Simple real-time QRS detector with the MaMeMi filter

AbstractDetection of QRS complexes in ECG signals is required to determine heart rate, and it is an important step in the study of cardiac disorders. ECG signals are usually affected by noise of low and high frequency. To improve the accuracy of QRS detectors several methods have been proposed to filter out the noise and detect the characteristic pattern of QRS complex. Most of the existing methods are at a disadvantage from relatively high computational complexity or high resource needs making them less optimized for its implementation on portable embedded systems, wearable devices or ultra-low power chips. We present a new method to detect the QRS signal in a simple way with minimal computational cost and resource needs using a novel non-linear filter

Influence of the Main Filter on QRS-amplitude and Duration in Human Electrocardiogram.

Accurate measurement of electrocardiograms (ECG) is critical for effective diagnosis of patient’s cardiac functions. Detailed examination of filters’ effects on ECG accuracy, reproducibility and robustness covering a wide range of available commercial products can provide valuable information on the relationship between quality and effectiveness of filters, and assessments of patients’ cardiac functions. In this study, ECG device with 12 leads and built-in filters used for ECG measurements was assessed on human volunteers. Results showed that with respect to measuring QRS wave duration and R-amplitude variation, there was a 4 % inaccuracy when the main filter was ON and OFF, and R-amplitude variation was most pronounced in the V4 lead. Accordingly, variability of R-amplitude and length of QRS wave can be reduced by the use of appropriate lead, and filter activation during the ECG assessment

Real-time ECG monitoring

Kľúčovým prvkom pri analyzovaní EKG signálu je detekcia QRS komplexov. QRS komplexy sú najvýraznejšia zložka signálu. V tejto práci je zahrnutý stručný prehľad metód detekcie QRS komplexov v reálnom čase. Na základe rešerše je vybraný vhodný detekčný algoritmus, ktorý je realizovaný v programovom prostredí LabVIEW. Práca je taktiež zameraná na problematiku srdečných arytmií. V zostrojenom algoritme sú detekované isté typy arytmií. Algoritmus je otestovaný na MIT/BIH arrhytmia database.The key element of ECG signal analysis is the detection of the QRS complex. The QRS complex is the most significant part of the signal. In this work, there is a short summary of methods used for QRS detection in real time. Based on previous research, the detection alghorithm is chosen and created in LabVIEW. The paper is also focused on cardiac arrhytmias. Created alghorithm is used for detecion certain types of arrhytmia. Alghorithm was tested on MIT/BIH arrhytmia database.

Verification and comparison of MIT-BIH arrhythmia database based on number of beats

The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database

Simulación de conducción con sensores fisiológicos para monitorización del estado del conductor

En los últimos años, se han usado simuladores de conducción en muchas investigaciones. Por ejemplo, se ha probado su idoneidad para evaluar el comportamiento de los conductores y como medio de aprendizaje de hábitos de conducción segura y eficiente. La inclusión en los simuladores de sensores fisiológicos que monitorizan al conductor les confiere de un potencial añadido para un estudio multifactorial de pruebas de conducción. En este Trabajo Fin de Grado, se ha desarrollado y adaptado una aplicación Android que monitorizará el estado del conductor con los sensores fisiológicos de la plataforma Shimmer, que incluye un sensor ECG (Electrocardiograma), EMG (Electromiograma) y GSR (Respuesta Galvánica de la piel), junto a un acelerómetro y giróscopo. La aplicación realiza procesados en tiempo real para determinar cuándo el estado del conductor no es propio de una conducción segura. Los datos almacenados durante las simulaciones también permiten realizar un análisis offline de la conducción.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by Vicerrectorado de Innovación, University of Alicante, Spain (Vigrob)