scholarly journals Method of Extracting Fetal QRS for Single-Lead Abdominal ECG Signal Based On Combined Compressive Sensing and Clustering Analysis

2021 ◽  
Author(s):  
Yapeng Dong ◽  
Wenbo Ju ◽  
Jiancheng Hu ◽  
Lijuan Hu ◽  
Dayu Ding ◽  
...  
Keyword(s):  
Low Power ◽  
Compressive Sensing ◽  
Clustering Analysis ◽  
Single Channel ◽  
Reducing Power ◽  
Motion Artifacts ◽  
Data Sets ◽  
Non Invasive ◽  
Fetal Ecg ◽  
Electrocardiogram Ecg

Abstract Objective: The fetal electrocardiogram (ECG) is an objective index that reflects a fetus’s health status. Non-invasive abdominal ECG (aECG) was obtained by placing silicone electrodes on pregnant women’s abdominal wall. However, fetal QRS (fQRS) extraction is very challenging due to maternal ECG interference, motion artifacts, and other noise. Approach: This paper introduces a new single-lead non-invasive fQRS extraction method based on compressive sensing and clustering analysis. This method can be applied to portable, low-power remote fetal ECG (fECG) acquisition equipment based on the Internet of Things (IoT). It is mainly divided into the following steps: (1) optimal component extraction of single-channel signal based on compressive sensing theory; (2) location of maternal QRS (mQRS) using the clustering method based on extreme value; (3) maternal ECG (mECG) elimination; (4) The preliminary location of fQRS based on double clustering and the correction of fQRS based on fetal RR interval. Main results: The new algorithm proposed in this paper is verified on two publicly available data sets. The averages of these indicators are Se=98.53%, PPV=98.28%, ACC=96.95%, F1=98.43% for the Silesia datasets and Se=97.59%, PPV=97.63%, ACC=95.44%, F1=97.62% for the Challenge datasets A. Significance: The results show that it is feasible and reliable to locate fQRS from a single-channel aECG signal under the condition of reducing power consumption. It lays a foundation for implementing the low-power wireless transmission of fECG signal and remote fetal heart rate (FHR) monitoring based on the IoT.

scholarly journals Quantifying Signal Quality From Unimodal and Multimodal Sources: Application to EEG With Ocular and Motion Artifacts

2021 ◽  
Vol 15 ◽  
Author(s):  
David O. Nahmias ◽  
Kimberly L. Kontson
Keyword(s):  
Motion Artifacts ◽  
Signal Quality ◽  
Data Sets ◽  
Bayesian Decision ◽  
Artifact Removal ◽  
Non Invasive ◽  
Eeg Data ◽  
Recorded Data ◽  
Electrophysiological Signal ◽  
Ocular Artifacts

With prevalence of electrophysiological data collected outside of the laboratory from portable, non-invasive modalities growing at a rapid rate, the quality of these recorded data, if not adequate, could affect the effectiveness of medical devices that depend of them. In this work, we propose novel methods to evaluate electrophysiological signal quality to determine how much of the data represents the physiological source of interest. Data driven models are investigated through Bayesian decision and deep learning-based methods to score unimodal (signal and noise recorded on same device) and multimodal (signal and noise each recorded from different devices) data, respectively. We validate these methods and models on three electroencephalography (EEG) data sets (N = 60 subjects) to score EEG quality based on the presence of ocular artifacts with our unimodal method and motion artifacts with our multimodal method. Further, we apply our unimodal source method to compare the performance of two different artifact removal algorithms. Our results show we are able to effectively score EEG data using both methods and apply our method to evaluate the performance of other artifact removal algorithms that target ocular artifacts. Methods developed and validated here can be used to assess data quality and evaluate the effectiveness of certain noise-reduction algorithms.

scholarly journals A Fetal ECG Monitoring System Based on the Android Smartphone

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 446 ◽  
Author(s):  
Li Yuan ◽  
Yanchao Yuan ◽  
Zhuhuang Zhou ◽  
Yanping Bai ◽  
Shuicai Wu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Sample Entropy ◽  
Ecg Monitoring ◽  
Fixed Point Algorithm ◽  
Ecg Signals ◽  
Non Invasive ◽  
Fetal Ecg ◽  
Fastica Algorithm ◽  
Electrocardiogram Ecg

In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs.

Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System

2016 ◽  
Vol 25 (09) ◽  
pp. 1650103 ◽  
Author(s):  
Jing Hua ◽  
Hua Zhang ◽  
Jizhong Liu ◽  
Junlong Zhou
Keyword(s):  
Compressive Sensing ◽  
Single Channel ◽  
Temporal Correlation ◽  
Reconstruction Algorithm ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Reconstruction Performance ◽  
Low Energy Consumption ◽  
Spatio Temporal ◽  
Electrocardiogram Ecg

Due to the capacity of compressing and recovering signal with low energy consumption, compressive sensing (CS) has drawn considerable attention in wireless telemonitoring of electrocardiogram (ECG) signals. However, most existing CS methods are designed for reconstructing single channel signal, and hence difficult to reconstruct multichannel ECG signals. In this paper, a spatio-temporal sparse model-based algorithm is proposed for the reconstruction of multichannel ECG signals by not only exploiting the temporal correlation in each individual channel signal, but also the spatial correlation among signals from different channels. In addition, a dictionary learning (DL) approach is developed to enhance the performance of the proposed reconstruction algorithm by using the sparsity of ECG signals in some transformed domain. The approach determines a dictionary by learning local dictionaries for each channel and merging them to form a global dictionary. Extensive simulations were performed to validate the proposed algorithms. Simulation results show that the proposed reconstruction algorithm has a better performance in recovering multichannel ECG signals as compared to the benchmarking methods. Moreover, the reconstruction performance of the algorithm can be further improved by using a dictionary matrix, which is obtained from the proposed DL algorithm.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

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.

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

WBAN Based Long Term ECG Monitoring

2013 ◽  
Vol 1 (3) ◽  
pp. 20-37 ◽  
Author(s):  
Marius Rosu ◽  
Sever Pasca
Keyword(s):  
Low Power ◽  
Sensor Nodes ◽  
Ecg Signal ◽  
Area Network ◽  
Wireless Sensor Nodes ◽  
Medication Process ◽  
Remote Healthcare ◽  
Electrocardiogram Ecg ◽  
Supply Information

Healthcare solutions using anytime, and anywhere remote healthcare surveillance devices, have become a major challenge. The patients with chronic diseases who need only therapeutic supervision are not advised to occupy a hospital bed. Using Wearable Wireless Body/Personal Area Network (WWBAN), intelligent monitoring of heart can supply information about medical conditions. Electrocardiogram (ECG) is the core reference in the diagnosis and medication process. An approach on healthcare solution WBAN based, for real-time ECG signal monitoring and long-term recording will be presented. Low-power wireless sensor nodes with local processing and encoding capabilities in order to achieve maximum mobility and flexibility are our main goal. ZigBee wireless technology will be used for transmission. Sensor device will be programmed to process locally the ECG signal and to raise an alert. Low-power and miniaturization are essential physical requirements.

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