Biosignal PI, an Affordable Open-Source ECG and Respiration Measurement System

Abtahi, Farhad; Snäll, Jonathan; Aslamy, Benjamin; Abtahi, Shirin; Seoane, Fernando; Lindecrantz, Kaj

doi:10.3390/s150100093

Cited by 22 publications

(13 citation statements)

References 17 publications

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“…In the past few years, methods have been proposed to obtain basic medical characteristics from an ECG [ 2 , 3 , 4 , 5 , 6 ]. Laguna et al [ 7 ] presented an automatic waveform boundaries detection algorithm for multiple lead ECG signals.…”

Section: Introductionmentioning

confidence: 99%

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

Yang

Luo

Tao

et al. 2017

Sensors

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A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been no previous study on using CEEMDAN to de-noise ECG signals, to the authors’ best knowledge. In the proposed method, the original noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs) sorted from high to low frequency by CEEMDAN. Each IMF is then analyzed by the autocorrelation method to find out the first few high frequency IMFs containing random noise, and these IMFs should be de-noised by the wavelet threshold. The zero-crossing rate (ZCR) of all IMFs, including final residue, are computed, and the IMFs with ZCR less than a certain value are removed. Finally, the remaining IMFs are reconstructed to obtain the clean ECG signal. The proposed algorithm is validated through experiments using the MIT–BIH ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently.

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Section: Introductionmentioning

confidence: 99%

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

Yang

Luo

Tao

et al. 2017

Sensors

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“…Compared with the results of other studies with the much more expensive ADAS tool for three leads [32], the results in Fig. 3 shown there was no signi cant difference in the quality of the recorded signal.…”

Section: Discussionmentioning

confidence: 59%

Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

Oktivasari¹,

Ishartati²,

Riandini³

et al. 2021

Preprint

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A simple system, a low-cost, fully automated, and design for monitoring RR interval Electrocardiography (ECG) signal described in this paper. The platform, named Simple Low-Cost Electrocardiography System (SLES), is capable of monitoring RR interval and R peaks in 3 lead standards. The system is in .exe format, so it can be easily installed on a computer. The system's goal is to design a fully integrated system for measuring a characteristic of Heart Rate Variability (HRV) parameters for various applications in heart signal research and education. The ECG signal is analog filtered and amplified and processed from analog to digital. Eventually, the ECG signal will be shown on the monitor after digital filtering. The data obtained from the ECG will accurately reflect the status of human heart health. The system has the benefits of small volume, low power consumption, low cost, and real-time operation. All design and development reports, files, and system software will be given non-commercial use online on https://github.com/oktivasari.

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“…ECG is perhaps one of the most widely prototyped [72][73][74][75][76][77][78][79][80][81] and well-documented medical devices using open electronics. ECG is a widely used for medical diagnosis and monitoring.…”

Section: Electrocardiograph (Ecg/ekg) Electroencephalogram (Eeg) and Neurological Monitoringmentioning

confidence: 99%

Open Electronics for Medical Devices: State-of-Art and Unique Advantages

Pandey

Vora

2019

Electronics

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A wide range of medical devices have significant electronic components. Compared to open-source medical software, open (and open-source) electronic hardware has been less published in peer-reviewed literature. In this review, we explore the developments, significance, and advantages of using open platform electronic hardware for medical devices. Open hardware electronics platforms offer not just shorter development times, reduced costs, and customization; they also offer a key potential advantage which current commercial medical devices lack—seamless data sharing for machine learning and artificial intelligence. We explore how various electronic platforms such as microcontrollers, single board computers, field programmable gate arrays, development boards, and integrated circuits have been used by researchers to design medical devices. Researchers interested in designing low cost, customizable, and innovative medical devices can find references to various easily available electronic components as well as design methodologies to integrate those components for a successful design.

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Biosignal PI, an Affordable Open-Source ECG and Respiration Measurement System

Cited by 22 publications

References 17 publications

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

Open Electronics for Medical Devices: State-of-Art and Unique Advantages

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