Using Wavelet transform in processing real-time ECG signals?
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The wavelet transform is used in processing real-time ECG signals for various purposes such as denoising, restoration, and arrhythmia classification. An optimized de-noising method based on the wavelet transform is proposed to remove noise from ECG signals, achieving high-precision diagnosis . The wavelet transform is also applied to discriminate mechanical defects in signals and determine their extents, although classification methods are necessary to achieve accurate results . Another study proposes an algorithm for arrhythmia classification using the wavelet transform, which decomposes signals at multiple levels and facilitates simultaneous location in time and frequency domains . A universal ECG signal arrhythmia classification system is proposed, utilizing the discrete wavelet transform and wavelet packet transform, with the wavelet packet transform-based algorithm being the most superior method . Additionally, the wavelet scattering transform is used to automatically classify arrhythmia ECG heartbeats, achieving high accuracy and assisting physicians in ECG interpretation .
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| Papers (5) | Insight |
|---|---|
| The paper proposes a novel approach using wavelet scattering transform for automatic classification of arrhythmia ECG heartbeats, achieving high accuracy. | |
2 Citations | The paper proposes a universal ECG signal classification system using the wavelet transform, specifically the discrete wavelet transform (DWT) and the wavelet packet transform (WPT). |
07 Apr 2022 1 Citations | The proposed algorithm uses Undecimated Discrete Wavelet Transform (UDWT) for processing and classifying real-time ECG signals. |
| The paper discusses the use of wavelet transform to discriminate mechanical defects, not specifically for processing real-time ECG signals. | |
| Yes, the paper introduces an optimized de-noising method based on wavelet transform for removing noise from ECG signals. |
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