ECG Data Analysis

2010 ◽  
pp. 122-144 ◽  
Author(s):  
Saurabh Pal ◽  
Swanirbhar Majumder
Keyword(s):  
Heart Failure ◽  
Data Analysis ◽  
Electrical Activity ◽  
Automatic Classification ◽  
Classification Techniques ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Electrocardiogram Ecg ◽  
Acquisition Technique ◽  
Ecg Data

In this chapter authors explain an idea for automation of heart failure with the help of ECG signals. An electrocardiogram (ECG) is a test that records the electrical activity of the heart. A brief description on automatic classification techniques is also given. ECG being the most vital physiological signal, its acquisition technique, noise and artifacts elimination methodologies are discussed in this chapter.

scholarly journals A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 606 ◽  
Author(s):  
Minggang Shao ◽  
Zhuhuang Zhou ◽  
Guangyu Bin ◽  
Yanping Bai ◽  
Shuicai Wu
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Monitoring ◽  
Web Browser ◽  
Ecg Signals ◽  
Cloud Server ◽  
Android App ◽  
Electrocardiogram Ecg ◽  
The Web ◽  
Ecg Data

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

Automated Recognition of Arrhythmia Using Deep Neural Networks for 12-Lead Electrocardiograms with Fractional Time–Frequency Domain Extension

2020 ◽  
Vol 10 (11) ◽  
pp. 2764-2767
Author(s):  
Chuanbin Ge ◽  
Di Liu ◽  
Juan Liu ◽  
Bingshuai Liu ◽  
Yi Xin
Keyword(s):  
Neural Networks ◽  
Frequency Domain ◽  
Deep Neural Networks ◽  
Fractional Fourier Transform ◽  
Training Dataset ◽  
Clinical Tool ◽  
Time Frequency ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Ecg Data

Arrhythmia is a group of conditions in which the heartbeat is irregular. There are many types of arrhythmia. Some can be life-threatening. Electrocardiogram (ECG) is an effective clinical tool used to diagnosis arrhythmia. Automatic recognition of different arrhythmia types in ECG signals has become an important and challenging issue. In this article, we proposed an algorithm to detect arrhythmia in 12-lead ECG signals and classify signals into 9 categories. Two 19-layer deep neural networks combining convolutional neural network and gated recurrent unit were proposed to realize this work. The first one was trained directly with the raw 12-lead ECG data while the other one was trained with an 18-"lead" ECG data, where the six extra leads containing morphology information in fractional time–frequency domain were generated utilizing fractional Fourier transform (FRFT). Overall detection results were obtained by fusing the output of these two networks and the final classification results on the testing dataset reports our proposed algorithm obtained a F1 score of 0.855. Furthermore, with our proposed algorithm, a better F1 score 0.81 was attained using training dataset provided by the China Physiological Signal Challenge held in 2018.

scholarly journals IMPROVEMENT OF THE CREDIBILITY OF ANALYSIS OF ELECTROCARDIOGRAMS FOR BIOMETRIC PERSONAL IDENTIFICATION

2020 ◽  
pp. 125-130
Author(s):  
Яковенко І.О. ◽  
Рудий О.Д. ◽  
Турчина М.О.
Keyword(s):  
Electrical Activity ◽  
Age Groups ◽  
Personal Identification ◽  
Physical Parameters ◽  
Biometric Authentication ◽  
High Demand ◽  
Biometric Data ◽  
Ecg Signals ◽  
High Level ◽  
Electrocardiogram Ecg

Nowadays there is a high demand for biometric authentication. These systems possess a high level of protection, as they evaluate not only the physical parameters, but also personality characteristics. The paper analyzes a biometric scheme based on the electrical activity of the human heart in the form of electrocardiogram (ECG) signals. The study was performed using standard laboratory measurements KL-720 has all age groups. As a result, an electrical activity signal was obtained. The aim of this work was to filter the captured signal for further use with biometric data.

Detection of Heart Defects using Electrocardiogram (ECG)

2018 ◽  
Vol 4 (11) ◽  
Author(s):  
Kenil Shah ◽  
Mayur Rane ◽  
Dr. Vahid Emamian
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Heart Diseases ◽  
Heart Defects ◽  
Healthy Person ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Processing Techniques ◽  
Electrocardiogram Ecg ◽  
Ecg Data

Electrocardiogram (ECG) signals are vital to identifying cardiovascular disease. The numerous availability of signal processing and neural networks techniques for processing of ECG signals has inspired us to do research on extracting features of ECG signals to identify different cardiovascular diseases. We distinguish between a healthy person ECG data and person having disease ECG data using signal processing and neural network toolbox in Matlab. The data was downloaded from physiobank. To distinguish normal and abnormal ECG, Neural network is used. Feature extraction method is used to identify heart diseases. The diseases that are identified include Tachycardia, Bradycardia, first- degree Atrioventricular (AV) and a healthy person. Subsequently, ECG signals are very noisy; signal processing techniques are used to remove the noise impurity. The heart rate can be calculated by detecting the distance between R-R intervals of the signal. The algorithm successfully distinguished between normal and abnormal ECG data.

EVALUATION OF QRS COMPLEX BASED ON DWT COEFFICIENTS ANALYSIS USING DAUBECHIES WAVELETS FOR DETECTION OF MYOCARDIAL ISCHAEMIA

2010 ◽  
Vol 10 (02) ◽  
pp. 273-290 ◽  
Author(s):  
G. M. PATIL ◽  
K. SUBBA RAO ◽  
U. C. NIRANJAN ◽  
K. SATYANARAYAN
Keyword(s):  
Myocardial Ischaemia ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Daubechies Wavelets ◽  
Detection Rates ◽  
Ecg Signals ◽  
Processing Module ◽  
Data Files ◽  
Electrocardiogram Ecg ◽  
Ecg Data

This paper presents a new approach in the field of electrocardiogram (ECG) feature extraction system based on the discrete wavelet transform (DWT) coefficients using Daubechies Wavelets. Real ECG signals recorded in lead II configuration are chosen for processing. The ECG signal was acquired by a battery operated, portable ECG data acquisition and signal processing module. In the second step the ECG signal was denoised using soft thresholding with Symlet4 wavelet. Further denoising was achieved by removing the corresponding wavelet coefficients at higher levels of decomposition. Later the ECG data files were converted to .txt files and subsequently to. mat files before being imported into the Matlab 7.4.0 environment for the computation of the decomposition coefficients. The QRS complexes were grouped as normal or myocardial ischaemic ones based on these decomposition coefficients. The algorithm developed by us was evaluated with control database comprising 120 records and validated using 60 records making up test database. By using the DWT coefficients, we have successfully achieved the myocardial ischaemia detection rates up to 97.5% with the technique developed by us for control data and up to 100% for validation test data.

Filtration and Classification of ECG Signals

2018 ◽  
pp. 72-94 ◽  
Author(s):  
Satya Ranjan Dash ◽  
Asim Syed Sheeraz ◽  
Annapurna Samantaray
Keyword(s):  
Electrical Activity ◽  
Electrical Conduction ◽  
Diagnostic Tool ◽  
Noise Level ◽  
Medical Professionals ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Electrocardiogram Ecg ◽  
Structure And Functions

Electrocardiogram (ECG) is a kind of process of recording the electrical activity/signals of the heart with respect to the time. ECG conveys a wide amount of information related to the structure and functions of the heart, its electrical conduction processes. ECG is a diagnostic tool that the doctors and medical professionals use to measure patients' heart activity by paying attention to the electric current flowing in the heart. Due to the presence of noises, it needs to carry out the filtration process. Filtration is the process of keeping the components of the signals of desired frequencies by setting up an “fc” value and removing the components apart from the said “fc” frequency. It is required to eliminate the noise level from the ECG signal, such that the resultant ECG signal must be free from noises. All these techniques and algorithms have their advantages and limitations which are discussed in this chapter.

scholarly journals Design of Vital Sign Monitor with ECG, BPM, and Respiration Rate Parameters

2021 ◽  
Vol 3 (1) ◽  
pp. 34-38
Author(s):  
Gede Aditya Mahendra Oka ◽  
Andjar Pudji
Keyword(s):  
Respiration Rate ◽  
Vital Sign ◽  
Critical Period ◽  
Heart Activity ◽  
Electrical Behavior ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Respiration Rates ◽  
Pulse Form ◽  
Electrocardiogram Ecg

Vital sign monitor is a device used to monitor a patient's vital sign, in the form of a heartbeat, pulse, blood pressure, temperature of the heart's pulse form continuously. Condition monitoring in patients is needed so that paramedics know the development of the condition of inpatients who are experiencing a critical period. Electrocardiogram (ECG) is a physiological signal produced by the electrical activity of the heart. Recording heart activity can be used to analyze how the characteristics of the heart. By obtaining respiration from outpatient electrocardiography, which is increasingly being used clinically to practice to detect and characterize the abnormal occurrence of heart electrical behavior during normal daily activities. The purpose of this study is to determine that the value of the Repiration Rate is taken from ECG signals because of its solidity. At the peak of the R ECG it has several respiratory signals such as signals in fluctuations. An ECG can be used to determine breathing numbers. This module utilizes leads ECG signals to 1 lead, namely lead 2, respiration rate taken from the ECG, BPM in humans displayed on a TFT LCD. This research module utilizes the use of filters to obtain ECG signals, and respiration rates to display the results on a TFT LCD. This module has the highest error value of 0.01% compared to the Phantom EKG tool. So this module can be used for the diagnosis process.ECG, Respiration Rate, Filter

scholarly journals Identifying Heart Failure in ECG Data With Artificial Intelligence—A Meta-Analysis

2021 ◽  
Vol 2 ◽  
Author(s):  
Dimitri Grün ◽  
Felix Rudolph ◽  
Nils Gumpfer ◽  
Jennifer Hannig ◽  
Laura K. Elsner ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Meta Analysis ◽  
Diagnostic Odds Ratio ◽  
Patient Level Data ◽  
Ecg Signals ◽  
Patient Level ◽  
Level Data ◽  
Comprehensive Search ◽  
Ecg Data

Introduction: Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of cardiovascular diseases including heart failure (HF). Artificial intelligence (AI) can be used for semi-automated ECG analysis. The aim of this evaluation was to provide an overview of AI use in HF detection from ECG signals and to perform a meta-analysis of available studies.Methods and Results: An independent comprehensive search of the PubMed and Google Scholar database was conducted for articles dealing with the ability of AI to predict HF based on ECG signals. Only original articles published in peer-reviewed journals were considered. A total of five reports including 57,027 patients and 579,134 ECG datasets were identified including two sets of patient-level data and three with ECG-based datasets. The AI-processed ECG data yielded areas under the receiver operator characteristics curves between 0.92 and 0.99 to identify HF with higher values in ECG-based datasets. Applying a random-effects model, an sROC of 0.987 was calculated. Using the contingency tables led to diagnostic odds ratios ranging from 3.44 [95% confidence interval (CI) = 3.12–3.76] to 13.61 (95% CI = 13.14–14.08) also with lower values in patient-level datasets. The meta-analysis diagnostic odds ratio was 7.59 (95% CI = 5.85–9.34).Conclusions: The present meta-analysis confirms the ability of AI to predict HF from standard 12-lead ECG signals underlining the potential of such an approach. The observed overestimation of the diagnostic ability in artificial ECG databases compared to patient-level data stipulate the need for robust prospective studies.

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.

Sign in / Sign up

Export Citation Format

Share Document