Wavelet-based ECG data compression optimization with genetic algorithm

Wu, Tsung-Ching; Hung, King-Chü; Liu, Je-Hung; Liu, Tung-Kuan

doi:10.4236/jbise.2013.67092

Cited by 13 publications

(3 citation statements)

References 14 publications

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“…K.C. Hung et al [23] proposed a genetic algorithm (GA) to optimize the quantization of wavelet ECG signals to compress data, where GA define stationary relationship property to control quantization scales of multi-resolution levels by using a single variable. Quantization scheme with linear distortion characteristic that does not depend on ECG signal is derived to reduce error.…”

Section: Compression Methods For Ecg Signalsmentioning

confidence: 99%

Bidirectional Recurrent Nets for ECG Signal Compression

AL-Saidi

Hindi

2022

j. of Comput. sci. res.

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Electrocardiogram (ECG) is a commonly used tool in biological diagnosis of heart diseases. ECG allows the representation of electrical signals which cause heart muscles to contract and relax. Recently, accurate deep learning methods have been developed to overcome manual diagnosis in terms of time and effort. However, most of current automatic medical diagnosis use long electrocardiogram (ECG) signals to inspect different types of heart arrhythmia. Therefore, ECG signal files tend to require large storage to store and may cause significant overhead when exchanged over a computer network. This raises the need to come up with effective compression methods for ECG signals. In this work, the authors investigate using BERT (Bidirectional Encoder Representations from Transformers) model, which is a bidirectional neural network that was originally designed for natural language. The authors evaluate the model with respect to its compression ratio and information preservation, and measure information preservation in terms of the of the accuracy of a convolutional neural network in classifying the decompressed signal. The results show that the method can achieve up to 83% saving in storage. Also, the classification accuracy of the decompressed signals is around 92.41%. Furthermore, the method enables the user to balance the compression ratio and the required accuracy of the CNN classifiers.

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Section: Compression Methods For Ecg Signalsmentioning

confidence: 99%

Bidirectional Recurrent Nets for ECG Signal Compression

AL-Saidi

Hindi

2022

j. of Comput. sci. res.

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“…But since the heart rate is usually less than 120 beats per minute, the speed of the input information will be slow enough to have a real-time analysis with the appropriate hardware and software. This means that the analysis of the last received model has been completed before the completion of the next ECG model [4,11].…”

Section: Introductionmentioning

confidence: 99%

Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction

Khezripour¹,

Mozaffari²,

Reshadi³

et al. 2023

Biomed. Phys. Eng. Express

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Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias. We proposed a new ECG signal classification method to increase signal classification sensitivity. We smoothed the ECG signal with noise removal filters. A discrete wavelet transform based on an arrhythmic database was applied to extract ECG features. Feature vectors were obtained based on wavelet decomposition energy properties and calculated values of PQRS morphological features. We used the genetic algorithm to reduce the feature vector and determine the input layer weights of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Proposed methods for classifying ECG signals were in different classes of rhythm to diagnose heart rhythm diseases. Training data was with 80% of the data set and test data was with 20% for the whole data set. The learning accuracy for the results of training and test data in the ANN classifier was calculated as 99.9% and 88.92% and in ANFIS as 99.8% and 88.83% respectively. Based on these results, good accuracy was observed.

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“…Optimization of the quantization scheme of wavelet-based electrocardiogram compression, based on a genetic algorithm is described in [8].…”

Section: Introductionmentioning

confidence: 99%

Signal approximation using GA guided wavelet decomposition

Oltean

Ivanciu

Kirei

2015

2015 International Symposium on Signals, Circuits and Systems (ISSCS)

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Signal approximation is a matter of great interest, as working with complete time-sampled signals requires great memory and computational resources. In order to diminish these requirements, signal compression and signal approximation methods are widely used. The paper proposes a signal approximation method, using a genetic algorithm that guides the wavelet decomposition process, by providing specific information, such as: the mother wavelet, the number of selected coefficients, and the decomposition level. The tradeoff between the quality of the signal approximation and its complexity is addressed in the objective function of the genetic algorithm. The method is validated using three test signals, specific to analog circuits. Simulation results prove that the method provides substantial dimensionality reduction, with increased accuracy, which makes it a viable candidate for applications that employ signal storage, transmission, and processing.

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Wavelet-based ECG data compression optimization with genetic algorithm

Cited by 13 publications

References 14 publications

Bidirectional Recurrent Nets for ECG Signal Compression

Bidirectional Recurrent Nets for ECG Signal Compression

Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction

Signal approximation using GA guided wavelet decomposition

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