Compression, Denoising and Classification of ECG Signals using the Discrete Wavelet Transform and Deep Convolutional Neural Networks

Chowdhury, M.; Poudel, Khem Narayan; Hu, Yating

doi:10.1109/spmb50085.2020.9353618

Cited by 6 publications

(11 citation statements)

References 9 publications

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“…During transmission, ECG signals can be corrupted by various types of noise, which can make it challenging to accurately interpret the information contained within the signal. As a result, pre-processing of ECG signals is critical for continuous monitoring of these signals [5]. ECG signals are typically recorded for several hours or even days to facilitate continuous monitoring of a patient's health.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Sharma

Sunkaria

2023

Biomed. Phys. Eng. Express

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Background:In telecardiology, the bio-signal acquisition processing and communication for clinical purposes occupies larger storage and significant bandwidth over a communication channel. Electrocardiograph (ECG) compression with effective reproductivity is highly desired. In the present work, a compression technique for ECG signals with less distortion by using a non-decimated stationary wavelet with a run-length encoding scheme has been proposed.Method:In the present work non-decimated stationary wavelet transform (NSWT) method has been developed to compress the ECG signals. The signal is subdivided into N levels with different thresholding values. The wavelet coefficients having values larger than the threshold are evaluated and the remaining are suppressed. In the presented technique, the biorthogonal wavelet is employed as it improves the compression ratio as well percentage root means square ratio when compared to the existing method and exhibits better results. Then the coefficients are pre-processed by using the Savitzky-Golay filter which discards the corrupted signals. The wavelet coefficients are quantized using dead-zone quantization which removes the value close to zeros. To encode these values run length encoding scheme is applied and compressed ECG signals are obtained.Results:The presented methodology has been evaluated on the MIT-BIH arrhythmias database which contains 4800 ECG fragments from forty-eight clinical records. An average compression ratio = 33.12, PRD =1.99, NPRD =2.53, and QS = 16.57 is obtained in the proposed technique.Conclusion:The proposed technique exhibits a high compression ratio and reduces distortion compared to the existing method.

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Section: Introductionmentioning

confidence: 99%

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Sharma

Sunkaria

2023

Biomed. Phys. Eng. Express

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“…Changes in any of these fixed values lead to heart arrhythmia. Heart arrhythmia is the change in the heart's normal rhythm that, if endured, leads to for sudden cardiac death (SCD) [9]. Arrhythmias can be categorized into two types: morphological, i.e., one irregular beat, and rhythmic, i.e., set of irregular rhythms [10].…”

Section: Introductionmentioning

confidence: 99%

“…These noises can be reduced, but the uncertainty present in defining the boundaries of peaks and peak intervals may reduce the usability of these methods [26]. Frequency-domain techniques such as Fourier transform (FT), discrete Fourier transform (DFT), Discrete Cosine Transform (DCT) [27], and discrete wavelet transform (DWT) [9,[28][29][30] are used to transform the time domain signals into the frequency domain. The ECG is a non-stationary signal, and FT cannot determine the time of the occurrence of the frequency component [9].…”

Section: Introductionmentioning

confidence: 99%

“…Frequency-domain techniques such as Fourier transform (FT), discrete Fourier transform (DFT), Discrete Cosine Transform (DCT) [27], and discrete wavelet transform (DWT) [9,[28][29][30] are used to transform the time domain signals into the frequency domain. The ECG is a non-stationary signal, and FT cannot determine the time of the occurrence of the frequency component [9]. It requires a constant window size to locate all the frequency components, and this can be achieved through short-time Fourier transform (STFT).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network

Soni

Nagpal

Garg³

et al. 2022

Electronics

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Incalculable numbers of patients in hospitals as a result of COVID-19 made the screening of heart patients arduous. Patients who need regular heart monitoring were affected the most. Telecardiology is used for regular remote heart monitoring of such patients. However, the resultant huge electrocardiogram (ECG) data obtained through regular monitoring affects available storage space and transmission bandwidth. These signals can take less space if stored or sent in a compressed form. To recover them at the receiver end, they are decompressed. We have combined telecardiology with automatic ECG arrhythmia classification using CNN and proposed an algorithm named TELecardiology using a Deep Convolution Neural Network (TELDCNN). Discrete cosine transform (DCT), 16-bit quantization, and run length encoding (RLE) were used for compression, and a convolution neural network (CNN) was applied for classification. The database was formed by combining real-time signals (taken from a designed ECG device) with an online database from Physionet. Four kinds of databases were considered and classified. The attained compression ratio was 2.56, and the classification accuracies for compressed and decompressed databases were 0.966 and 0.990, respectively. Comparing the classification performance of compressed and decompressed databases shows that the decompressed signals can classify the arrhythmias more appropriately than their compressed-only form, although at the cost of increased computational time.

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“…It is a non-trivial programming task to setup a computer to solve the equations of a mathematical model. There are existing toolkits which solve this issue, but they have limitations as given in [5]. Once the model has been set up, it needs to be used in a variety of experimental protocols, to quantify the behavior of the model and any abnormal conditions the experimentalist is interested in.…”

Section: Introductionmentioning

confidence: 99%

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Jabbar¹,

Alwan²,

Ali³

2022

PEN

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Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field. The Convolutional Neural Network (CNN) algorithm offer a valuable way of gaining insight into the dynamic behaviors of the heart, in normal and pathological conditions. Great efforts have been put into modelling the ventricles, whilst the atria have received less focus. This research therefore concentrates on developing models of the heart ECG atria using deep learning. The research developed an experimental result on MIT-BIH dataset for modelling myocyte electrophysiology and excitation waves in 1D & 2D tissues. It includes optimizations such as adaptive stimulus protocols. As examples of application, it is used to investigate effects of a novel anion bearing current on heart atrial excitation and the effect of remodeling on atrial myocyte electrical heterogeneity. A computationally efficient CNN anatomically based model of the heart atria is constructed. The 3D-CNN model includes heterogeneous, biophysically detailed electrophysiology and conduction anisotropy. The full model activates in 121 ms in heart rhythm, in close agreement with clinical ECG data. The model is used, with the toolkit, to investigate the function effects of S140G mutation in MIT-BIH dataset which is associated with familial. The 3D-CNN model forms the core of a boundary element model of the P-wave Body Surface Potential (BSP). The CNN model incorporates representations of the heart blood masses. Generated ECGs show qualitative agreement with clinical data. Their morphology is as expected for a healthy person, with a lead duration of 103 ms. The CNN model is used to verify an existing algorithm for focal atrial tachycardia location and in providing explanation for a novel clinical phenomenon, using CNN with 99.27% accuracy. Models of the human atria and body surface potential are constructed. The models are validated against both experimental and clinical data. These models are suitable to use as the platform for further research.

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Compression, Denoising and Classification of ECG Signals using the Discrete Wavelet Transform and Deep Convolutional Neural Networks

Cited by 6 publications

References 9 publications

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

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