Adaptive Wavelet Thresholding for Noise reduction in Electrocardiogram (ECG) Signals

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array

Scientific Reports ◽

10.1038/s41598-021-84971-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yanfei Yang ◽

Mingzhu Xu ◽

Aimin Liang ◽

Yan Yin ◽

Xin Ma ◽

...

Keyword(s):

Independent Component Analysis ◽

Data Acquisition ◽

Current Distribution ◽

Spin Exchange ◽

Healthy Human ◽

Ecg Signals ◽

Mode Decomposition ◽

Magnetometer Array ◽

Data Acquisition And Processing ◽

Electrocardiogram Ecg

AbstractIn this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

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Novel Stable Capacitive Electrocardiogram Measurement System

Sensors ◽

10.3390/s21113668 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3668

Author(s):

Chi-Chun Chen ◽

Shu-Yu Lin ◽

Wen-Ying Chang

Keyword(s):

Measurement System ◽

Input Resistance ◽

Voltage Divider ◽

Motion Artifact ◽

Guard Ring ◽

Capacitive Sensing ◽

Ecg Signals ◽

Optimal Input ◽

Electrocardiogram Ecg ◽

Mode Interference

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R–R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life.

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Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

Applied Sciences ◽

10.3390/app11031125 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1125

Author(s):

Htet Myet Lynn ◽

Pankoo Kim ◽

Sung Bum Pan

Keyword(s):

Short Term Memory ◽

Feature Learning ◽

Biometric Authentication ◽

Qrs Complex ◽

Data Types ◽

Ecg Signals ◽

Data Independent Acquisition ◽

Deep Networks ◽

Classification Tasks ◽

Electrocardiogram Ecg

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

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Digitization of electrocardiogram (ECG) signals using delta-sigma modulation

Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286) ◽

10.1109/iembs.1998.746986 ◽

2002 ◽

Cited By ~ 8

Author(s):

S.W. Leung ◽

Y.T. Zhang

Keyword(s):

Ecg Signals ◽

Delta Sigma ◽

Delta Sigma Modulation ◽

Electrocardiogram Ecg

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