A microcontroller based system for real-time heart rate estimation from ECG signal
01 Dec 2012-pp 1020-1025
TL;DR: An algorithm for real time detection QRS complex from ECG signal for computation of heart rate from R peak locations based on Atmel 89C51 microcontroller is illustrated.
Abstract: This paper illustrates an algorithm for real time detection QRS complex from ECG signal for computation of heart rate. The algorithm is implemented on a standalone embedded system based on Atmel 89C51 microcontroller. Synthetic ECG is generated using Physionet data through the parallel port (LPT1) of a personal computer and delivered to the embedded system. During an initial training period of first 1500 samples, some amplitude and slope based signatures are learned to form a rule base, which are used for detecting the subsequent QRS regions accurately. An average sensitivity of 97.82% and predictivity of 98.35% respectively are obtained from MIT BIH arrhythmia data. From the detected successive R peak locations heart rate has been computed.
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01 Jan 2014
TL;DR: The design process of a low cost and portable microcontroller based heart-rate counting system for monitoring heart condition that can be implemented with off-the-shelf components and results obtained when compared to those obtained from the manual test involving counting of heart rate was found satisfactory.
Abstract: This article describes the design process of a low cost and portable microcontroller based heart-rate counting system for monitoring heart condition that can be implemented with off-the-shelf components. The raw heart-rate signals were collected from finger using IR TX-RX (Infrared Transmitter and Receiver pair) module which was amplified in order to convert them to an observable scale. The inherent noise signal was then eliminated using a low pass filter. These signals were counted by a microcontroller module (ATmega8L) and displayed on the LCD panel. An algorithm has been developed which was programmed into the microcontroller to run the proposed heart rate counting system. The results obtained using the developed device when compared to those obtained from the manual test involving counting of heart rate was found satisfactory. The proposed system is applicable for family, hospital, clinic, community medical treatment, sports healthcare and other medical purposes. Also, fit for the adults and the pediatrics. However, presented method in the developed system needs further investigation and need more functionality, which may be useful to
14 citations
16 Mar 2015
TL;DR: This paper demonstrates a simple, efficient and low cost ECG and heart beat measurement system using PIC18F4550 microcontroller which includes LT1028 OP AMP IC based signal conditioning unit.
Abstract: This paper demonstrates a simple, efficient and low cost ECG and heart beat measurement system using PIC18F4550 microcontroller. The design includes LT1028 OP AMP IC based signal conditioning unit. Detected ECG signal inside microcontroller is send to a Lab VIEW based GUI platform with USB CDC protocol and also tested with RS-232 protocol. Signal plotting and heart beat calculation is done inside LabVIEW program. ECG signal can also be viewed from PWM port of PIC18F4550 microcontroller via a low pass filtering stage. The whole design is simulated successfully with Proteus circuit simulator. The ECG signal is extracted in audio format (.WAV) from LabVIEW biomedicai toolkit for ECG simulator keeping 1.2 mV upper voltage limit and −0.4 mV lower voltage limit. The audio file is given as the input to the circuit in Proteus. USB simulation is done with the help of Proteus Virtual USB and Microchip USB CDC driver. RS-232 virtual serial port is created using Eltima virtual serial port software. MPLAB IDE is used as microcontroller program compiler.
7 citations
Cites background from "A microcontroller based system for ..."
...There are several highly expensive and inexpensive ECG measuring devices available, but their performance and accuracy gets reduced as switched to inexpensive devices....
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01 Aug 2016
TL;DR: A microprocessor system interfaced with an ADC and a pulse sensor is implemented in FPGA hardware and the system is able to achieve good results as compared to the direct measurement using oscilloscope.
Abstract: The ability to perform real-time signal analysis on physiological signal is important in today's technology. This allows physician to monitor their patient's health condition and provide the necessary action immediately. To obtain accurate measurement of this signal, proper signal interface between the patient body and the signal analyzer is crucial. In this work, a microprocessor system interfaced with an ADC and a pulse sensor is implemented in FPGA hardware. An experiment is carried out to analyze the performance of the system by executing real-time data acquisition system on various test data. From the experiment results, the system is able to achieve good results as compared to the direct measurement using oscilloscope. Based on the power analysis results, the proposed system has total power dissipation around 200.63 mW to 201.17 mW.
4 citations
Cites methods from "A microcontroller based system for ..."
...Block diagram of the microcontroller system [4]...
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...A real-time ECG signal heart rate estimator using a microcontroller based system is presented by the authors in paper [4]....
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Journal Article•
TL;DR: A new ARM processor-based system-on-chip architecture is presented for heart rate monitoring applications, which is implemented in Altera Cyclone II FPGA and its functionality is verified by monitoring the heart rate of a student.
Abstract: Data acquisition systems are used for data or signal monitoring in many applications. In the biomedical field, signals such as from an ECG, EEG or PPG are monitored using data acquisition systems. A review of several ARM processor-based data acquisition systems for biomedical applications is presented here. A new ARM processor-based system-on-chip architecture is presented for heart rate monitoring applications, which is implemented in Altera Cyclone II FPGA. A PPG-based sensor is used to carry out heart rate monitoring. An experiment is carried out to verify the functionality of the system by monitoring the heart rate of a student. Based on the results, the monitored heart rate is within the average range for heart rate. According to the compilation report, the total logic elements used were 4740, and the total power estimated for the system in Cyclone II FPGA is 199.59mW.
1 citations
Cites background from "A microcontroller based system for ..."
...the biological signals in memory, transfer them to a personal computer (PC), and carry out processing on the data within the system [1-3]....
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12 Oct 2022
TL;DR: In this article , a real-time postoperative heart disease patient monitoring system based on the Internet of Things (IoT) was created, which enables the user to measure many heart parameter indices at the same time and present the findings in a mobile application.
Abstract: The heart is an important organ of the human body because it functions as a pump, giving oxygen and nutrients to the body while also carrying metabolic waste such as carbon dioxide to the lungs. As a result, the health of the heart is vital in the human body. Cardiovascular disease (CVD) refers to a group of ailments that affect the heart or blood arteries and is one of the leading causes of death globally. Adult cardiovascular disease (CVD) prevalence is increasing in Bangladesh, as it is in many other nations. CVD is often regarded as a serious public health problem on a global scale. Cardiovascular instability is responsible for the majority of postoperative problems and significantly increases postoperative mortality compared to intraoperative mortality. The elderly patient with pre-existing cardiac disease has a greater risk of surgical complications. To avoid this danger, it is critical to monitor the heart. The majority of previously established systems for monitoring heart problems are incapable of providing cost-effective real-time heart monitoring while also detecting real-time environmental conditions. To solve this problem, a real-time postoperative heart disease patient monitoring system based on the Internet of Things (IoT) was created. The developed product is cost-effective, simple to use, and portable. It will enable the user to measure many heart parameter indices at the same time and present the findings in a mobile application. The system is constructed in such a manner that it is accessible to anybody. Numerous analog sensors are utilized in conjunction with the Arduino UNO to monitor various heart characteristics. The electrocardiography (ECG) sensor, the pulse oximeter sensor, the temperature sensor, and the humidity sensor are all used to monitor the heart in this system. A temperature and humidity sensor are utilized to determine the ambient state. A Bluetooth module is utilized to link the Arduino to a mobile device in order to monitor postoperative cardiac disease patients' heart activity characteristics. The approach was investigated and validated with a number of individuals. The system continuously monitors many people in order to assess and identify heart activity in real-time. The findings are shown on the serial monitor of the Arduino as well as on the mobile application. The data processing capabilities of the system may reveal cardiac activity in some of the people being tracked, as well as the cause of heart disease. This study also examines individuals who are not at risk of developing cardiac problems based on the data gathered. This study will aid everyone in gaining a better understanding of postoperative cardiac issues, as well as how to recognize and avoid them.
1 citations
References
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TL;DR: A new approach to ECG arrhythmia analysis is described, based on hidden Markov modeling (HMM), a technique successfully used since the mid 1970s to model speech waveforms for automatic speech recognition.
Abstract: A new approach to ECG arrhythmia analysis is described. It is based on hidden Markov modeling (HMM), a technique successfully used since the mid 1970s to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The HMM approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum-likelihood reestimation algorithm. Initial results suggest that this approach can provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat, including the P-wave. >
527 citations
TL;DR: The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination.
Abstract: Accurate QRS detection is an important first step for the analysis of heart rate variability Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (9968% sensitivity, 9963% positive predictivity) but also the largest time error The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed
407 citations
"A microcontroller based system for ..." refers methods in this paper
...Initial approached involved digital filters and derivative based algorithms [1]–[4]....
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Journal Article•
TL;DR: The authors used an adaptive multilayer perceptron structure to model the nonlinear background noise so as to enhance the QRS complex, providing more reliable detection of QRS complexes even in a noisy environment.
282 citations
"A microcontroller based system for ..." refers methods in this paper
...Soft computational techniques like Hidden Markov model, Artificial neural Networks, genetic Algorithms, Support Vector Machines and many more have been used for more accurate QRS detections [6]-[9]....
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TL;DR: An approach to QRS complex detection based on mathematical morphology is presented, which works as a peak-valley extractor and it is controlled by the shape of the structuring element, resulting in very fast execution times.
Abstract: An approach to QRS complex detection based on mathematical morphology is presented. QRS complexes are detected by the application of a simple morphological operator. This operator works as a peak-valley extractor and it is controlled by the shape of the structuring element. A set (horizontal line segment) is used as a structuring element, resulting in very fast execution times. The accuracy of this approach has been tested using a standard ECG library; a sensitivity of 99.38% and a positive predictivity of 99.48% have been achieved. >
213 citations
"A microcontroller based system for ..." refers methods in this paper
...A morphological operator based peak value extractor is used for QRS detector [5]....
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TL;DR: In this article, a five-step digital filter was developed which removes components other than those of QRS complex from the recorded electrocardiogram (ECG), and the final step of the filter produces a square wave whose on-intervals correspond to the segments with QRS complexes in the original wave.
Abstract: The five step digital filter has been developed which removes components other than those of QRS complex from the recorded electrocardiogram (ECG). The final step of the filter produces a square wave whose on-intervals correspond to the segments with QRS complexes in the original wave.
205 citations