Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

Chen, Binqiang; Li, Yang; Zeng, Nianyin

doi:10.1109/access.2019.2907249

Cited by 17 publications

(9 citation statements)

References 38 publications

(43 reference statements)

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“…From the filter bank point of view using multiresolution analysis,

c_{k}

refers to the approximate coefficients at level o and

d_{j k}

refers to the detail coefficients at level j [70 ]. Let

h ()(, n)

and

g ()(, n)

are the discrete finite impulse response functions of

ϕ ()(, x)

and

ψ ()(, x)

, then two‐scale relationships exist for the time‐frequency atoms [68 ]

\begin{matrix} right leftthickmathspace.5em \\ ϕ (x) & = \sum_{n ϵ C} h (n) ϕ (2 x - n) \\ ψ (x) & = \sum_{n ϵ C} g (n) ψ (2 x - n) \end{matrix}

The filterbank implementation of DWT is shown in Fig. 10, where LPF (

H false(w false)

) and HPF (

G false(w false)

) represent Fourier counterparts of finite support filters.…”

Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

“…It is the decomposition of a signal into a set of basis functions consisting of contractions, expansions, and translations of a mother function

ψ ()(, x)

, called the mother wavelet [67 ]. Dyadic WT (DWT) is useful in analysing ECG signals because of its fast computation and its multiresolution property [68 ]. DWT and its multiresolution property are discussed below.…”

Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

See 1 more Smart Citation

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

168

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“…From the filter bank point of view using multiresolution analysis,

c_{k}

refers to the approximate coefficients at level o and

d_{j k}

refers to the detail coefficients at level j [70 ]. Let

h ()(, n)

and

g ()(, n)

are the discrete finite impulse response functions of

ϕ ()(, x)

and

ψ ()(, x)

, then two‐scale relationships exist for the time‐frequency atoms [68 ]

\begin{matrix} right leftthickmathspace.5em \\ ϕ (x) & = \sum_{n ϵ C} h (n) ϕ (2 x - n) \\ ψ (x) & = \sum_{n ϵ C} g (n) ψ (2 x - n) \end{matrix}

The filterbank implementation of DWT is shown in Fig. 10, where LPF (

H false(w false)

) and HPF (

G false(w false)

) represent Fourier counterparts of finite support filters.…”

Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

“…It is the decomposition of a signal into a set of basis functions consisting of contractions, expansions, and translations of a mother function

ψ ()(, x)

Section: Techniques For Ecg Noise Removalmentioning

confidence: 99%

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

168

View full text Add to dashboard Cite

“…The associate editor coordinating the review of this manuscript and approving it for publication was Yonghong Peng. of P, QRS complex and T points [5]. P wave indicates the time taken by the pulse to propagate to both atria which can be used to describe the status of the atria activation.…”

Section: Introductionmentioning

confidence: 99%

“…During the past years, various feature extraction techniques have been reported to expose the distinctive information from ECG signals. These techniques can be primarily categorized into four groups: P-QRS-T complex features extraction method [15], statistical features [16], morphological features [17] and Wavelet transform (WT) [5]. WT is the most popular feature extraction techniques because of its powerful timefrequency localization property.…”

Section: Introductionmentioning

confidence: 99%

Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

Sun

Zeng

He³

2019

IEEE Access

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Electrocardiogram (ECG) signal represents the electrical activity of the heart and playing an increasingly important role for practitioners to diagnose heart diseases. Widely available ECG data and machine learning algorithms present an opportunity to improve the accuracy of automated arrhythmia diagnosis. However, a comprehensive evaluation of morphological arrhythmias for the ECG analysis across a wide variety of diagnostic classes is still a complex task. This paper presents a generic morphological arrhythmias classification method designed for robust and accurate detection of the ECG heartbeat. In this method, the gray-level co-occurrence matrix (GLCM) is employed for features vector description because of its extraordinary statistical feature extraction ability. In addition, the convolutional neural network (CNN) approach is utilized to automatically classification from the generated 3D multi-scale GLCM. Obtained results from the experiments demonstrate that the proposed method in this paper is quite suitable for morphological arrhythmias detection. These findings demonstrate that the GLCM description can efficiently extract the shape features vector for a broad range of distinct arrhythmias from the lead ECGs with high diagnostic performance. The experimental results of the recognition for morphological arrhythmias show the feasibility and effectiveness of the proposed method and could be used to reduce the rate of the misdiagnosed computerized ECG interpretations.INDEX TERMS Morphological arrhythmia, gray-level co-occurrence matrix (GLCM), electrocardiogram (ECG), cardiac arrhythmias classification, convolutional neural network (CNN).

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“…With the development of society and the improvement of economy, humans have been paying more and more attention to their health conditions [1]- [3]. Meanwhile, datadriven intelligent technologies have achieved great success in tackling the health problems and challenges faced by patients [4]- [6], such as introducing EEG signals to monitor and prevent epilepsy, encephalitis and intracranial…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Human Health Monitoring and Time-Frequency Sparse Representation Using EEG and ECG Signals

Geng

et al. 2019

IEEE Access

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In the field of human health monitoring, intelligent diagnostic methods have drawn much attention recently to tackle the health problems and challenges faced by patients. In this paper, an efficient and flexible diagnostic method is proposed, which enables the simultaneous use of a machine learning method and sparsity-based representation technique. Specifically, the proposed method is based on a convolutional neural network (CNN) and generalized minimax-concave (GMC) method. First, measured potential signals, for instance, electroencephalogram (EEG) and electrocardiogram (ECG) signals are directly inputted into the designed network based on CNN for health conditions classification. The designed network adopts small convolution kernels to enhance the performance of feature extraction. In the training process, small batch samples are applied to improve the generalization of the model. Meanwhile, the ''Dropout'' strategy is applied to overcome the overfitting problem in fully connected layers. Then, for a record of the interested EEG or ECG signal, the sparse representation of useful time-frequency features can be estimated via the GMC method. Case studies of seizure detection and arrhythmia signal analysis are adopted to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can effectively identify different health conditions and maximally enhance the sparsity of time-frequency features. INDEX TERMS Feature extraction, convolutional neural network, deep learning, sparse representation, health monitoring.

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Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

Cited by 17 publications

References 38 publications

Review of noise removal techniques in ECG signals

Review of noise removal techniques in ECG signals

Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

Simultaneous Human Health Monitoring and Time-Frequency Sparse Representation Using EEG and ECG Signals

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