Wearable real-time heart attack detection and warning system to reduce road accidents

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone—that is one in every four deaths—but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time–frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the worldAcknowledgments: The publication of this article was funded by the Qatar National Library. This work was supported in part by the Undergraduate Research Experience Program (UREP) under Grant number UREP19-069-2-031, in part by the Qatar University Student Grant under Grant number QUST-CENG-SPR\2017-23.Scopu

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Implementation of a Convolutional Neural Network to Distinguish between Radiological Patterns of COVID-19 and Pneumonia in Chest CT Images

En el año 2020, la Organización Mundial de la Salud (OMS) proclamó la existencia de una pandemia originada por el coronavirus (COVID-19), cuyo brote inicial tuvo lugar en Wuhan, China. Este virus ha tenido un impacto devastador, cobrando la vida de miles y afectando a millones en todo el mundo. Sus síntomas, que incluyen tos, fiebre, fatiga y disnea, se asemejan a los de una gripe común. La propagación del virus ocurre principalmente a través de partículas respiratorias emitidas por personas infectadas, las cuales pueden depositarse en los ojos, boca o nariz de otras personas. Para confirmar la infección, se utilizan dos tipos de pruebas: la prueba de reacción en cadena de la polimerasa con transcripción inversa (RT-PCR) y las pruebas de antígenos. Sin embargo, debido a sus procesamientos, estas pruebas pueden demorar en proporcionar resultados definitivos. Es en este contexto que la inteligencia artificial y las técnicas de Machine Learning (ML) se presentan como herramientas valiosas para mejorar la detección del virus en los pulmones de manera eficiente. En este trabajo, se propone la implementación de una Red Neuronal Convolucional (CNN) para la detección temprana de pacientes con COVID-19. Se utiliza un conjunto de datos compuesto por 3616 imágenes de rayos X de tórax, empleando una red neuronal preentrenada denominada VGG16. A través del entrenamiento, se logra una precisión óptima en la clasificación de las imágenes en las categorías de COVID y Neumonía.In 2020, the World Health Organization (WHO) proclaimed the existence of a pandemic originating from the coronavirus (COVID-19), the initial outbreak of which occurred in Wuhan, China. This virus has had a devastating impact, claiming the lives of thousands and affecting millions worldwide. Its symptoms, which include cough, fever, fatigue and dyspnea, resemble those of a common flu. Spread of the virus occurs primarily through respiratory particles emitted by infected people, which can be deposited in the eyes, mouth or nose of others. Two types of tests are used to confirm infection: reverse transcription-polymerase chain reaction (RT-PCR) and antigen testing. However, due to their processing, these tests can take time to provide definitive results. It is in this context that artificial intelligence and Machine Learning (ML) techniques are presented as valuable tools to improve virus detection in lungs in an efficient way. In this work, the implementation of a Convolutional Neural Network (CNN) for the early detection of patients with COVID-19 is proposed. A dataset composed of 3616 chest X-ray images is used, employing a pre-trained neural network named VGG16. Through training, optimal accuracy in classifying images into COVID and Pneumonia categories is achieved

Can AI help in screening Viral and COVID-19 pneumonia?

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.Comment: 12 pages, 9 Figure

Machine Learning in Wearable Biomedical Systems

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems

A review of technologies for heart attack monitoring systems

Every year, approximately 1.35 million people die in car accidents. One of the causes of traffic accidents is a heart attack while driving. Common heart attack warning signs are pain or discomfort in the chest or one or both arms or shoulders, light-headedness, faintness, cold sweat, and shortness of breath. When having a heart attack, a car driver has strong pain in the centre or left side of the chest. Current technology for heart attack detection is based on sensory signal properties such as the electrocardiogram (ECG), heart rate and oxygen saturation (SpO2). This paper is intended to give the readers an overview of technologies for heart attack monitoring system that has been used at the hospital, at the home and in the vehicle. The result shows that ECG, heart rate and SpO2 properties are mostly used by numerous researchers for heart attack monitoring systems at hospitals. Meanwhile, many researchers developed a system by using heart rate, ECG, SpO2 and images as properties for heart attack monitoring systems at home. Existing technologies for heart attack monitoring systems in the vehicle used heart rate and ECG as properties in a system. However, there are no review papers yet on heart attack monitoring systems using image processing in vehicles. We believe that researchers and practitioners will embrace this technology by addressing image processing in the heart attack monitoring system in vehicles

Investigation of heart specific sensory neurons using a transgenic approach in a healthy and after ichemic damage mouse heart

Duyusal sinir sistemi, kalp fonksiyonunun regülasyonunda kritik öneme sahiptir. Otonom sinirlerin kalbi inervasyonu ve fonksiyonu hakkında birçok çalışma olmakla beraber, kalbin afferent inervasyonu konusunda bilgimiz yetersizdir. Literatürdeki bu eksikliği gidermek ve afferentlerin sağlıklı ve miyokard infarktüs (MI) sonrası kalbe inervasyonunu 3 boyutlu (3B) tüm kalpte haritalamak amacıyla tez kapsamında VGLUT2-Cre::tdTomato ikili transgenik fare soyu üretildi ve ileri yöntemler kullanılarak görüntülendi. VGLUT2 ifade eden spinal ve vagal kökenli kardiyak afferentlerin haritasını kalbin dorsal ve ventral yüzlerinde daha önce gösterilmemiş yüksek çözünürlükte ve 3B görüntüledik. Kalbin dorsal yüzünde ventrale oranla anlamlı şekilde daha yoğun afferent inervasyonunun olduğunu tespit ettik (p=0.004). Afferent ve efferent karşılaştırmalı 3B kardiyak haritalarını oluşturduğumuz çalışmamızda nöron çapı analizlerinde ~5μm'den kalın fiberlerin dorsal yüzde istatistiksel anlamlı olarak yoğun olduğu tespit edildi (p=0.01). Atriyum ve ventriküllerin ayrıntılı karakterizasyonu sonucunda, çiçek benzeri sonlanmalar, kas içi uçlar ve kalbin farklı anatomik kısımlarını inerve eden serbest sinir ucu gibi afferent sinir sonlanma morfolojileri yüksek çözünürlükle tespit edildi. VGLUT2::tdTomato transgenik fare kalplerinde TUJ1 immün boyama ile saptanan global sinir inervasyonunun %20-30'unun VGLUT2 ifade eden affrentler ile örtüştüğü tespit edildi. Sağlıklı ve iskemik hasar sonrası kalbin afferent ve efferent inervasyonunu karşılaştırmak amacıyla oluşturulan MI modelinde, hasar sınır bölgesinde hiperinervasyon ve infarktüs alanına uzak bölgelere kıyasla afferent yoğunluğunda istatistiksel bir artış olduğunu gösterdik (p<0.003). Bu bulgular, iskemik hasar sonrası afferent ağdaki değişimin kardiyak disfonksiyon ve mortaliteye etki edebileceğini düşündürmektedir. Bu tez kapsamında üretilen VGLUT2::tdTomato ikili transgenik farelerde 3B kalp görüntüleme tekniklerinde yapılan gelişmeler ile vagal ve spinal afferentlerin büyük çoğunluğunu temsil eden VGLUT2 ifadeli aksonların atriyum ve ventrikül özelinde sağlıklı ve MI sonrası inervasyon paternleri detaylı olarak gösterilmiştir. Bu tezde elde dilen sonuçlar, nörokardiyak ağın afferent inervasyonu tanımlaması yönü ile literatürdeki önemli bir bilgi eksikliğinin giderilmesine önemli katkı sağlamakla beraber, gelecekte yapılacak çalışmalarda afferent ağın özellikle iskemik hasar ve diğer kardiyak anomalilerde etkisinin fonksiyonel araştırmaları için değerli bir alt yapı oluşturmuştur.The sensory nervous system is critical in the regulation of heart function. Although there are many studies on cardiac innervation and function of autonomic nerves, our knowledge on afferent innervation of the heart is insufficient. In order to fill this gap in the literature and to map the innervation of afferents to the healthy and post-myocardial infarction (MI) heart in 3D (3D) whole heart, VGLUT2-Cre::tdTomato double transgenic mouse strain was produced and imaged using advanced methods. We visualized a map of spinal and vagal-derived cardiac afferents expressing VGLUT2 on the dorsal and ventral side of the heart in high resolution and 3D that has not been shown before. We found that there was significantly more intense afferent innervation on the dorsal side of the heart compared to the ventral side (p=0.004). In our study, in which we visualized whole cardiac afferent and efferent innervation, in neuron diameter analyzes fibers thicker than ~5μm were found to be statistically significant on the dorsal side (p=0.01). As a result of detailed characterization of atria and ventricles, spinal afferent nerve ending morphologies such as flower-like endings, intramuscular endings and free nerve endings innervating different anatomical parts of the heart were detected with high resolution. In VGLUT2::tdTomato transgenic mouse hearts, 20-30% of global nerve innervation detected by TUJ1 immunostaining was found to overlap with VGLUT2 expressing afferents. To compare the afferent and efferent innervation of the healthy and post-ischemic heart, we generated mouse MI model. We showed that there was statistical increase in afferent hyperinnervation in the injury border zone when compared to the regions far from the infarct area (p<0.003). These findings suggest that the change in the afferent network after ischemic injury may affect cardiac dysfunction and mortality. In the VGLUT2::tdTomato double transgenic mice produced within the scope of this thesis, the developments in 3D cardiac imaging techniques; the innervation patterns of VGLUT2 expressed axons, which represent the majority of vagal and spinal afferents, in healthy and MI hearts' atria and ventricles have been shown in detail. The results obtained in this thesis, besides making an important contribution to the elimination of an important lack of knowledge in the literature in terms of the definition of afferent innervation of the neurocardiac network, formed a valuable infrastructure for the functional studies of the effect of the afferent network, especially in ischemic damage and other cardiac anomalies, in future studies

Renewable Energy

This book discusses renewable energy resources and systems as well as energy efficiency. It contains twenty-three chapters over six sections that address a multitude of renewable energy types, including solar and photovoltaic, biomass, hydroelectric, and geothermal. The information presented herein is a scientific contribution to energy and environmental regulations, quality and efficiency of energy services, energy supply security, energy market-based approaches, government interventions, and the spread of technological innovation