A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks

Abstract

This study proposes a Raspberry Pi-based system for the diagnosis of heart valve diseases as a primary tool to improve the diagnostic accuracy of physicians. The proposed system is able to detect and classify nine common valvular heart cases encompassing eight types of heart valve diseases as well as the normal case of valves. The design and development of the proposed system are mainly divided into two phases, namely development of a disease classification approach, and design and implementation of the diagnostic hardware system. The developed disease classification approach is comprised of five stages, namely obtaining phonocardiogram (PCG) signals, preprocessing, segmentation using a proposed automatic algorithm, feature extraction in three domains (time, frequency, and wavelet decomposition domains) and classification using a backpropagation neural network. The hardware of the diagnostic system consists of a PCG signal acquisition module connected to a processing and displaying unit, which is represented by a Raspberry Pi connected to a touch screen. Where the developed disease classification approach is implemented in

An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring

“…In conclusion, our group is the first to develop a comprehensive ECG monitoring device utilizing Raspberry Pi. A recent study conducted by Joukhadar et al [32] introduced a portable system based on Raspberry Pi for diagnosing heart valve diseases. However, it is important to note that their monitoring system is specifically designed to measure the phonocardiogram (PCG) signal, not the ECG signal.…”

“…Patil et al [31] proposed a real time ECG on internet using Raspberry Pi, they built a simple circuit on a bread board and displayed the results on an Oscilloscope. Recently, Joukhadar et al [32] proposed a portable raspberry pi-based system for heart valve diseases diagnosis using automatic segmentation and artificial neural network. The hardware of the proposed system consists of a phonocardiogram (PCG) signal acquisition module and a raspberry pi connected to a touch screen to process and display the data.…”

Automatic shoe drying oven integrated with Raspberry Pi Cloud system for advanced footwear industry

“…Designing an automatic shoe drying oven equipped with a Raspberry PI with an LCD screen, fan, multilevel shelves, and electric heater temperature control to provide maximum and even heating of leather shoes when gluing and drying leather shoes. Raspberry PI aims to combine and collect production process data in cloud storage in the form of a database, which will then be sent and displayed directly to the operator [12]. Copetti et al [13] The two-dimensional model developed can encourage digitalization and make it easier for MSMEs to face the changes that occur, the government has increased the ease of access and transferred technology to MSME players so that they can survive in business competition.…”

Аналіз Сучасних Мікроконтролерів Для Вирішення Біоінженерних Задач Та Використанням Інтернету Речей

“…Для багатьох проектів тактові цикли діляться між такими операціями, як обчислення, комунікація по I2C, читання та запис в контакти та регістри, а також багатьма іншими операціями.Плати до прототипування Raspberry Pi, є комп'ютерами компактного розміру, майже розміром з кредитну картку (рис.3). Їх легко можна підключити до монітора, комп'ютера або телевізора, вони сумісні зі стандартною клавіатурою та мишею[8]. Навіть користувачі з обмеженим технічним досвідом використовують ці платформи для налаштування своїх цифрових медіа-систем та систем спостереження.…”