Electrocardiogram Signal Compression Based On The Modified SPIHT

The increasing of modern medical equipment leads to the explosion of the quantity of medical data. In order to decrease the quantity of memory needed, the original data have to be compressed, while the diagnose information can't be damaged. With the computers' extensive application in the field of heart diseases diagnosis and patient monitoring, data compression technology began to find its application on ECG (Electrocardiogram) data in 1960s. ECG data compression can decrease the channel bandwidth used to transmit the data and the memory consumed to store the data.Wavelet-based compression scheme has been put into practice in the area of compression of signal. The basic ideology of the wavelet based compression algorithm is the signal multi-resolution decomposition. Then encode the decomposition coefficient. Coefficient encoding is the core of wavelet-based compression algorithm. So, the encoding algorithm is a hotspot in the compression area.In this paper, a new ECG signal compression algorithm named Modified Set Partitioning In Hierarchical Trees (MSPIHT), which is based on the SPIHT(Set Partitioning In Hierarchical Trees) algorithm, has been proposed. Several measures have been taken to improve the SPIHT algorithm and these measures include that the lifting wavelet transform is adopted, the extended zerotree structure is redefined, and the threshold optimization.Simulation experiments by using the MSPIHT algorithm and several other compression algorithms have been conducted to verify the validity of the MSPIHT algorithm and test its performance. The experimental results show that the proposed MSPIHT algorithm is valid and can achieve the better compression performance than other compression algorithms based on the wavelet transform. To achieve bigger compression ratio, we also use the proposed algorithm to compress two dimensional ECG data.