Wireless 3-lead ECG system with on-board digital signal processing for ambulatory monitoring

Buxi, Dilpreet; Berset, Torfinn; Hijdra, Martijn; Tutelaers, M.; Geng, Di; Hulzink, Jos; Noorloos, M. van; Romero, Iñaki; Torfs, Tom; Helleputte, Nick Van

doi:10.1109/biocas.2012.6418435

Cited by 14 publications

(2 citation statements)

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“…A wireless system performing on-board processing is presented in (Buxi et al, 2012). The system is equipped with a custom DSP and a TI MSP430 microcontroller.…”

Section: Related Workmentioning

confidence: 99%

Multiple Biopotentials Acquisition System for Wearable Applications

Benatti¹,

Milosevic²,

Tomasini³

et al. 2015

Proceedings of the International Conference on Biomedical Electronics and Devices

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Wearable devices for monitoring vital signs such as heart-rate, respiratory rate and blood pressure are demonstrating to have an increasing role in improving quality of life and in allowing prevention for chronic cardiac diseases. However, the design of a wearable system without reference to ground potential requires multi-level strategies to remove noise caused from power lines. This paper describes a bio-potential acquisition embedded system designed with an innovative analog front-end, showing the performance in EEG and ECG applications and the comparison between different noise reduction algorithms. We demonstrate that the proposed system is able to acquire bio-potentials with a signal quality equivalent to state-of-the-art bench-top biomedical devices and can be therefore used for monitoring purpose, with the advantages of a low-cost low-power wearable devices.

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“…A wireless system performing on-board processing is presented in (Buxi et al, 2012). The system is equipped with a custom DSP and a TI MSP430 microcontroller.…”

Section: Related Workmentioning

confidence: 99%

Multiple Biopotentials Acquisition System for Wearable Applications

Benatti¹,

Milosevic²,

Tomasini³

et al. 2015

Proceedings of the International Conference on Biomedical Electronics and Devices

View full text Add to dashboard Cite

show abstract

“…The number of people who suffer from cardiac diseases is increasing every day. This disease is the leading cause of death in developed countries [1][2][3]. Electrocardiography is a method of recording and studying the electric fields that are generated during the work of the heart.…”

Section: Introductionmentioning

confidence: 99%

System for Neural Network Determination of Atrial Fibrillation on ECG Signals with Wavelet-Based Preprocessing

2021

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Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals.

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