Smartphone App for Heart Rate Monitoring and Its Impact on Education Toward Industry 4.0

 This paper presents the development of Smartphone app for monitoring the heart rate, which used as a tool for life-based learning. The app featured life-based experiments for undergraduates' students through do-it-by-your-self activities. This app was developed with Firebase to create a heart rate monitoring system interface as well as a framework for life-based experiments. Also, this app had video features as learning resources. We used an experimental method to evaluate the proposed system of learning. The subjects were 30 undergraduate students who were separated into two groups. Based on our survey, student learning outcomes increased by 35% compared to conventional experiments.

The Design of Digital Heart Rate Meter Using Microcontroller

Cardiac tests generally use an electrocardiograph, the results of which are used by medical teams to diagnose heart conditions. Individual ECG examination is held in a health care institution so that it cannot be held independently, considering the high costs and the need for analysis by a specialist.  It is, therefore, necessary to have a functional and portable device to detect heart rate. The heart rate measuring device, equipped with a finger sensor, was designed for adults. The 15-second measurement interval showed the heart rate in one minute and the results were shown on an LCD. The minimum system circuit used ATMega 16

Implementação de um sistema de análise automática do ECG para identificação de episódios de fibrilação atrial utilizando uma plataforma de aquisição BITalino® e um smartphone Android™

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáAs arritmias cardíacas são distúrbios que afetam a frequência e/ou o ritmo dos batimentos cardíacos. O diagnóstico da maioria das arritmias é feito através da análise do eletrocardiograma (ECG), o qual consiste na representação gráfica da atividade elétrica do coração. A fibrilação atrial (AF) é um tipo de arritmia cardíaca, sendo a mais presente na população mundial. Se não identificada nos estágios iniciais, aumenta as chances de ocorrência de paragens cardíacas e acidente vascular cerebral, que constituem uma das maiores causas de morte no mundo. Uma das principais características presentes no sinal de ECG de indivíduos com AF é a irregularidade no ritmo cardíaco, ou seja, variação no intervalo entre dois picos R consecutivos. Pelo fato da AF muitas vezes se apresentar de forma assintomática, o uso de sistemas computacionais para a análise automática do sinal de ECG se apresenta como uma alternativa interessante para auxiliar o profissional de saúde no diagnóstico dessa arritmia. Nesse contexto, o presente trabalho trata da implementação de um sistema de análise automática do sinal de ECG para identificação de episódios de AF. O sistema consiste em uma etapa de aquisição do sinal realizada por um sensor de ECG BITalino conectado à plataforma BITalino (r)evolution Core, ambos desenvolvidos pela PLUX – Wireless Biosignals S.A. O sinal adquirido é transmitido via comunicação bluetooth para um smartphone com sistema operacional Android™. O processamento do sinal é feito através de um aplicativo desenvolvido através da IDE Android™ Studio. O sistema de análise foi desenvolvido através do software MATLAB® e, posteriormente, implementado no aplicativo com o auxílio da aplicação MATLAB Coder™ e da interface JNI. Em linhas gerais, o sistema de análise é composto por um algoritmo para detecção dos picos da onda R do sinal de ECG, seguido de uma etapa de extração de características, e outra de classificação. A característica utilizada na entrada do modelo de classificação foi o intervalo entre picos R consecutivos. O modelo de classificação utilizado é baseado em redes neurais do tipo LSTM (Long Short-Term Memory). Quando validado sobre os sinais do banco de dados MIT-BIH Atrial Fibrillation, o algoritmo de detecção dos picos da onda R apresentou valores médios de sensibilidade (Se) e preditividade positiva (P+) de 98,99% e 95,95%, respectivamente. O modelo de classificação utilizado apresentou exatidão média de 94,94% na identificação de episódios de AF.Cardiac arrhythmias are disorders that affect the rate and/or rhythm of the heartbeats. The diagnosis of most arrhythmias is made through the analysis of the electrocardiogram (ECG), which consists of a graphic representation of the electrical activity of the heart. Atrial fibrillation (AF) is a type of cardiac arrhythmia, being the most present in the world population. If not identified in the early stages, it increases the chances of cardiac arrest and stroke, which are one of the biggest causes of death in the world. One of the main characteristics present in the ECG signal of individuals with AF is the irregularity in the cardiac rhythm, that is, variation in the interval between two consecutive R peaks. Since AF is often asymptomatic, the use of computer systems for the automatic analysis of the ECG signal is an interesting alternative to assist health professionals in diagnosing this arrhythmia. In this context, this work deals with the implementation of an automatic ECG signal analysis system to identify AF episodes. The system consists of a signal acquisition step performed by a BITalino ECG sensor connected to the BITalino (r)evolution Core platform, both developed by PLUX – Wireless Biosignals SA. The acquired signal is transmitted via bluetooth communication to a smartphone with Android™ operating system. The signal processing is done through an application developed using the IDE Android™ Studio. The analysis system was developed using the MATLAB® software and later implemented in the application with the help of the MATLAB Coder™ application and the JNI interface. In general terms, the analysis system is composed of an algorithm for detecting the peaks of the R wave of the ECG signal, followed by a feature extraction step, and a classification step. The feature used in the entry of the classification model was the interval between consecutive R peaks (RRi). The classification model used is based on a LSTM neural network. When validated over the signals from the MIT-BIH Atrial Fibrillation database, the R-wave peak detection algorithm showed mean values of sensitivity (Se) and positive predictivity (P+) of 98.99% and 95.95%, respectively. The classification model used had an average accuracy of 94.94% in identifying AF episodes

Experimental Study of ECG Signal Transmission System Via a Coaxial Cable Line Using Duty-Cycle Modulation

This paper presents the first real and well tested prototyping duty cycle modulation (DCM) signal transmission system. The experimented system is applied to the transmission of ECG signals. It consists of an ECG signal acquisition system, a duty cycle modulation transmitter, a coaxial cable transmission medium and finally a simple low-pass filter as receiver. After a brief review of the literature highlighting the interest of this experiment, we analytically developed different parts of the proposed system. The experimental workbench and main results obtained are presented. These yield very good results since in addition to the high quality of ECG signal reconstitution, we manage to eliminate the power line interference induced during transmission. These results experimentally confirm the feasibility as well as new perspectives of using the proposed systems as a simple remote biomedical instrument. Cite as: Nguefack LT, Paune F, Mbihi J. 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