A Hybrid 2D ECG Compression Algorithm using DCT and Embedded Zero Tree Wavelet

Pal, Hardev Singh; Kumar, Anil; Vishwakarma, Amit; Balyan, L. K.

doi:10.1109/cict56698.2022.9997915

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…The obtained CR was 2.56, and the classification accuracies were 0.966 and 0.990 for the compressed and decompressed databases, respectively. Further, Pal et al [ 18 ] proposed a compression algorithm for 2D ECG signals based on the combination of DCT and embedded zero-tree wavelet. The results showed that the suggested approach could raise the sparsity of the transform domain, which boosts compression effectiveness with a small degradation in reconstruction quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Compression of Bio-Signals Using Block-Based Haar Wavelet Transform and COVIDOA for IoMT Systems

et al. 2023

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Background: Bio-signals are the essential data that smart healthcare systems require for diagnosing and treating common diseases. However, the amount of these signals that need to be processed and analyzed by healthcare systems is huge. Dealing with such a vast amount of data presents difficulties, such as the need for high storage and transmission capabilities. In addition, retaining the most useful clinical information in the input signal is essential while applying compression. Methods: This paper proposes an algorithm for the efficient compression of bio-signals for IoMT applications. This algorithm extracts the features of the input signal using block-based HWT and then selects the most important features for reconstruction using the novel COVIDOA. Results: We utilized two different public datasets for evaluation: MIT-BIH arrhythmia and EEG Motor Movement/Imagery, for ECG and EEG signals, respectively. The proposed algorithm’s average values for CR, PRD, NCC, and QS are 18.06, 0.2470, 0.9467, and 85.366 for ECG signals and 12.6668, 0.4014, 0.9187, and 32.4809 for EEG signals. Further, the proposed algorithm shows its efficiency over other existing techniques regarding processing time. Conclusions: Experiments show that the proposed method successfully achieved a high CR while maintaining an excellent level of signal reconstruction in addition to its reduced processing time compared with the existing techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Compression of Bio-Signals Using Block-Based Haar Wavelet Transform and COVIDOA for IoMT Systems

et al. 2023

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“…Because of these factors, DMWT is typically carried out using a filter bank consisting of four scalar filters. The creation of this four-channel filter bank for a specific Multiwavelet, with its transfer function (5), can be accomplished as described in (6).…”

Section: Multiwavelet Transformmentioning

confidence: 99%

“…Over the years, various researchers have actively contributed to the investigation of ECG signals processing, proposing novel methodologies for sensing [2,3], compression [4,5], classification [6], and detection [7]. Typically, these methodologies incorporate compression techniques applied to the ECG signal, aiming to achieve signal sparsity for the purposes mentioned earlier.…”

Section: Introductionmentioning

confidence: 99%

ECG Sparsity Evaluation on a Multiwavelet Basis

Kromka,

Jusková,

Šaliga

2023

Acta Electrotechnica Et Informatica

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In this paper, an evaluation of the multiwavelet basis’ capability to represent the ECG signal sparsely was performed. The paper includes the mathematical formulation of sparsity, a brief introduction to the multiwavelet transform, as well as details about the simulation setup used for evaluation. Throughout the paper, various multiwavelets were investigated. The reported results show that the BAT and DB multiwavelets performed well, thus they could be used in the ECG signal sparsification. The investigation also focused on the ECG signals displaying deformations associated with illnesses. Preliminary results suggest that multiwavelets may prove beneficial for diverse processing of ECG signals.

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