Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm

By Zhitao Lu, Dongyoun Kim, William A. Pearlman. IEEE Transactions on Biomedical Engineering, 2000

525 citations recorded by the source corpus.

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Leveraging Fog Computing for Healthcare IoT (2018, background): “For example, lossless ECG compression methods [19, 20, 21] cannot be run in many types of sensors.”
Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach (2018, methodology): “For instance, many popular lossless ECG compression methods [34, 35, 36] do not fit to many types of sensors while lossy compression methods, for example a method introduced by Yu et al.”
Wavelet transforms and the ECG: a review (2005, methodology): “with sleep apnoea/hyponeoa syndrome (SAHS). They compared their spectral analysis of the HRV signals with the current screening method of pulse oximetry. Their results indicated that spectral analysis of HRV appears to provide a better indicator of SAHS than oximetry in non-REM sleep and a comparabl”
Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes (2011, methodology): “Interestingly, these algorithms are all based on the digital wavelet transform (DWT), namely, the embedded zerotree wavelet (EZW) [8], the set partitioning in hierarchical trees (SPIHT) [9], and thresholding-based algorithms [10], [11].”
Discrete-Time Speech Signal Processing: Principles and Practice (2001, methodology): “[69] LU Z, kim DY, Pearlman WA, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm” in IEEE Transactions on Biomedical Engineering Vol.”