1,836 research outputs found

    Real-time smart-digital stethoscope system for heart diseases monitoring

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    One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.Funding: This research was partially funded by Qatar National Research Foundation (QNRF), grant number UREP19-069-2-031 and UREP23-027-2-012 and Research University Grant AP-2017-008/1. The publication of this article was funded by the Qatar National Library.Scopu

    Innovative Medical Devices for Telemedicine Applications

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    Design and development of electronic stethoscope for auscultation

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    Background: Currently, several companies offer Bluetooth-based electronic stethoscopes. However, the stethoscopes are pretty overpriced. In this case, we need a stethoscope innovation with a more affordable price that carries the same function and improves ear sensitivity during auscultation of heart and lung sounds.Technic: This stethoscope is equipped with a condenser mic that functions as a sound catcher on the stethoscope membrane. The analog data of the condenser mic is regulated by the potential of the pre-amp mic amplifier; then, analog data is forwarded using Bluetooth 5.0 A2DP BT600 USB Wireless Audio Transmitter and received by Bluetooth receiver using earphones.Conclusion: A electronic stethoscope has been successfully developed, which can function adequately to detect, increase heart, lung, bowel sounds, and prenatal sounds

    AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring

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    Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure

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    Heart failure (HF) is an international public health priority and a focus of the NHS Long Term Plan. There is a particular need in primary care for screening and early detection of heart failure with reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic, and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable, accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and 80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was independent of operator, and usability of the tool extended to patients being able to self-examine. Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (£2,500 per patient). This underpins a health economic model for the deployment of AI-ECG across primary care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient self-administered AI-ECG screening programme. I propose approaches to mitigating any potential harms, towards preserving and promoting trust, patient engagement, and public health. Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health economic, and ethical considerations can directly serve patients and health systems by blueprinting best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction needed to realise the full potential of such technologies.Open Acces

    Employment of artificial intelligence mechanisms for e-Health systems in order to obtain vital signs and detect diseases from medical images improving the processes of online consultations and diagnosis

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    Nowadays e-Health web applications allow doctors to access different types of features, such as knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person. The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time, access to a web stethoscope, to a medical image uploader that predicts if a certain disease is present, through deep learning methods, and also allows the visualization of all historical data of a patient. This dissertation has the objective of defending the concept of online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. The process of obtaining vital signs was done via artificial intelligence using a computer camera as sensor. This methodology requires that the user is at a state of rest during the measurements. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.No quotidiano, as aplicações Web e-Saúde permitem aos médicos acesso a diferentes tipos de funcionalidades, como saber qual a medicação que o doente consumiu ou a realização de consultas online. Os sistemas via internet para a saúde podem ser melhorados, utilizando mecanismos de inteligência artificial para os processos de deteção de doenças e de obtenção de dados biológicos, permitindo que os médicos tenham informações importantes que facilitam o processo de diagnóstico ou a escolha do tratamento correto para um determinado utente. O trabalho de investigação proposto pretende apresentar uma abordagem inovadora na comparação com as plataformas tradicionais, ao disponibilizar sinais vitais online em tempo real, acesso a um estetoscópio web, a um uploader de imagens médicas que prevê se uma determinada doença está presente, através de métodos de aprendizagem profunda, bem como permite visualizar todos os dados históricos de um paciente. Esta dissertação visa defender o conceito de consultas virtuais, providenciando funcionalidades complementares aos processos tradicionais de realização de um diagnóstico médico, através da utilização de práticas de engenharia de software. O processo de obtenção de sinais vitais foi feito através de inteligência artificial para visão computacional utilizando uma câmara de computador. Esta metodologia requer que o utilizador esteja em estado de repouso durante a obtenção dos dados medidos. Esta investigação permitiu concluir que, no futuro, muitos processos médicos atuais provavelmente serão feitos online, sendo esta prática considerada extremamente útil na análise e tratamento de doenças contagiosas, ou de casos que requerem acompanhamento constante

    Strengthening of prism beam by using NSM technique with roots planted in concrete

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    This paper presents experimental results of four prismatic concrete reinforced beam and strengthened by NSM (Near surface mounted) FRP (Fiber Reinforced Polymer) reinforced technique, with additional roots planted in the concrete. The strengthening technique causes load capacity of beams to increase from (6%-8%).A decrease in mid-span deflection was also observed from (4%-5%).Using this technique gave increasing in flexural beam resistant under the same conditions and this increasing was also noted in shear beam resistant

    ELECTRO-MECHANICAL DATA FUSION FOR HEART HEALTH MONITORING

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    Heart disease is a major public health problem and one of the leading causes of death worldwide. Therefore, cardiac monitoring is of great importance for the early detection and prevention of adverse conditions. Recently, there has been extensive research interest in long-term, continuous, and non-invasive cardiac monitoring using wearable technology. Here we introduce a wearable device for monitoring heart health. This prototype consists of three sensors to monitor electrocardiogram (ECG), phonocardiogram (PCG), and seismocardiogram (SCG) signals, integrated with a microcontroller module with Bluetooth wireless connectivity. We also created a custom printed circuit board (PCB) to integrate all the sensors into a compact design. Then, flexible housing for the electronic components was 3D printed using thermoplastic polyurethane (TPU). In addition, we developed peak detection algorithms and filtering programs to analyze the recorded cardiac signals. Our preliminary results show that the device can record all three signals in real-time. Initial results for signal interpretation come from a recurrent neural network (RNN) based machine learning algorithm, Long Short-Term Memory (LSTM), which is used to monitor and identify key features in the ECG data. The next phase of our research will include cross-examination of all three sensor signals, development of machine learning algorithms for PCG and SCG signals, and continuous improvement of the wearable device
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