scholarly journals ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7003
Author(s):  
Pasquale Daponte ◽  
Luca De Vito ◽  
Grazia Iadarola ◽  
Francesco Picariello
Keyword(s):  
Myocardial Infarction ◽  
Compressed Sensing ◽  
Healthy Subjects ◽  
Signal Quality ◽  
Ecg Monitoring ◽  
Sensing Matrix ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Innovative Method ◽  
Electrocardiogram Ecg

This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio (CR) up to 10, without compromising signal quality. In particular, for CR= 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.

scholarly journals Utility of a Smartphone Based System (cvrPhone) to Predict Short-term Arrhythmia Susceptibility

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kwanghyun Sohn ◽  
Steven P. Dalvin ◽  
Faisal M. Merchant ◽  
Kanchan Kulkarni ◽  
Furrukh Sana ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Ventricular Arrhythmias ◽  
Smart Phone ◽  
Coronary Artery Occlusion ◽  
Artery Occlusion ◽  
Ecg Signals ◽  
Arrhythmic Event ◽  
User Friendly ◽  
Electrocardiogram Ecg

Abstract Repolarization alternans (RA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. We developed a 12-lead, blue-tooth/Smart-Phone (Android) based electrocardiogram (ECG) acquisition and monitoring system (cvrPhone), and an application to estimate RA, in real-time. In in-vivo swine studies (N = 17), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. RA was estimated using the Fast Fourier Transform (FFT) method using a custom developed algorithm in JAVA. Underlying ischemia was detected using a custom developed ischemic index. RA from each lead showed a significant (p < 0.05) increase within 1 min of occlusion compared to baseline (n = 29). Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 4) were preceded by significant (p < 0.05) increase of RA prior to the onset of the tachy-arrhythmias. Similarly, the ischemic index exhibited a significant increase following myocardial infarction (p < 0.05) and preceding a tachy-arrhythmic event. In conclusion, RA can be effectively estimated using surface lead electrocardiograms by analyzing beat-to-beat variability in ECG morphology using a smartphone based platform. cvrPhone can be used to detect myocardial ischemia and arrhythmia susceptibility using a user-friendly, clinically acceptable, mobile platform.

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.

FRACTAL ANALYSIS OF THE ELECTROCARDIOGRAM SIGNAL

2014 ◽  
Vol 14 (04) ◽  
pp. 1450055 ◽  
Author(s):  
IBTICEME SEDJELMACI ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Premature Ventricular Contraction ◽  
Multifractal Spectrum ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Linear Approach ◽  
Atrial Premature Beat ◽  
The Subject ◽  
Electrocardiogram Signal ◽  
Electrocardiogram Ecg

In this paper, the analysis of the electrocardiogram (ECG) signal is carried out according a non-linear approach. This concerns the eventual fractal behavior of such signal and the correlation of such behavior with normal and pathological ECG signals. The analysis is carried out on different ECG signals taken from the MIT-BIH arrhythmia database. In fact these signals are those of six subjects with different ages and presenting both normal and abnormal arrhythmias situations. The abnormal situations are atrial premature beat (APB), premature ventricular contraction (PVC), right bundle branch block (RBBB) and left bundle branch block (LBBB). The fractal behavior of these signals is analyzed according to the determination of the multifractal spectrum and the fractal dimension variations and looking for eventually a fractal signature of each heart disease and age of the subject. The obtained results show a fractal signature according to the age and the pathologies for the studied cases. However further investigations are required on larger databases to confirm such results.

scholarly journals Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

2020 ◽  
Vol 12 (10) ◽  
pp. 1685 ◽  
Author(s):  
Amin Ullah ◽  
Syed Muhammad Anwar ◽  
Muhammad Bilal ◽  
Raja Majid Mehmood
Keyword(s):  
Premature Ventricular Contraction ◽  
Careful Study ◽  
Spectral Image ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
The One ◽  
Short Time ◽  
Electrocardiogram Ecg ◽  
Better Than

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

scholarly journals Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5290
Author(s):  
Huaiyu Zhu ◽  
Yisheng Zhao ◽  
Yun Pan ◽  
Hanshuang Xie ◽  
Fan Wu ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Signal Acquisition ◽  
Ecg Monitoring ◽  
Daily Lives ◽  
Heartbeat Classification ◽  
Ecg Analysis ◽  
Ecg Signals ◽  
Extreme Gradient Boosting ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices

Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

scholarly journals Arrhythmia Classification Using One Dimensional Conventional Neural Network

2021 ◽  
Vol 13 (3) ◽  
pp. 43-58
Author(s):  
Sarah kamil ◽  
Lamia Muhammed
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Disease Classification ◽  
Visual Interpretation ◽  
Deep Convolutional Neural Networks ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Short Period ◽  
Electrocardiogram Ecg

Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. As a result, we present a significant approach for identifying arrhythmias using ECG signals. In this study, we proposed an approach based on Deep Learning (DL) technology that is a framework of nine-layer one-dimension Conventional Neural Network (1D CNN) for classifying automatically ECG signals into four cardiac conditions named: normal (N), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The practical test of this work was executed with the benchmark MIT-BIH database. We achieved an average accuracy of 99%, precision of 98%, recall of 96.5%, specificity of 99.08%, and an F1-score of 95.75%. The obtained results were compared with some relevant models, and they showed that the proposed framework outperformed those models in some measures. The new approach’s performance indicates its success. Also, it has been shown that deep convolutional neural networks can be used efficiently in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving time and effort. Keywords: 1-D CNN, Arrhythmia, Cardiovascular Disease, Classification, Deep learning, Electrocardiogram(ECG), MIT-BIH arrhythmia database.

VT/VF Detection Method Based on ECG Signal Quality Assessment

2018 ◽  
Vol 27 (11) ◽  
pp. 1850169 ◽  
Author(s):  
Borisav Jovanović ◽  
Srdan Milenković ◽  
Milan Pavlović
Keyword(s):  
Cardiac Arrhythmias ◽  
Signal Analysis ◽  
Detection Method ◽  
Signal Quality ◽  
Ecg Signals ◽  
Battery Charge ◽  
Time Domain Signal ◽  
Signal Parameters ◽  
Life Threatening ◽  
Electrocardiogram Ecg

Artefacts which are present in electrocardiogram (ECG) recordings distort detection of life-threatening arrhythmias such as ventricular tachycardia and ventricular fibrillation. The method examines single ECG lead and exploits time domain signal parameters for real-time detection of severe cardiac arrhythmias. The method is dedicated to implementation in mobile ECG telemetry systems, which are designed by using low-power microcontrollers, operating more than a week on a single battery charge. The method has been validated on publicly available databases and the results are presented. We verified our method on ECG signals obtained without pre-selection meaning that the noisy intervals were not omitted from signal analysis.

scholarly journals Understanding ST equivalents on ECG

2021 ◽  
pp. 52-57
Author(s):  
Quoc Bao Tran ◽  
Anh Khoa Phan ◽  
Anh Binh Ho
Keyword(s):  
Myocardial Infarction ◽  
Acute Coronary Syndrome ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Bundle Branch Block ◽  
Cut Point ◽  
Coronary Syndrome ◽  
St Elevation ◽  
Electrocardiogram Ecg

Acute coronary syndrome (ACS) is a syndrome due to decreased blood flow in the coronary arteries such that part of the heart muscle is unable to function properly or dies. Even though the detection of a rise and/or fall of cTn values with at least one value above the 99th percentile URL is the key in diagnose of ACS, the role of electrocardiogram (ECG) still plays an important role in ACS simply because of its sensitivity and specificity. In clinical practice, ST-Elevation Myocardial infarction (STEMI) is easy for physicians and cardiologists to identify. STEMI is defined by new ST-elevation at the J-point in the absence of left ventricular hypertrophy and bundle branch block with two contiguous leads with cut-point: ≥ 1mm in all leads other than V2-V3 where the following cut-point apply: ≥ 2mm in men ≥ 40 years; ≥2.5mm in men < 40 years, or ≥ 1.5mm in women regardless of age. 

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.

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