SVD Analysis on Reduced 3-Lead ECG Data

2015 ◽  
pp. 253-260 ◽  
Author(s):  
Sibasankar Padhy ◽  
S. Dandapat
Keyword(s):  
Svd Analysis ◽  
Ecg Data

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

ECG Data Compression Using Modified Cortes

2015 ◽  
Vol 3 (2) ◽  
pp. 13-18
Author(s):  
Himani Tiwari ◽  
◽  
V. K. Giri ◽  
Keyword(s):  
Data Compression ◽  
Ecg Data Compression ◽  
Ecg Data

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

Hardware challenges with an anticoagulation strategy guided by detection of atrial fibrillation by an implantable loop recorder

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Mittal ◽  
D Brenner ◽  
S Oliveros ◽  
A Bhatt ◽  
M Preminger ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Data Transmission ◽  
Data Transfer ◽  
Implantable Loop Recorder ◽  
Loop Recorder ◽  
Bedside Monitor ◽  
Pill In The Pocket ◽  
Fundamental Requirement ◽  
Ecg Data ◽  
Clinical Strategy

Abstract Background A “pill-in-the-pocket” anticoagulation strategy, guided by ECG data from an implantable loop recorder (ILR), has been advocated as a clinical strategy. However, a fundamental requirement is the ability to reliably obtain daily ECG data from patients. Objective To determine the reliability of daily ECG data transfer from ILRs. Methods We evaluated patients implanted with an ILR in whom we sought to withhold oral anticoagulation (OAC) unless atrial fibrillation (AF) was detected. The ILR transmits data nightly to a bedside monitor. Once received, the data are sent to a central server. Over the course of a month, we tracked for each patient whether ECG data were received by the server. Results The study included 170 AF patients with an ILR where we planned to withhold OAC unless AF was documented. Daily ECG data were automatically transmitted and retrievable in only 36 (21%) patients. Two (1%) pts had not a single day of connectivity, 6 (4%) pts were connected <7 days, and 16 (9%) pts were connected <14 days. Wireless connectivity was lost for >48 hours in 89 (52%) patients (Figure). Most patients experienced multiple reasons for data transmission failure within the month. Conclusions To determine whether an ILR guided OAC strategy is feasible, reliable daily transmission of ECG data is a fundamental prerequisite. Current technology facilitated daily ECG data transfer in only 1/5 of patients. In the remaining, there was either extended loss of connectivity or no connectivity at all. A “pill-in-the-pocket” anticoagulation approach is currently difficult given existing hardware limitations. Funding Acknowledgement Type of funding source: None

scholarly journals Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Chao-Chen Chen ◽  
Fuchiang Rich Tsui
Keyword(s):  
Wavelet Transforms ◽  
Low Frequency ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Temporal Position ◽  
Important Indicator ◽  
Real Time Processing ◽  
Step Functions ◽  
Baseline Wander ◽  
Ecg Data

Abstract Background Electrocardiogram (ECG) signal, an important indicator for heart problems, is commonly corrupted by a low-frequency baseline wander (BW) artifact, which may cause interpretation difficulty or inaccurate analysis. Unlike current state-of-the-art approach using band-pass filters, wavelet transforms can accurately capture both time and frequency information of a signal. However, extant literature is limited in applying wavelet transforms (WTs) for baseline wander removal. In this study, we aimed to evaluate 5 wavelet families with a total of 14 wavelets for removing ECG baseline wanders from a semi-synthetic dataset. Methods We created a semi-synthetic ECG dataset based on a public QT Database on Physionet repository with ECG data from 105 patients. The semi-synthetic ECG dataset comprised ECG excerpts from the QT database superimposed with artificial baseline wanders. We extracted one ECG excerpt from each of 105 patients, and the ECG excerpt comprised 14 s of randomly selected ECG data. Twelve baseline wanders were manually generated, including sinusoidal waves, spikes and step functions. We implemented and evaluated 14 commonly used wavelets up to 12 WT levels. The evaluation metric was mean-square-error (MSE) between the original ECG excerpt and the processed signal with artificial BW removed. Results Among the 14 wavelets, Daubechies-3 wavelet and Symlets-3 wavelet with 7 levels of WT had best performance, MSE = 0.0044. The average MSEs for sinusoidal waves, step, and spike functions were 0.0271, 0.0304, 0.0199 respectively. For artificial baseline wanders with spikes or step functions, wavelet transforms in general had lower performance in removing the BW; however, WTs accurately located the temporal position of an impulse edge. Conclusions We found wavelet transforms in general accurately removed various baseline wanders. Daubechies-3 and Symlets-3 wavelets performed best. The study could facilitate future real-time processing of streaming ECG signals for clinical decision support systems.

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