Fibrilazio aurikular parosixistikoa aurresateko metodoa

Bihotzeko arritmiak bihotzaren funtzio elektrikoan gertatzen diren irregulartasunak dira.Bihotzeko erritmoaren aldaketa hauek, erritmoa bizkortzeagatik ala gutxiagotzeagatik izan daitezke, eta ez dira nahitaez irregularrak izan behar. Hala ere, normalean arritmiek erritmo irregularra sortzen dute, bihotzaren berezko taupadan edota bihotzaren sistema elektrikoan anomaliak agertzen direnean. Zenbait arritmia mota aurki daitezke; horien artean, aurikuletan agertzen dena bereiz daiteke. Kasu honetan, aurikuletan ematen diren taupadak desorekatuak direnean fibrilazio aurikularra (FA) agertu dela esaten da. FA bihotz-arritmiarik ohikoena dela esan ohi da, baita osasun sisteman kosturik handiena eragiten duena ere. Izan ere, azkeneko hamarkadan FA ren prebalentzia handituz joan da, eta okerrera jarraituko du gizartearen zahartzearen ondorioz. FA ez da arritmien artean larriena, arritmia mota honek ez baitu berez gizakiaren bizitza arriskuan ipintzen. Hala ere, oso kaltegarria da bizitzarako. Izan ere, arritmia honek pertsona batengan bihotz-gutxiegitasunagatik edo enbolia batengatik morbilitate arrisku altua izatea eragin ditzake. Hau guztia dela eta, FA aurresateko metodoak izatea oso garrantzitsua dela ondoriozta dezakegu. Hori izango da, beraz, proiektu honetan zehar garatuko dena. Horretarako, zenbait pazienteren informazioaz baliatuko da, geroago azalduko den moduan, informazio hori elektrokardiograma deituriko erregistroetan oinarrituta egongo da. Beraz, erregistro horiek abiapuntutzat edukita, eta informazio jakin hori prozesatu eta landu ondoren, FA aurresateko zenbait algoritmo garatuko dira. Azkenik, garatutako algoritmo horiekin lortutako emaitzak aztertuko dira, metodo hauek fidagarriak diren ala ez ondorioztatzeko.Se le llaman arritmias a las irregularidades que suceden en el sistema de conducción cardíaco. Estas variaciones que se dan en el ritmo cardiaco pueden ser porque el corazón acelere o disminuya su frecuencia cardiaca, y no tienen que ser necesariamente irregulares. Sin embargo, normalmente las arritmias crean ritmos irregulares cuando suceden anomalías en el marcapasos fisiológico del corazón o en el sistema eléctrico del corazón. Se diferencian varias arritmias entre las cuales se encuentra la que sucede en las aurículas. En este caso, cuando la conducción de los latidos que se dan en estas cavidades es desordenada, se padece fibrilación auricular (FA). La FA es la arritmia más común, además de ser también la que más gastos ocasiona en el sistema de la salud. De hecho, en la última década la prevalencia de dicha arritmia ha ido aumentando, y se prevé que irá a peor por consecuencia del envejecimiento de la población. La FA no se encuentra entre las arritmias más graves, ya que esta arritmia no pone por si sola en riesgo la vida del ser humano. Sin embargo, es muy perjudicial para la salud ya que las personas que padecen esta enfermedad podrían tener un alto riesgo de morbilidad en caso de sufrir una insuficiencia cardiaca o una embolia. Por todo esto, podemos deducir que es muy importante encontrar métodos que predicen la FA. Por lo cual, eso va a ser lo que se va a desarrollar en este proyecto. Para ello, aprovecharemos de la información de ciertos pacientes la cual, como se explicará más adelante, se basa en registros llamados electrocardiogramas. Por consiguiente, teniendo estos registros como punto de partida, y después de haberlos procesado y trabajado, se desarrollarán diferentes algoritmos para predecir la FA. Por último, se analizarán los resultados conseguidos a partir de estos algoritmos, los cuales nos ayudaran a concluir si estos métodos son fiables o no.Arrhythmias are known as irregularities that happen in the cardiac conduction system. These heart rate variations can occur when the heart rate is accelerated or decreased, and they do not necessarily have to be irregular. However, arrhythmias usually create irregular rhythms when abnormalities occur in the physiological pacemaker of the heart or in the electrical system of the heart. There are several types of arrhythmias, some of the most typical ones are those occurring in the atria. A patient suffers atrial fibrillation (AF) when the beats that occur in those cavities are disorganized. AF is the most common arrhythmia, as well as being the one that causes most expenses in the health system. In fact, in the last decade the prevalence of this arrhythmia has been increasing, and it is expected that it will get worse due to the aging of the population. AF is not one of the most serious arrhythmias, since it doesn ́t put human lives at risk. However, it is very detrimental for the patient ́s health, since people suffering from this disease could have a high risk of morbidity in case of suffering heart failure or an embolism. Thus, we can conclude that it is very important to find methods that predict AF. Therefore, that ́s what is going to be developed in this project. To obtain this goal, we will take some information of certain patients which, as it will be explained later, is basedon records called electrocardiograms. Therefore, after processing this information, different algorithms will be developed to predict AF. Finally, the results obtained from these algorithms will be analyzed, which will help us to conclude if these methods are reliable or not

Fibrilazio aurikular parosixistikoa aurresateko metodoa

Bihotzeko arritmiak bihotzaren funtzio elektrikoan gertatzen diren irregulartasunak dira.Bihotzeko erritmoaren aldaketa hauek, erritmoa bizkortzeagatik ala gutxiagotzeagatik izan daitezke, eta ez dira nahitaez irregularrak izan behar. Hala ere, normalean arritmiek erritmo irregularra sortzen dute, bihotzaren berezko taupadan edota bihotzaren sistema elektrikoan anomaliak agertzen direnean. Zenbait arritmia mota aurki daitezke; horien artean, aurikuletan agertzen dena bereiz daiteke. Kasu honetan, aurikuletan ematen diren taupadak desorekatuak direnean fibrilazio aurikularra (FA) agertu dela esaten da. FA bihotz-arritmiarik ohikoena dela esan ohi da, baita osasun sisteman kosturik handiena eragiten duena ere. Izan ere, azkeneko hamarkadan FA ren prebalentzia handituz joan da, eta okerrera jarraituko du gizartearen zahartzearen ondorioz. FA ez da arritmien artean larriena, arritmia mota honek ez baitu berez gizakiaren bizitza arriskuan ipintzen. Hala ere, oso kaltegarria da bizitzarako. Izan ere, arritmia honek pertsona batengan bihotz-gutxiegitasunagatik edo enbolia batengatik morbilitate arrisku altua izatea eragin ditzake. Hau guztia dela eta, FA aurresateko metodoak izatea oso garrantzitsua dela ondoriozta dezakegu. Hori izango da, beraz, proiektu honetan zehar garatuko dena. Horretarako, zenbait pazienteren informazioaz baliatuko da, geroago azalduko den moduan, informazio hori elektrokardiograma deituriko erregistroetan oinarrituta egongo da. Beraz, erregistro horiek abiapuntutzat edukita, eta informazio jakin hori prozesatu eta landu ondoren, FA aurresateko zenbait algoritmo garatuko dira. Azkenik, garatutako algoritmo horiekin lortutako emaitzak aztertuko dira, metodo hauek fidagarriak diren ala ez ondorioztatzeko.Se le llaman arritmias a las irregularidades que suceden en el sistema de conducción cardíaco. Estas variaciones que se dan en el ritmo cardiaco pueden ser porque el corazón acelere o disminuya su frecuencia cardiaca, y no tienen que ser necesariamente irregulares. Sin embargo, normalmente las arritmias crean ritmos irregulares cuando suceden anomalías en el marcapasos fisiológico del corazón o en el sistema eléctrico del corazón. Se diferencian varias arritmias entre las cuales se encuentra la que sucede en las aurículas. En este caso, cuando la conducción de los latidos que se dan en estas cavidades es desordenada, se padece fibrilación auricular (FA). La FA es la arritmia más común, además de ser también la que más gastos ocasiona en el sistema de la salud. De hecho, en la última década la prevalencia de dicha arritmia ha ido aumentando, y se prevé que irá a peor por consecuencia del envejecimiento de la población. La FA no se encuentra entre las arritmias más graves, ya que esta arritmia no pone por si sola en riesgo la vida del ser humano. Sin embargo, es muy perjudicial para la salud ya que las personas que padecen esta enfermedad podrían tener un alto riesgo de morbilidad en caso de sufrir una insuficiencia cardiaca o una embolia. Por todo esto, podemos deducir que es muy importante encontrar métodos que predicen la FA. Por lo cual, eso va a ser lo que se va a desarrollar en este proyecto. Para ello, aprovecharemos de la información de ciertos pacientes la cual, como se explicará más adelante, se basa en registros llamados electrocardiogramas. Por consiguiente, teniendo estos registros como punto de partida, y después de haberlos procesado y trabajado, se desarrollarán diferentes algoritmos para predecir la FA. Por último, se analizarán los resultados conseguidos a partir de estos algoritmos, los cuales nos ayudaran a concluir si estos métodos son fiables o no.Arrhythmias are known as irregularities that happen in the cardiac conduction system. These heart rate variations can occur when the heart rate is accelerated or decreased, and they do not necessarily have to be irregular. However, arrhythmias usually create irregular rhythms when abnormalities occur in the physiological pacemaker of the heart or in the electrical system of the heart. There are several types of arrhythmias, some of the most typical ones are those occurring in the atria. A patient suffers atrial fibrillation (AF) when the beats that occur in those cavities are disorganized. AF is the most common arrhythmia, as well as being the one that causes most expenses in the health system. In fact, in the last decade the prevalence of this arrhythmia has been increasing, and it is expected that it will get worse due to the aging of the population. AF is not one of the most serious arrhythmias, since it doesn ́t put human lives at risk. However, it is very detrimental for the patient ́s health, since people suffering from this disease could have a high risk of morbidity in case of suffering heart failure or an embolism. Thus, we can conclude that it is very important to find methods that predict AF. Therefore, that ́s what is going to be developed in this project. To obtain this goal, we will take some information of certain patients which, as it will be explained later, is basedon records called electrocardiograms. Therefore, after processing this information, different algorithms will be developed to predict AF. Finally, the results obtained from these algorithms will be analyzed, which will help us to conclude if these methods are reliable or not

Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health, weed presence and phenological state, among others. Traditionally, models based on normalized difference vegetation index (NDVI), near infrared channel (NIR) or RGB have been a good indicator of vegetation presence. However, these methods are not suitable for accurately segmenting vegetation showing damage, which precludes their use for downstream phenotyping algorithms. In this paper, we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation. The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image. Second, we compute two newly proposed vegetation indices from this estimated virtual NIR: the infrared-dark channel subtraction (IDCS) and infrared-dark channel ratio (IDCR) indices. Finally, both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition. The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days. The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel (F1=0.94) and with the proposed IDCR and IDCS vegetation indices (F1=0.95) derived from the estimated NIR channel, while the use of only the image or RGB indices lead to inferior performance (RGB(F1=0.90) NIR(F1=0.82) or NDVI(F1=0.89) channel). The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions

Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets

[EN] Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.This project was partially supported by the Spanish Government through CDTI Centro para el Desarrollo Tecnológico e Industrial project AI4ES (ref CER-20211030), by the University of the Basque Country (UPV/EHU) under grant COLAB20/01 and by the Basque Government through grant IT1229-19

Fibrilazio aurikular parosixistikoa aurresateko metodoa

Bihotzeko arritmiak bihotzaren funtzio elektrikoan gertatzen diren irregulartasunak dira.Bihotzeko erritmoaren aldaketa hauek, erritmoa bizkortzeagatik ala gutxiagotzeagatik izan daitezke, eta ez dira nahitaez irregularrak izan behar. Hala ere, normalean arritmiek erritmo irregularra sortzen dute, bihotzaren berezko taupadan edota bihotzaren sistema elektrikoan anomaliak agertzen direnean. Zenbait arritmia mota aurki daitezke; horien artean, aurikuletan agertzen dena bereiz daiteke. Kasu honetan, aurikuletan ematen diren taupadak desorekatuak direnean fibrilazio aurikularra (FA) agertu dela esaten da. FA bihotz-arritmiarik ohikoena dela esan ohi da, baita osasun sisteman kosturik handiena eragiten duena ere. Izan ere, azkeneko hamarkadan FA ren prebalentzia handituz joan da, eta okerrera jarraituko du gizartearen zahartzearen ondorioz. FA ez da arritmien artean larriena, arritmia mota honek ez baitu berez gizakiaren bizitza arriskuan ipintzen. Hala ere, oso kaltegarria da bizitzarako. Izan ere, arritmia honek pertsona batengan bihotz-gutxiegitasunagatik edo enbolia batengatik morbilitate arrisku altua izatea eragin ditzake. Hau guztia dela eta, FA aurresateko metodoak izatea oso garrantzitsua dela ondoriozta dezakegu. Hori izango da, beraz, proiektu honetan zehar garatuko dena. Horretarako, zenbait pazienteren informazioaz baliatuko da, geroago azalduko den moduan, informazio hori elektrokardiograma deituriko erregistroetan oinarrituta egongo da. Beraz, erregistro horiek abiapuntutzat edukita, eta informazio jakin hori prozesatu eta landu ondoren, FA aurresateko zenbait algoritmo garatuko dira. Azkenik, garatutako algoritmo horiekin lortutako emaitzak aztertuko dira, metodo hauek fidagarriak diren ala ez ondorioztatzeko.Se le llaman arritmias a las irregularidades que suceden en el sistema de conducción cardíaco. Estas variaciones que se dan en el ritmo cardiaco pueden ser porque el corazón acelere o disminuya su frecuencia cardiaca, y no tienen que ser necesariamente irregulares. Sin embargo, normalmente las arritmias crean ritmos irregulares cuando suceden anomalías en el marcapasos fisiológico del corazón o en el sistema eléctrico del corazón. Se diferencian varias arritmias entre las cuales se encuentra la que sucede en las aurículas. En este caso, cuando la conducción de los latidos que se dan en estas cavidades es desordenada, se padece fibrilación auricular (FA). La FA es la arritmia más común, además de ser también la que más gastos ocasiona en el sistema de la salud. De hecho, en la última década la prevalencia de dicha arritmia ha ido aumentando, y se prevé que irá a peor por consecuencia del envejecimiento de la población. La FA no se encuentra entre las arritmias más graves, ya que esta arritmia no pone por si sola en riesgo la vida del ser humano. Sin embargo, es muy perjudicial para la salud ya que las personas que padecen esta enfermedad podrían tener un alto riesgo de morbilidad en caso de sufrir una insuficiencia cardiaca o una embolia. Por todo esto, podemos deducir que es muy importante encontrar métodos que predicen la FA. Por lo cual, eso va a ser lo que se va a desarrollar en este proyecto. Para ello, aprovecharemos de la información de ciertos pacientes la cual, como se explicará más adelante, se basa en registros llamados electrocardiogramas. Por consiguiente, teniendo estos registros como punto de partida, y después de haberlos procesado y trabajado, se desarrollarán diferentes algoritmos para predecir la FA. Por último, se analizarán los resultados conseguidos a partir de estos algoritmos, los cuales nos ayudaran a concluir si estos métodos son fiables o no.Arrhythmias are known as irregularities that happen in the cardiac conduction system. These heart rate variations can occur when the heart rate is accelerated or decreased, and they do not necessarily have to be irregular. However, arrhythmias usually create irregular rhythms when abnormalities occur in the physiological pacemaker of the heart or in the electrical system of the heart. There are several types of arrhythmias, some of the most typical ones are those occurring in the atria. A patient suffers atrial fibrillation (AF) when the beats that occur in those cavities are disorganized. AF is the most common arrhythmia, as well as being the one that causes most expenses in the health system. In fact, in the last decade the prevalence of this arrhythmia has been increasing, and it is expected that it will get worse due to the aging of the population. AF is not one of the most serious arrhythmias, since it doesn ́t put human lives at risk. However, it is very detrimental for the patient ́s health, since people suffering from this disease could have a high risk of morbidity in case of suffering heart failure or an embolism. Thus, we can conclude that it is very important to find methods that predict AF. Therefore, that ́s what is going to be developed in this project. To obtain this goal, we will take some information of certain patients which, as it will be explained later, is basedon records called electrocardiograms. Therefore, after processing this information, different algorithms will be developed to predict AF. Finally, the results obtained from these algorithms will be analyzed, which will help us to conclude if these methods are reliable or not

Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health, weed presence and phenological state, among others. Traditionally, models based on normalized difference vegetation index (NDVI), near infrared channel (NIR) or RGB have been a good indicator of vegetation presence. However, these methods are not suitable for accurately segmenting vegetation showing damage, which precludes their use for downstream phenotyping algorithms. In this paper, we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation. The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image. Second, we compute two newly proposed vegetation indices from this estimated virtual NIR: the infrared-dark channel subtraction (IDCS) and infrared-dark channel ratio (IDCR) indices. Finally, both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition. The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days. The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel (F1=0.94) and with the proposed IDCR and IDCS vegetation indices (F1=0.95) derived from the estimated NIR channel, while the use of only the image or RGB indices lead to inferior performance (RGB(F1=0.90) NIR(F1=0.82) or NDVI(F1=0.89) channel). The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions