scholarly journals Unreadable Segment Recognition of Single-Lead ECG Signals Based on XGBoost: Fuision of Shannon Energy Envelope and Empirical Mode Decomposition

2021 ◽  
Author(s):  
Hanshuang Xie ◽  
Jiayi Yan ◽  
Huaiyu Zhu ◽  
Qineng Cao ◽  
Yamin Liu ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Error Rate ◽  
Wearable Devices ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Shannon Energy ◽  
Analysis System ◽  
Fold Cross Validation

The quality of ECG signals is commonly affected by severe noise, especially for the single-lead ECG signals acquired from long-term wearable devices. Recognizing and ignoring these interfered signals can reduce the error rate of automatic ECG analysis system, and in addition, improve the efficiency of cardiologists. Based on XGBoost classifier, we propose an unreadable ECG segment recognition method using features extracted through Shannon Energy Envelope (SEE) and Empirical Mode Decomposition (EMD). An unreadable CarePatchTM ECG patch database is established, containing 8169 readable segments and 6114 unreadable segments with a length of 10 seconds. The XGBoost with 5-fold cross-validation is applied and obtained an accuracy of 99.51+/-0.15%. In conclusion, SSE and EMD features contribute to the unreadable segments recognition and alleviate the misdiagnosis of abnormal rhythms.

scholarly journals Comparison of Active Electrode Materials for Non-Contact ECG Measurement

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3585 ◽  
Author(s):  
Shun Peng ◽  
Ke Xu ◽  
Wei Chen
Keyword(s):  
Electrode Materials ◽  
Ecg Monitoring ◽  
Active Electrode ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Flexible Printed Circuit ◽  
Conductive Textile ◽  
Quality Indexes

For long-term and more convenience electrocardiograph (ECG) monitoring, an active- electrode-based ECG monitoring system, which can measure ECG through clothes, is proposed in this paper. The hardware of the system includes active electrodes, signal processing and data transmission modules and the software mainly includes a denoising algorithm based on empirical mode decomposition (EMD). Then the proposed system was verified using the comparison of the ECG signals measured synchronously by active electrodes and Ag/AgCl electrodes. In addition, three flexible materials, including conductive textile, copper foil tape and a flexible printed circuit (FPC) are developed for the sensing layer with active electrodes. To compare the performance of these three materials for the sensing layer, the ECG signals of 10 subjects were measured by different materials in three postures and several indexes for quality evaluation were calculated. Results show that effective and clear ECG waveforms can be measured by all three kinds of materials and the quality of ECG signals measured by FPC is the best by conducting a significant t-test for signal quality indexes of three materials.

scholarly journals Empirical mode decomposition of long-term polar motion observations

2014 ◽  
Vol 59 (2) ◽  
pp. 200-211 ◽  
Author(s):  
Giorgio Spada ◽  
Gaia Galassi ◽  
Marco Olivieri
Keyword(s):  
Empirical Mode Decomposition ◽  
Polar Motion ◽  
Mode Decomposition

scholarly journals Biomedical Photoacoustic Imaging Optimization with Deconvolution and EMD Reconstruction

2018 ◽  
Vol 8 (11) ◽  
pp. 2113 ◽  
Author(s):  
Chengwen Guo ◽  
Yingna Chen ◽  
Jie Yuan ◽  
Yunhao Zhu ◽  
Qian Cheng ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Empirical Mode Decomposition ◽  
Photoacoustic Imaging ◽  
Optimization Method ◽  
Positive Polarity ◽  
Image Optimization ◽  
Mode Decomposition ◽  
Phantom Studies ◽  
Micro Structures

A photoacoustic (PA) signal of an ideal optical absorbing particle is a single N-shape wave. PA signals are a combination of several individual N-shape waves. However, the N-shape wave basis leads to aliasing between adjacent micro-structures, which deteriorates the quality of final PA images. In this paper, we propose an image optimization method by processing raw PA signals with deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent deconvolution kernel, which is measured in advance. EMD is subsequently adopted to further process the PA signals adaptively with two restrictive conditions: positive polarity and spectrum consistency. With this method, signal aliasing is alleviated, and the micro-structures and detail information, previously buried in the reconstructing images, can now be revealed. To validate our proposed method, numerical simulations and phantom studies are implemented, and reconstructed images are used for illustration.

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