An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet

Mohamed, Muhidin; Deriche, Mohamed

doi:10.5120/16850-6712

Cited by 22 publications

(12 citation statements)

References 8 publications

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“…1) Primary fading dynamics cognition based on partial fading data By referring to [18], Daubechies4(db4) is selected accompanied with decomposition layers of 5. Fig.…”

Section: A Prediction Without Consideration Of Udp Andcre: the Proposementioning

confidence: 99%

Degradation Dynamics Cognition and Prediction of Li-Ion Battery: An Integrated Methodology for Alleviating Range Anxiety

et al. 2020

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We report an integrated methodology (FMC-XGBoost) that mainly consists of a five-state nonhomogeneous Markov chain model (FMC) and XGBoost model. Unlike those existing methods in which capacity fading processes are assumed to be irreversible, the proposed integrated methodology can combine user-specific driving patterns (UDP) and capacity recovery effects (CRE) to predict battery fading dynamics even with partially available data for an individual battery. The parameters of the constructed FMC model are linked to the known physicochemical and material properties of Li-ion battery fading dynamics, which aims to cognize and predict the primary fading dynamics, and the proposed XGBoost model is to cognizes and predicts the fluctuation dynamics regarding UDP & CRE. To comprehensively verify the capabilities of the proposed integrated methodology, a series of cases and comparisons are conducted and analysed based on partial available fading data by selecting batteries to simulate situations of individual differences and different UDPs & CREs. The averages of MAE, MRE and RMSE are approximately 0.0128, 0.9251%, and 0.0153 respectively even when only 60% of the data are available. All verifications and comparison analyses reveal that the proposed integrated methodology provides an accurate, robust, stable, and general way to cognize and predict battery fading dynamics during usage, and subsequently to alleviate range anxiety for batteries in real applications. INDEX TERMS FMC-XGBoost, fading dynamics prediction, user-specific driving patterns, capacity recovery effects, Li-ion battery, range anxiety.

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“…1) Primary fading dynamics cognition based on partial fading data By referring to [18], Daubechies4(db4) is selected accompanied with decomposition layers of 5. Fig.…”

Section: A Prediction Without Consideration Of Udp Andcre: the Proposementioning

confidence: 99%

Degradation Dynamics Cognition and Prediction of Li-Ion Battery: An Integrated Methodology for Alleviating Range Anxiety

et al. 2020

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“…The DWT has different representations depending on the type of mother wavelet employed. Daubechies wavelets [48] have been commonly used in classification of one-dimensional signals such as those from electroencephalography and electrocardiography [49], [50]. Hence, we adopted the Daubechies 5 as wavelet function, along with its scaling function.…”

Section: Wavelet Analysismentioning

confidence: 99%

User Recognition Based on Human Body Impulse Response: A Feasibility Study

et al. 2020

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Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the k-nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR. INDEX TERMS Biosignal, human body channel, identification, impulse response, user recognition.

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“…WT consists of continuous WT and discrete WT (DWT). In many aspects, DWT can be used for the ECG signal feature extraction (Mohamed & Deriche, 2014;Hazarika, Chen, Tsoi, & Sergejew, 1997). DWT is defined as follows:…”

Section: Cfase Feature Extractionmentioning

confidence: 99%

Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

Zhang

2019

Expert Systems

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Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.

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An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet

Cited by 22 publications

References 8 publications

Degradation Dynamics Cognition and Prediction of Li-Ion Battery: An Integrated Methodology for Alleviating Range Anxiety

Degradation Dynamics Cognition and Prediction of Li-Ion Battery: An Integrated Methodology for Alleviating Range Anxiety

User Recognition Based on Human Body Impulse Response: A Feasibility Study

Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

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