A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm (A. Said and W.A. Pearlman, IEEE Trans. Ccts. Syst. II, vol. 6, p. 243-50, 1996) has achieved notable success in still image coding. The authors modified the algorithm for the one-dimensional case and applied it to compression of ECG data. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate.

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

Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.

Wavelet and wavelet packet compression of electrocardiograms

A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is-insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.

An efficient coding algorithm for the compression of ECG signals using the wavelet transform

The method of truncated singular value decomposition (SVD) is proposed for electrocardiogram (ECG) data compression. The signal decomposition capability of SVD is exploited to extract the significant feature components of the ECG by decomposing the ECG into a set of basic patterns with associated scaling factors. The signal information is mostly concentrated within a certain number of singular values with related singular vectors due to the strong interbeat correlation among ECG cycles. Therefore, only the relevant parts of the singular triplets need to be retained as the compressed data for retrieving the original signals. The insignificant overhead can be truncated to eliminate the redundancy of ECG data compression. The Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database was applied to evaluate the compression performance and recoverability in the retrieved ECG signals. The approximate achievement was presented with an average data rate of 143.2 b/s with a relatively low reconstructed error. These results showed that the truncated SVD method can provide efficient coding with high-compression ratios. The computational efficiency of the SVD method in comparing with other techniques demonstrated the method as an effective technique for ECG data storage or signals transmission.

ECG data compression using truncated singular value decomposition

A new integrated design approach for an optimal zonal wavelet-based ECG data compression (OZWC) method for a mobile telecardiology model is presented. The hybrid implementation issues of this wavelet method with a GSM-based mobile telecardiology system are also introduced. The performance of the mobile system with compressed ECG data segments selected from the MIT-BIH arrhythmia database is evaluated in terms of bit error rate (BER), percent rms difference (PRD), and visual clinical inspection. The compression performance analysis of the OZWC is compared with another wavelet-based (Discrete Symmetric Wavelet Compression) approach. The optimal wavelet algorithm achieved a maximum compression ratio of 18:1 with low PRD ratios. The mobile telemedical simulation results show the successful compressed ECG transmission at speeds of 100 (km/h) with BER rates of less than 10/sup -15/, providing a 73% reduction in total mobile transmission time with clinically acceptable reconstruction of the received signals. This approach will provide a framework for the design and functionality issues of GSM-based wireless telemedicine systems with wavelet compression techniques and their future integration for the next generation of mobile telecardiology systems.

Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system

This paper proposes a new ECG signal compression algorithm using a discrete symmetric wavelet transform. This proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels. Using the new method, a compression ratio of 8 to 1 can be achieved with PRD=3.9%, in contrast to the AZTEC compression ratio of 6.8 to 1 with PRD=10.0% and the fan algorithm compression ratio of 7;4 to 1 with PRD=8.1%.

A. Djohan

Papers

ECG compression using discrete symmetric wavelet transform