ECG Signal  Denoising by Discrete Wavelet Transform

Aqil, Mounaim; Jbari, Atman; Bourouhou, Abdennaser

doi:10.3991/ijoe.v13i09.7159

Cited by 41 publications

(28 citation statements)

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“…Wavelet transform has been used in this phase since it has been proven to be a useful tool for nonstationary signal analysis [51]. As the performance of signal processing system is heavily dependent on the chosen wavelet family and on the wavelet filter length, accurate selection was carried out.…”

Section: Wavelet Denoisingmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.

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Section: Wavelet Denoisingmentioning

confidence: 99%

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

2019

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“…Biến đổi wavelet là một phương pháp toán học được nhiều nhà khoa học, các nhà chuyên môn nghiên cứu và thực hiện. Các biện pháp khử nhiễu sử dụng biến đổi wavelet rời rạc DWT [2] [3] có hiệu quả khử nhiễu khá tốt, nhưng trong một số trường hợp có thể sinh ra một số dao động giả tạo do quá trình biến đổi. Một số nghiên cứu phương pháp khử nhiễu sử dụng biến đổi wavelet dịch bất biến TIDWT (Translation-Invariant Discrete Wavelet transforms) [4] đã công bố sử dụng ngưỡng phổ dụng (ngưỡng đều), tín hiệu khôi phục trơn hơn DWT (Discrete Wavelet transforms), nhưng chỉ ứng dụng tốt trong các môi trường chuẩn, nhiễu trắng.…”

Section: Hình 2 Nhiễu Sóng Ecgunclassified

Khử nhiễu tín hiệu điện tim sử dụng giải thuật ngưỡng Wavelet dịch bất biến

Xuân¹

2019

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Xử lý tín hiệu điện tim ECG sử dụng biến đổi wavelet đã được thực hiện hiệu quả trong chẩn đoán y học. Việc xử lý tín hiệu điện tim cơ bản là khử nhiễu và tách các điểm đặc trưng phát hiện các sóng, đỉnh sóng, tính nhịp tim. Các phương pháp xử lý tín hiệu truyền thống thường sử dụng phép biến đổi wavelet rời rạc DWT. Tuy nhiên, DWT có thể sinh ra những dao động giả tạo làm méo dạng tín hiệu. Bài báo đề xuất một giải thuật ngưỡng wavalet dịch bất biến dựa trên cơ sở biến đổi wavelet rời rạc dịch bất biến TIDWT để khử nhiễu tín hiệu điện tim, loại bỏ được những nhiễu phát sinh gây ra do quá trình biến đổi tín hiệu, nâng cao hiệu quả khử nhiễu. Hiệu năng của thuật toán đề xuất sẽ được đánh giá bằng mô phỏng trên đồ thị và tính toán tỉ số tín hiệu trên nhiễu SNR.

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“…Unfortunately, the denoising process is a challenging task due to the overlap of all the noise signals at both low and high frequencies [4]. To prevent noise interference, several approaches have been proposed to denoise ECG signals based on adaptive filtering [5][6][7], wavelet methods [8,9], and empirical mode decomposition [10,11]. However, all these proposed techniques require analytical calculation and high computation; also, because cut-off processing can lose clinically essential components of the ECG signal, these techniques run the risk of misdiagnosis [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

et al. 2020

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The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice.

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ECG Signal Denoising by Discrete Wavelet Transform

Cited by 41 publications

References 0 publications

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification

Khử nhiễu tín hiệu điện tim sử dụng giải thuật ngưỡng Wavelet dịch bất biến

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

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