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

Area of Science:

• Biomedical Engineering
• Signal Processing
• Data Compression

Background:

• Electrocardiogram (ECG) data requires efficient compression for storage and transmission.
• Existing ECG compression methods have limitations in efficiency and computational cost.
• The Set Partitioning in Hierarchical Trees (SPIHT) algorithm is a successful still image compression technique.

Purpose of the Study:

• To adapt and apply the SPIHT algorithm for one-dimensional ECG data compression.
• To evaluate the efficiency and computational performance of the proposed ECG codec.
• To assess the bit rate control and progressive stream generation capabilities.

Main Methods:

• Modification of the SPIHT algorithm for one-dimensional data.
• Application of the modified SPIHT algorithm to ECG signal compression.
• Experimental evaluation using records from the MIT-BIH arrhythmia database.

Main Results:

• The proposed wavelet ECG codec demonstrates significantly higher compression efficiency.
• The codec exhibits improved computational performance compared to previous ECG compression schemes.
• Exact bit rate control and progressive quality/rate bit stream generation were achieved.

Conclusions:

• The modified SPIHT algorithm provides an efficient and effective solution for ECG data compression.
• This wavelet-based codec offers advantages in terms of speed, compression ratio, and control.
• The developed codec has the potential to improve the management and transmission of ECG data.