scholarly journals Electrocardiogram Fiducial Point Detector Using a Bilateral Filter and Symmetrical Point-Filter Structure

2021 ◽  
Vol 18 (20) ◽  
pp. 10792
Author(s):  
Tae-Wuk Bae ◽  
Kee-Koo Kwon ◽  
Kyu-Hyung Kim
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Bilateral Filter ◽  
Ecg Signal ◽  
Edge Preservation ◽  
T Wave ◽  
The Difference ◽  
Electrocardiogram Ecg ◽  
Point Detector

The characteristics or aspects of important fiducial points (FPs) in the electrocardiogram (ECG) signal are complicated because of various factors, such as non-stationary effects and low signal-to-noise ratio. Due to the various noises caused by the ECG signal measurement environment and by typical ECG signal deformation due to heart diseases, detecting such FPs becomes a challenging task. In this study, we introduce a novel PQRST complex detector using a one-dimensional bilateral filter (1DBF) and the temporal characteristics of FPs. The 1DBF with noise suppression and edge preservation preserves the P- or T-wave whereas it suppresses the QRS-interval. The 1DBF acts as a background predictor for predicting the background corresponding to the P- and T-waves and the remaining flat interval excluding the QRS-interval. The R-peak and QRS-interval are founded by the difference of the original ECG signal and the predicted background signal. Then, the Q- and S-points and the FPs related to the P- and T-wave are sequentially detected using the determined searching range and detection order based on the detected R-peak. The detection performance of the proposed method is analyzed through the MIT-BIH database (MIT-DB) and the QT database (QT-DB).

Identification of myocardial infarction from analysis of ECG signal

2020 ◽  
Vol 24 (3) ◽  
pp. 217-226
Author(s):  
D.B.V. Jagannadham ◽  
D.V. Sai Narayana ◽  
P. Ganesh ◽  
D. Koteswar
Keyword(s):  
Myocardial Infarction ◽  
Feature Extraction ◽  
Early Stage ◽  
Heart Diseases ◽  
Ecg Signal ◽  
St Segment Elevation ◽  
T Wave ◽  
European Database ◽  
St Segment ◽  
Electrocardiogram Ecg

Many heart diseases can be identified and cured at an early stage by studying the changes in the features of electrocardiogram (ECG) signal. Myocardial Infarction (MI) is the serious cause of death worldwide. If MI can be detected early, the death rate will reduce. In this paper, an algorithm to detect MI in an ECG signal using Daubechies wavelet transform technique is developed. The ECG signal-denoising is performed by removing the corresponding wavelet coefficients at higher scale. After denoising, an important step towards identifying an arrhythmia is the feature extraction from the ECG. Feature extraction is carried out to detect the R peaks of the ECG signal. Since as R peak is having the highest amplitude, and therefore it is detected in the first round, subsequently location of other peaks are determined. Having completed the preprocessing and the feature extraction the MI is detected from the ECG based on inverted T wave logic and ST segment elevation. The algorithm was evaluated using MIT-BIH database and European database satisfactorily.

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

scholarly journals Fractal Based Analysis of the Influence of Odorants on Heart Activity

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish
Keyword(s):  
Heart Rate ◽  
Heart Diseases ◽  
Approximate Entropy ◽  
Ecg Signal ◽  
Heart Activity ◽  
Heart Research ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
External Stimuli ◽  
Electrocardiogram Ecg

Abstract An important challenge in heart research is to make the relation between the features of external stimuli and heart activity. Olfactory stimulation is an important type of stimulation that affects the heart activity, which is mapped on Electrocardiogram (ECG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the ECG signal. This study investigates the relation between the structures of heart rate and the olfactory stimulus (odorant). We show that the complexity of the heart rate is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal heart rate. Also, odorant having higher entropy causes the heart rate having lower approximate entropy. The method discussed here can be applied and investigated in case of patients with heart diseases as the rehabilitation purpose.

A MODIFIED CORRELATION METHOD FOR T-WAVE ALTERNANS DETECTION

2013 ◽  
Vol 25 (03) ◽  
pp. 1350030
Author(s):  
Xiangkui Wan ◽  
Kanghui Yan ◽  
Minggui Li ◽  
Dingcheng Xiang
Keyword(s):  
Heart Diseases ◽  
Correlation Method ◽  
Absolute Difference ◽  
T Wave ◽  
Formidable Challenge ◽  
Maximum Absolute Difference ◽  
T Wave Alternans ◽  
Improved Performance ◽  
The Time Domain ◽  
Electrocardiogram Ecg

Identification of individuals who are at risk for sudden cardiac death (SCD) remains a formidable challenge. T-wave alternans (TWA) evaluation is emerging as an important tool for risk stratification in patients with heart diseases. Several methods have been developed in recent years to detect and quantify TWA. One such method is known as the correlation method (CM). This method performs well for different levels of TWA and phase shifts in the time domain, but it is sensitive to noise and requires higher quality of electrocardiogram (ECG) signal for test. In this paper, we propose a modified correlation method (MCM) to ensure a robust and accuracy detection of TWA. Compared with CM, MCM add a stage of T-wave curve fitting before media T-wave template, and the TWA magnitude is obtained by meaning the maximum absolute difference between even and odd T-wave. Our assessment study demonstrates the improved performance of the proposed algorithm.

scholarly journals Smartphone based ECG Acquisition and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 539-543
Author(s):  
Renuka Vijay Kapse
Keyword(s):  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Health Monitoring ◽  
Early Warning ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Android Application ◽  
Ecg Signals ◽  
Heart Damage ◽  
Electrocardiogram Ecg

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

scholarly journals Classification of Multi Heart Diseases With Android Based Monitoring System

2020 ◽  
pp. 14-22
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Confusion Matrix ◽  
Predictive Coding ◽  
Classification Performance ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Predictive Coding ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

EMD-BASED ECG DENOISING USING SOURCE SEPARATION

2015 ◽  
Vol 15 (05) ◽  
pp. 1550082 ◽  
Author(s):  
MOURAD TALBI ◽  
SABEUR ABID ◽  
ADNEN CHERIF
Keyword(s):  
Signal To Noise Ratio ◽  
Source Separation ◽  
Second Step ◽  
Ecg Signal ◽  
Hybrid Technique ◽  
Mode Function ◽  
Mode Decomposition ◽  
Ecg Denoising ◽  
The Mean ◽  
Electrocardiogram Ecg

We consider the problem of electrocardiogram (ECG) denoising using source separation. In this study, a hybrid technique using empirical mode decomposition (EMD) and source separation, is proposed. This technique consists of two steps, the first step consists in applying the EMD to two different mixtures. These mixtures are obtained by corrupting in additive manner, the same ECG signal by a white Gaussian noise with two different values of the signal-to-noise ratio (SNR). The second step consists in computing the entropy of each obtained intrinsic mode function (IMF) and finds the two IMFs having the minimal entropy. These two IMFs are used to estimate the separation matrix of the ECG signal from noise by using the source separation. The proposed technique is evaluated by comparing it to the denoising technique based on source separation in time domain using runica and the technique based on Bionic wavelet transform (BWT) and also using source separation. The obtained results from SNR and the mean square error (MSE) computations, show that the proposed technique outperforms the other two techniques used in this evaluation.

scholarly journals Design of a Wearable 12-Lead Noncontact Electrocardiogram Monitoring System

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1509 ◽  
Author(s):  
Chien-Chin Hsu ◽  
Bor-Shing Lin ◽  
Ke-Yi He ◽  
Bor-Shyh Lin
Keyword(s):  
Myocardial Ischemia ◽  
Monitoring System ◽  
Mechanical Design ◽  
Heart Diseases ◽  
Signal Quality ◽  
Ecg Signal ◽  
Ventricular Premature Contraction ◽  
Dry Electrodes ◽  
Electrocardiogram Monitoring ◽  
Electrocardiogram Ecg

A standard 12-lead electrocardiogram (ECG) is an important tool in the diagnosis of heart diseases. Here, Ag/AgCl electrodes with conductive gels are usually used in a 12-lead ECG system to access biopotentials. However, using Ag/AgCl electrodes with conductive gels might be inconvenient in a prehospital setting. In previous studies, several dry electrodes have been developed to improve this issue. However, these dry electrodes have contact with the skin directly, and they might be still unsuitable for patients with wounds. In this study, a wearable 12-lead electrocardiogram monitoring system was proposed to improve the above issue. Here, novel noncontact electrodes were also designed to access biopotentials without contact with the skin directly. Moreover, by using the mechanical design, this system allows the user to easily wear and take off the device and to adjust the locations of the noncontact electrodes. The experimental results showed that the proposed system could exactly provide a good ECG signal quality even while walking and could detect the ECG features of the patients with myocardial ischemia, installation pacemaker, and ventricular premature contraction.

Study of Quality Evaluation Indexes in Filtered ECG Signal

2013 ◽  
Vol 284-287 ◽  
pp. 1671-1675
Author(s):  
Gang Zheng ◽  
Ming Li Sun ◽  
Yuan Gu
Keyword(s):  
Quality Evaluation ◽  
Shape Change ◽  
Signal To Noise Ratio ◽  
Ecg Signal ◽  
Voltage Amplitude ◽  
Evaluation Indexes ◽  
St Segment ◽  
Ecg Signal Processing ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) is a kind of weak signal. It was disturbed by surrounding factors, even by patient him/herself. It was happened mostly in portable device. Filtering is an usual step in ECG signal processing. Therefore, the quality evaluation of ECG signal became necessary. In the paper, some indexes were proposed to evaluate the quality of filtered ECG signal. The definition and recommended values or limits of the indexes were discussed. The indexes covered from the aspects of signal procession and clinical diagnosis meanings. They were Signal-to Noise Ratio (SNR), Autocorrelation coefficient (AC), Transformation Ratio (StTR) and Voltage Amplitude Change (StTV) of ECG ST Segment. Median, Wavelet, and Morphology filters were selected in the experiments. From the experiment results, Wavelet performs best in controlling attenuation, but it distorted ST segment the most, both in shape and in its voltage amplitude. The shape change ratio may reach 25%, compare to 17% of median and 14% of morphology, and those filters were acceptable clinical evaluation. It was proved that the indexes can become the potential standard in quality evaluation in ECG signal filtering process.

scholarly journals An Efficient Technique for Compressing ECG Signals Using QRS Detection, Estimation, and 2D DWT Coefficients Thresholding

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Mohammed Abo-Zahhad ◽  
Sabah M. Ahmed ◽  
Ahmed Zakaria
Keyword(s):  
Threshold Level ◽  
Ecg Signal ◽  
Error Signal ◽  
Qrs Complex ◽  
Qrs Detection ◽  
High Compression Ratio ◽  
Compression Technique ◽  
Ecg Signals ◽  
The Difference ◽  
Electrocardiogram Ecg

This paper presents an efficient electrocardiogram (ECG) signals compression technique based on QRS detection, estimation, and 2D DWT coefficients thresholding. Firstly, the original ECG signal is preprocessed by detecting QRS complex, then the difference between the preprocessed ECG signal and the estimated QRS-complex waveform is estimated. 2D approaches utilize the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. The error signal is cut and aligned to form a 2-D matrix, then the 2-D matrix is wavelet transformed and the resulting wavelet coefficients are segmented into groups and thresholded. There are two grouping techniques proposed to segment the DWT coefficients. The threshold level of each group of coefficients is calculated based on entropy of coefficients. The resulted thresholded DWT coefficients are coded using the coding technique given in the work by (Abo-Zahhad and Rajoub, 2002). The compression algorithm is tested for 24 different records selected from the MIT-BIH Arrhythmia Database (MIT-BIH Arrhythmia Database). The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and low computational complexity in comparison with other methods.

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