Journal ArticleDOI
Preserving Abnormal Beat Morphology in Long-Term ECG Recording: An Efficient Hybrid Compression Approach
TLDR
A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio of ECG data, and a useful outcome is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation.Abstract:
In long-term electrocardiogram (ECG) recording for arrhythmia monitoring, using a uniform compression strategy throughout the entire data to achieve high compression efficiency may result in unacceptable distortion of abnormal beats. The presented work addressed a solution to this problem, rarely discussed in published research. A support vector machine (SVM)-based binary classifier was implemented to identify the abnormal beats, achieving a classifier sensitivity (SE) and negative predictive value (NPV) of 99.89% and 0.003%, respectively with 34 records from MIT-BIH Arrhythmia database (mitdb). A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio (CR) of ECG data. A wavelet-based compression for the abnormal beats was implemented, while the consecutive normal beats were compressed in groups using a hybrid encoder, employing a combination of wavelet and principal component analysis. Finally, a neural network-based intelligent model was used, which was offline tuned by a particle swarm optimization (PSO) technique, to allocate optimal quantization level of transform domain coefficients generated from the hybrid encoder. The proposed technique was evaluated with four types of morphology tags, “A,” “F,” “L,” and “V,” from mitdb database, achieving less than 2% PRDN and less than 1% in two diagnostic distortion measures for abnormal beats. Overall, an average CR of 19.78 and PRDN of 3.34% was obtained. A useful outcome of the proposed technique is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation.read more
Citations
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Journal ArticleDOI
ECG compressed sensing method with high compression ratio and dynamic model reconstruction
TL;DR: An alternative method for compressed sensing and reconstruction of ECG that is patient agnostic and offers a high compression ratio is introduced that keeps the structure of heartbeats preserved including the exact positions of R waves, and it reduces the noise interfering with ECG signals.
Proceedings ArticleDOI
A Dynamic Approach for Compressed Sensing of Multi–lead ECG Signals
TL;DR: A dynamic method based on Compressed Sensing to reconstruct multi-lead electrocardiography signals in support of Internet-of-Medical-Things by dynamically evaluated through the signal samples acquired by the first lead.
Journal ArticleDOI
Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network
TL;DR: 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.
Journal ArticleDOI
Attention-Based Convolutional Denoising Autoencoder for Two-Lead ECG Denoising and Arrhythmia Classification
TL;DR: A novel attention-based convolutional denoising autoencoder (ACDAE) model is proposed that utilizes a skip-layer and attention module for reliable reconstruction of ECG signals from extreme noise conditions that outperformed the most cited published works.
Journal ArticleDOI
Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT.
TL;DR: This study points out drawbacks of compression algorithms, presents new compression algorithm which is properly described, tested and objectively compared with other authors and serves as an example how the standardization should look like.
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ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier
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ECG Signal Compression and Classification Algorithm With Quad Level Vector for ECG Holter System
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