ECG-based wireless home infant apnoea monitor

Fraiwan, Luay; Al-Bataineh, Osama M.; Ma’touq, Jumana; Haddad, S.G.; Bani-Amer, Maryam J

doi:10.1080/03091900802565718

Cited by 8 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An example of its importance is the prevention of sudden cardiac arrest in newborns with long QT syndrome (Saul et al, 2014;Brockmeier, Nazal, and Sreeram, 2016). Multiple technologies are being developed for newborn remote long-term monitoring applications utilizing ECG and other vital signs such as respiratory activity and temperature (Aviles-Espinosa et al, 2019;Fraiwan et al, 2009;Taylor et al, 2022;Lin et al, 2019;Anton et al, 2022;Henry et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Abdou,

Krishnan

2024

Front. Signal Process.

View full text Add to dashboard Cite

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Abdou,

Krishnan

2024

Front. Signal Process.

View full text Add to dashboard Cite

show abstract

Remote sensing of physiological signs using a machine vision system

Al-Naji

Gibson

Chahl

2017

Journal of Medical Engineering & Technology

View full text Add to dashboard Cite

The aim of this work is to remotely measure heart rate (HR) and respiratory rate (RR) using a video camera from long range (> 50 m). The proposed system is based on imperceptible signals produced from blood circulation, including skin colour variations and head motion. As these signals are not visible to the naked eye and to preserve the signal strength in the video, we used an improved video magnification technique to enhance these invisible signals and detect the physiological activity within the subject. The software of the proposed system was built in a graphic user interface (GUI) environment to easily select a magnification system to use (colour or motion magnification) and measure the physiological signs independently. The measurements were performed on a set of 10 healthy subjects equipped with a finger pulse oximeter and respiratory belt transducer that were used as reference methods. The experimental results were statistically analysed by using the Bland-Altman method, Pearson's correlation coefficient, Spearman correlation coefficient, mean absolute error, and root mean squared error. The proposed system achieved high correlation even in the presence of movement artefacts, different skin tones, lighting conditions and distance from the camera. With acceptable performance and low computational complexity, the proposed system is a suitable candidate for homecare applications, security applications and mobile health devices.

show abstract