scholarly journals Delineation of electrocardiogram morphologies by using discrete wavelet transforms

2021 ◽  
Vol 22 (1) ◽  
pp. 159
Author(s):  
Annisa Darmawahyuni ◽  
Siti Nurmaini ◽  
Hanif Habibie Supriansyah ◽  
Muhammad Irham Rizki Fauzi ◽  
Muhammad Naufal Rachmatullah ◽  
...  
Keyword(s):  
Wavelet Transforms ◽  
Noise Removal ◽  
P Wave ◽  
Discrete Wavelet ◽  
Wave Localization ◽  
T Wave ◽  
Wave Morphology ◽  
Cardiac Disorders ◽  
Non Stationary Signal ◽  
Different Characteristics

<span>The accuracy of electrocardiogram (ECG) delineation can affect the precise diagnose for cardiac disorders interpretation. Some nonideal ECG presentation can make a false decision in precision medicine. Besides, the physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration is also affected. <span>This paper proposes a discrete wavelet transform (DWT), non-stationary signal analysis for noise removal, and onset-offset of PQRST feature extraction. A well-known database from Physionet: QT database (QTDB) is used to validate the DWT function for detecting the onset and offset of P-wave, QRS-complex, and T-wave localization. From the results, P-peak detection gets the highest result that achieves 2.19 and 13.62 milliseconds of mean error and standard deviation, respectively. In contrast, Toff has obtained the highest error value due to differences in the T-wave morphology. It can be affected by inverted o</span>r biphasic T-waves and others.</span>

ECG SIGNAL GENERATION AND HEART RATE VARIABILITY SIGNAL EXTRACTION: SIGNAL PROCESSING, FEATURES DETECTION, AND THEIR CORRELATION WITH CARDIAC DISEASES

2012 ◽  
Vol 12 (04) ◽  
pp. 1240012 ◽  
Author(s):  
GOUTHAM SWAPNA ◽  
DHANJOO N. GHISTA ◽  
ROSHAN JOY MARTIS ◽  
ALVIN P. C. ANG ◽  
SUBBHURAAM VINITHA SREE
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sinus Bradycardia ◽  
Premature Ventricular Contraction ◽  
P Wave ◽  
Ecg Signal ◽  
Qrs Complex ◽  
T Wave ◽  
St Segment ◽  
Cardiac Disorders

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.

Fault Location of Transmission Line Based on Discrete Wavelet Transforms

2011 ◽  
Vol 121-126 ◽  
pp. 1269-1273
Author(s):  
Wen Xiu Tang ◽  
Mo Zhang ◽  
Ying Liu ◽  
Xu Fei Lang ◽  
Liang Kuan Zhu
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Wavelet Transforms ◽  
Fault Location ◽  
Short Circuit ◽  
Signal Transmission ◽  
Transient Signal ◽  
Discrete Wavelet ◽  
Novel Method ◽  
Non Stationary Signal

In this paper, a novel method is investigated to detect short-circuit fault signal transmission lines in strong noise environment based on discrete wavelet transform theory. Simulation results show that the method can accurately determine the fault position, can effectively analyze the non-stationary signal and be suitable for transmission line fault occurred after transient signal detection. Furthermore, it can effectively eliminate noise effects of fault signal so as to realize the transmission lines of accurate fault.

scholarly journals Electrocardiographic manifestations in severe hypokalemia

2018 ◽  
Vol 48 (1) ◽  
pp. 030006051881105 ◽  
Author(s):  
Xiqiang Wang ◽  
Dan Han ◽  
Guoliang Li
Keyword(s):  
P Wave ◽  
Dynamic Changes ◽  
T Wave ◽  
Electrolyte Disturbances ◽  
St Segment ◽  
Pr Interval ◽  
Wave Morphology ◽  
Life Threatening

Hypokalemia is one of the most common electrolyte disturbances in the clinic and it can increase the risk of life-threatening arrhythmias. Electrocardiographic characteristics associated with hypokalemia include dynamic changes in T-wave morphology, ST-segment depression, and U waves, which are often best seen in the mid-precordial leads (V2–V4). The PR interval can also be prolonged along with an increase in the amplitude of the P wave. We report a case of a patient with hypokalemia (1.31 mmol/L) who showed typical electrocardiographic characteristics of hypokalemia.

Noise reduction by perfect-translation-invariant complex discrete wavelet transforms for fetal electrocardiography and magnetocardiography

2014 ◽  
Vol 12 (04) ◽  
pp. 1460008 ◽  
Author(s):  
Yasuhiro Ishikawa ◽  
Hitoshi Horigome ◽  
Akihiko Kandori ◽  
Hiroshi Toda ◽  
Zhong Zhang
Keyword(s):  
Noise Reduction ◽  
Wavelet Transforms ◽  
Body Movement ◽  
Internal Noise ◽  
P Wave ◽  
Time Interval ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Qrs Complex ◽  
Fetal Electrocardiography

Echocardiography is widely used for the diagnosis of fetal cardiac arrhythmias. However, this method does not detect configurational changes in the electrocardiogram (ECG) such as life-threatening changes in QRS and the prolongation of the QT interval. Fetal magnetocardiography (fMCG) and fetal electrocardiography (fECG) are valuable tools for the detection of electrophysiological cardiac signals although both have certain limitations. Such techniques must deal with excess internal noise such as maternal respiratory movements, fetal movements, muscle contraction and fetal body movement and external noise (e.g., electromagnetic waves). Heart rate variability (HRV) is a well-known phenomenon with fluctuation in the time interval between heartbeats. The lack of translation invariance is a serious defect in the conventional wavelet transforms (discrete wavelet transform (DWT)). Fluctuation of the impulse response at each energy level is observed in the multi-resolution analysis (MRA). Configurational changes in the ECG waveforms are frequently observed after noise reduction by the conventional wavelet transforms. Both the lack of translation invariance of conventional wavelet transforms and HRV cause deformation of the ECG waveforms. We describe here the CDWTs with perfect translation invariance (PTI). Compared with conventional wavelets, PTI of the fECG and fMCG resulted in only minor configurational changes in the ECG waveforms. This technique yields persistently stable ECG waveforms, including P wave and QRS complex. First, an independent component analysis (ICA) was applied to fECG or fMCG data to remove noise. We provide an example to show that the morphological change in QRS complex is barely affected when PTI is applied to normal fECG. Examples of fetal arrhythmias, such as ventricular trigeminy, ventricular bigeminy and premature atrial contraction are demonstrated using this technique. The results lead us to the conclusion that ICA and noise reduction in fECG and fMCG by PTI are promising methods for the diagnosis of fetal arrhythmia.

scholarly journals SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis

2017 ◽  
Vol 7 (5) ◽  
pp. 2555 ◽  
Author(s):  
Madina Hamiane ◽  
Fatema Saeed
Keyword(s):  
Wavelet Transforms ◽  
Noise Removal ◽  
Image Features ◽  
Support Vector ◽  
Computer Assisted ◽  
Discrete Wavelet ◽  
Normal Brain ◽  
Morphological Operations ◽  
Malignant Tumours ◽  
Brain Images

Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature.

scholarly journals ECG signal denoising using a novel approach of adaptive filters for real-time processing

2021 ◽  
Vol 11 (2) ◽  
pp. 1243
Author(s):  
Amean Al-Safi
Keyword(s):  
Wavelet Transforms ◽  
Adaptive Filters ◽  
Reference Signal ◽  
Noise Removal ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Mean Square ◽  
Least Mean Square ◽  
Novel Approach

Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.

scholarly journals P-211 T-wave morphology analysis in healthy subjects and in patients with cardiac disorders

EP Europace ◽  
2003 ◽  
Vol 4 (Supplement_2) ◽  
pp. B116-B116
Author(s):  
Y. Gang ◽  
K. Hnatkova ◽  
J. Gimeno ◽  
A. Ghuran ◽  
M. Malik
Keyword(s):  
Healthy Subjects ◽  
T Wave ◽  
Morphology Analysis ◽  
Wave Morphology ◽  
Cardiac Disorders

scholarly journals Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique

2021 ◽  
Vol 12 (5) ◽  
pp. 1-15
Author(s):  
Shabana R. Ziyad ◽  
Radha V. ◽  
Thavavel Vaiyapuri
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Wavelet Transforms ◽  
Noise Removal ◽  
Adaptive Thresholding ◽  
Discrete Wavelet ◽  
Lung Cancer Patients ◽  
Ionizing Radiations ◽  
Low Dose Computed Tomography ◽  
Removal Technique

Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.

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