico produjo una PB mediana (rango interdecilo, IDR) de 79.5 (79.4-79.6)%. El mejor modelo multiparamétrico (SVM con 6 parámetros) resultó en una PB mediana de 81.4 (81.2-81.6)%, y SE, SP de 83.4 (83.1-84.0)% y 79.3 (79.1- 80.0)%, respectivamente. Los modelos de aprendizaje máquina permiten mejorar la predicción monoparamétrica hasta en 2 pun- tos de PB. En el futuro deberán desarrollarse nuevos predictores, para lo que la extracción exhaustiva de característi-cas mediante redes convolucionales (CNN) sería una buena alternativa.Este trabajo ha recibido ayuda financiera del Ministerio de Ciencia, Innovación y Universidades, proyecto RTI- 2018-101475-BI00, junto con el Fondo Europeo de Desarrollo Regional (FEDER), y del Gobierno Vasco por medio de la subvención a grupos de investigación IT-1229- 19 y la beca del programa Ikasiker IkasC_2019_1_0275

Aprendizaje máquina para la predicción del éxito de la desfibrilación mediante el análisis de la forma de onda de la fibrilación ventricular

Predecir el éxito de la terapia de desfibrilación eléctrica para revertir la fibrilación ventricular (FV) permitiría mejorar la supervivencia de los pacientes en parada cardiorrespiratoria extra-hospitalaria (PCREH). Los métodos de predicción se basan en el análisis de la forma de onda del electrocardiograma (ECG) de la FV usando predictores individuales. El objetivo del trabajo es desarrollar modelos de aprendizaje máquina para mejorar la precisión de los métodos. Se dispuso de una base de 1630 casos de PCREH anotados por clínicos, que incluían 3836 intentos de desfibrilación (1081 exitosos). Se diseñaron modelos predictores del éxito de la desfibrilación caracterizando con 27 parámetros el intervalo ECG pre-shock de 2.05s de duración. Se evaluaron los modelos en términos de sensibilidad (SE, éxito), especificidad (SP, no éxito) y la media de ambas (precisión balanceada, PB). Los modelos se ajustaron y evaluaron mediante validación cruzada en particiones por paciente y estratificadas, repitiendo el proceso 100 veces para caracterizar estadísticamente las métricas. Se comparó un modelo de regresión logística mono- paramétrico con varios modelos de aprendizaje máquina: regre- sión logística multiparamétrica, bosques de árboles y máquinas de vectores soporte (SVM) con kernel gaussiano. El mejor mode- lo monoparamétrico produjo una PB mediana (rango interdecilo, IDR) de 79.5 (79.4-79.6)%. El mejor modelo multiparamétrico (SVM con 6 parámetros) resultó en una PB mediana de 81.4 (81.2-81.6)%, y SE, SP de 83.4 (83.1-84.0)% y 79.3 (79.1- 80.0)%, respectivamente. Los modelos de aprendizaje máquina permiten mejorar la predicción monoparamétrica hasta en 2 pun- tos de PB. En el futuro deberán desarrollarse nuevos predictores, para lo que la extracción exhaustiva de característi-cas mediante redes convolucionales (CNN) sería una buena alternativa.Este trabajo ha recibido ayuda financiera del Ministerio de Ciencia, Innovación y Universidades, proyecto RTI- 2018-101475-BI00, junto con el Fondo Europeo de Desarrollo Regional (FEDER), y del Gobierno Vasco por medio de la subvención a grupos de investigación IT-1229- 19 y la beca del programa Ikasiker IkasC_2019_1_0275

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

Data mart based research in heart surgery

Arnrich B. Data mart based research in heart surgery. Bielefeld (Germany): Bielefeld University; 2006.The proposed data mart based information system has proven to be useful and effective in the particular application domain of clinical research in heart surgery. In contrast to common data warehouse systems who are focused primarily on administrative, managerial, and executive decision making, the primary objective of the designed and implemented data mart was to provide an ongoing, consolidated and stable research basis. Beside detail-oriented patient data also aggregated data are incorporated in order to fulfill multiple purposes. Due to the chosen concept, this technique integrates the current and historical data from all relevant data sources without imposing any considerable operational or liability contract risk for the existing hospital information systems (HIS). By this means the possible resistance of involved persons in charge can be minimized and the project specific goals effectively met. The challenges of isolated data sources, securing a high data quality, data with partial redundancy and consistency, valuable legacy data in special file formats, and privacy protection regulations are met with the proposed data mart architecture. The applicability was demonstrated in several fields, including (i) to permit easy comprehensive medical research, (ii) to assess preoperative risks of adverse surgical outcomes, (iii) to get insights into historical performance changes, (iv) to monitor surgical results, (v) to improve risk estimation, and (vi) to generate new knowledge from observational studies. The data mart approach allows to turn redundant data from the electronically available hospital data sources into valuable information. On the one hand, redundancies are used to detect inconsistencies within and across HIS. On the other hand, redundancies are used to derive attributes from several data sources which originally did not contain the desired semantic meaning. Appropriate verification tools help to inspect the extraction and transformation processes in order to ensure a high data quality. Based on the verification data stored during data mart assembly, various aspects on the basis of an individual case, a group, or a specific rule can be inspected. Invalid values or inconsistencies must be corrected in the primary source data bases by the health professionals. Due to all modifications are automatically transferred to the data mart system in a subsequent cycle, a consolidated and stable research data base is achieved throughout the system in a persistent manner. In the past, performing comprehensive observational studies at the Heart Institute Lahr had been extremely time consuming and therefore limited. Several attempts had already been conducted to extract and combine data from the electronically available data sources. Dependent on the desired scientific task, the processes to extract and connect the data were often rebuilt and modified. Consequently the semantics and the definitions of the research data changed from one study to the other. Additionally, it was very difficult to maintain an overview of all data variants and derived research data sets. With the implementation of the presented data mart system the most time and effort consuming process with conducting successful observational studies could be replaced and the research basis remains stable and leads to reliable results

Aprendizaje automático para la anotación de ritmos en parada cardiorrespiratoria

Resumen (castellano) Las paradas cardiorrespiratorias extrahospitalarias (PCREH) se posicionan como una de las principales causas de defunción en los países desarrollados. Ante dicho evento, existen ciertos factores determinantes para la supervivencia del sujeto, incluyendo la reanimación cardio pulmonar, una pronta desfibrilación y la calidad del tratamiento ofrecido por el Servicio de Emergencias Médicas. El corazón del paciente puede presentar hasta cinco tipos de ritmos distintos. Puesto que cada estado clínico precisa un tratamiento diferente, es de vital importancia para el personal médico, la pronta y correcta identificación del ritmo/estado del paciente. Por consiguiente, existen numerosos estudios dedicados al entendimiento de dichas patologías, los cuales emplean grabaciones de la señal electrocardiograma (ECG) durante episodios PCREH. Dichas grabaciones deben ser anotadas manualmente por un grupo de expertos clínicos. Por lo tanto, resulta una tarea dispendiosa, lo cual ocasiona escasez de bases de datos debidamente caracterizadas y anotadas. Con el objetivo de facilitar el acceso a colecciones de datos correctamente anotadas, existen algoritmos de anotación semiautomáticos. Estos algoritmos permiten identificar con elevada certeza, las patologías presentes en distintos intervalos temporales de la señal ECG. De esta forma, los expertos clínicos se focalizan en repasar las decisiones del algoritmo, ahorrando tiempo y coste. Por todo ello, los algoritmos de anotación facilitan los estudios de enfermedades cardiacas, mejorando la calidad del tratamiento realizado y, de esta forma, la probabilidad de supervivencia del paciente. En este trabajo se presentan cuatro clasificadores de ritmos de pacientes en PCREH. Para su desarrollo, primero se prepara una colección de episodios PCREH con los que entrenar los algoritmos. El primer clasificador extrae información únicamente de la señal ECG. El segundo añade la información presente en la impedancia transtorácica del paciente. Después, se desarrolla un tercer clasificador mediante técnicas de Deep Learning, puesto que recientemente ha demostrado su potencial en este campo. El cuarto clasificador lo conforma una versión optimizada del anterior modelo. Finalmente, se analizan los resultados y se compara el rendimiento de las distintas soluciones propuestas.Summary (English) Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death in developed countries. There are several key factors to survive an OHCA event, including cardiopulmonary resuscitation, early defibrillation and the overall quality of treatment given by the Emergency Medical System. The patient's heart can present up to five different types of rhythms. Since each clinical condition requires a different treatment, a fast and precise identification of the patient's rhythm/status is crucial for the medical staff. Therefore, there are numerous studies that focus on the understanding of these pathologies, using electrocardiogram signals (ECG) recorded during OHCA events. These recordings must be manually annotated by a group of clinical experts. Because the high costs associated to manual annotation, there is a lack of properly characterized and annotated databases. In order to facilitate access to correctly annotated data collections, there are semiautomatic annotation algorithms. These algorithms identify with high accuracy the pathologies present in different time intervals of the ECG signal. In this way, clinical experts would focus on reviewing the algorithm's decisions, saving time and money. All these considerations make annotation algorithms a key factor to develop studies on OHCA, improving the quality of the treatment performed and the probability of patient survival. In this work, four classifiers of OHCA rhythms are presented. For their development, first a collection of OHCA episodes is prepared, in order to train the algorithms. The first classifier extracts information only from the ECG signal. The second one, adds the information present in the patient's transthoracic impedance. Then, a third classifier is developed using Deep Learning techniques, since it has recently demonstrated its potential in this field. After that, a fourth classifier is made optimizing the previous model. Lastly, the results are analysed and the performance of the different proposed solutions is compared.Laburpena (Euskara) Hospitalez kanpoko bihotz geldiketa (HKBG) mundo garatuko heriotza kausa handienetariko bat dira. Geldiketa bat ematen denean zenbait gertakari gako dira pazientearen biziraupenerako, adibidez bihotz biriketako masajea, desfibrilazio goiztiarra edota emergentzia zerbitzuek emandako tratamendua. Pazientearen bihotzak bost erritmo desberdin aurkez ditzazke HKGB batean. Egoera kliniko bakoitzak tratamendu desberdina behar duenez, pazientearen erritmoa/egoera goiz eta zehatz detektatzea oso garrantzitsua da. Ondorioz, lan asko egin dira patologia horiek ulertzeko eta identifikatzeko, orokorrean pazientearen grabatutako elektrokardiograma (EKG) erabiliz. Grabaketa horietan aditu klinikoek erritmoa identifikatu eta anotatu behar dute. Azken hau kostu handiko lana da, eta ondorioz oso HKBG datubase gutxi dago erritmo anotazio egokiekin. Erritmo anotazioak dituzten HKGB datubaseak sortzeko badira erritmoa modu erdiautomatikoan anotatzeko algoritmoak. Algoritmo hauek modu nahiko zehatzean identifika dezaketa HKGB pazientearen erritmo/egoera, horretarako grabatutako EKG erabiliz. Horrela aditu klinikoek emandako diagnostikoa baino ez dute berrikusi behar, denbora eta kostuak aurreztuz. Horregatik anotaziorako algoritmoek HKGBaren inguruko ikerkuntza errazteu eta hobetuko lituzkete, emandako tratamendua hobetuz, eta pazienteen biziraupen aukerak handituz. Lan honetan lau algoritmo garatu dira HKGB erritmoak modu automatikoan sailkatzeko. Algoritmoak garatzeko lehendabizi HKGB kasuen datubase bat prestatu da, algoritmoak entrenatzeko. Lehen sailkatzailea EKG-tik soilik lortzen du informazioa. Bigarrenak bular inpedantziako informazioa ere erabiltzen du. Gero, ikasketa sakonean oinarritutako sailkatzailea garatu da, esparru honetan teknika hauek oso emaitza onak eman izan baitituzte. Azkenik laugarren sailkatzailea aurrekoaren bertsio hobetua da. Bukatzeko, emaitzak aztertu eta sailkatzaileen errendimenduak alderatu dira

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Aprendizaje automático para la anotación de ritmos en parada cardiorrespiratoria

Resumen (castellano) Las paradas cardiorrespiratorias extrahospitalarias (PCREH) se posicionan como una de las principales causas de defunción en los países desarrollados. Ante dicho evento, existen ciertos factores determinantes para la supervivencia del sujeto, incluyendo la reanimación cardio pulmonar, una pronta desfibrilación y la calidad del tratamiento ofrecido por el Servicio de Emergencias Médicas. El corazón del paciente puede presentar hasta cinco tipos de ritmos distintos. Puesto que cada estado clínico precisa un tratamiento diferente, es de vital importancia para el personal médico, la pronta y correcta identificación del ritmo/estado del paciente. Por consiguiente, existen numerosos estudios dedicados al entendimiento de dichas patologías, los cuales emplean grabaciones de la señal electrocardiograma (ECG) durante episodios PCREH. Dichas grabaciones deben ser anotadas manualmente por un grupo de expertos clínicos. Por lo tanto, resulta una tarea dispendiosa, lo cual ocasiona escasez de bases de datos debidamente caracterizadas y anotadas. Con el objetivo de facilitar el acceso a colecciones de datos correctamente anotadas, existen algoritmos de anotación semiautomáticos. Estos algoritmos permiten identificar con elevada certeza, las patologías presentes en distintos intervalos temporales de la señal ECG. De esta forma, los expertos clínicos se focalizan en repasar las decisiones del algoritmo, ahorrando tiempo y coste. Por todo ello, los algoritmos de anotación facilitan los estudios de enfermedades cardiacas, mejorando la calidad del tratamiento realizado y, de esta forma, la probabilidad de supervivencia del paciente. En este trabajo se presentan cuatro clasificadores de ritmos de pacientes en PCREH. Para su desarrollo, primero se prepara una colección de episodios PCREH con los que entrenar los algoritmos. El primer clasificador extrae información únicamente de la señal ECG. El segundo añade la información presente en la impedancia transtorácica del paciente. Después, se desarrolla un tercer clasificador mediante técnicas de Deep Learning, puesto que recientemente ha demostrado su potencial en este campo. El cuarto clasificador lo conforma una versión optimizada del anterior modelo. Finalmente, se analizan los resultados y se compara el rendimiento de las distintas soluciones propuestas.Summary (English) Out-of-hospital cardiac arrest (OHCA) is one of the leading causes of death in developed countries. There are several key factors to survive an OHCA event, including cardiopulmonary resuscitation, early defibrillation and the overall quality of treatment given by the Emergency Medical System. The patient's heart can present up to five different types of rhythms. Since each clinical condition requires a different treatment, a fast and precise identification of the patient's rhythm/status is crucial for the medical staff. Therefore, there are numerous studies that focus on the understanding of these pathologies, using electrocardiogram signals (ECG) recorded during OHCA events. These recordings must be manually annotated by a group of clinical experts. Because the high costs associated to manual annotation, there is a lack of properly characterized and annotated databases. In order to facilitate access to correctly annotated data collections, there are semiautomatic annotation algorithms. These algorithms identify with high accuracy the pathologies present in different time intervals of the ECG signal. In this way, clinical experts would focus on reviewing the algorithm's decisions, saving time and money. All these considerations make annotation algorithms a key factor to develop studies on OHCA, improving the quality of the treatment performed and the probability of patient survival. In this work, four classifiers of OHCA rhythms are presented. For their development, first a collection of OHCA episodes is prepared, in order to train the algorithms. The first classifier extracts information only from the ECG signal. The second one, adds the information present in the patient's transthoracic impedance. Then, a third classifier is developed using Deep Learning techniques, since it has recently demonstrated its potential in this field. After that, a fourth classifier is made optimizing the previous model. Lastly, the results are analysed and the performance of the different proposed solutions is compared.Laburpena (Euskara) Hospitalez kanpoko bihotz geldiketa (HKBG) mundo garatuko heriotza kausa handienetariko bat dira. Geldiketa bat ematen denean zenbait gertakari gako dira pazientearen biziraupenerako, adibidez bihotz biriketako masajea, desfibrilazio goiztiarra edota emergentzia zerbitzuek emandako tratamendua. Pazientearen bihotzak bost erritmo desberdin aurkez ditzazke HKGB batean. Egoera kliniko bakoitzak tratamendu desberdina behar duenez, pazientearen erritmoa/egoera goiz eta zehatz detektatzea oso garrantzitsua da. Ondorioz, lan asko egin dira patologia horiek ulertzeko eta identifikatzeko, orokorrean pazientearen grabatutako elektrokardiograma (EKG) erabiliz. Grabaketa horietan aditu klinikoek erritmoa identifikatu eta anotatu behar dute. Azken hau kostu handiko lana da, eta ondorioz oso HKBG datubase gutxi dago erritmo anotazio egokiekin. Erritmo anotazioak dituzten HKGB datubaseak sortzeko badira erritmoa modu erdiautomatikoan anotatzeko algoritmoak. Algoritmo hauek modu nahiko zehatzean identifika dezaketa HKGB pazientearen erritmo/egoera, horretarako grabatutako EKG erabiliz. Horrela aditu klinikoek emandako diagnostikoa baino ez dute berrikusi behar, denbora eta kostuak aurreztuz. Horregatik anotaziorako algoritmoek HKGBaren inguruko ikerkuntza errazteu eta hobetuko lituzkete, emandako tratamendua hobetuz, eta pazienteen biziraupen aukerak handituz. Lan honetan lau algoritmo garatu dira HKGB erritmoak modu automatikoan sailkatzeko. Algoritmoak garatzeko lehendabizi HKGB kasuen datubase bat prestatu da, algoritmoak entrenatzeko. Lehen sailkatzailea EKG-tik soilik lortzen du informazioa. Bigarrenak bular inpedantziako informazioa ere erabiltzen du. Gero, ikasketa sakonean oinarritutako sailkatzailea garatu da, esparru honetan teknika hauek oso emaitza onak eman izan baitituzte. Azkenik laugarren sailkatzailea aurrekoaren bertsio hobetua da. Bukatzeko, emaitzak aztertu eta sailkatzaileen errendimenduak alderatu dira

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data