King-Chu Hung

Wavelet-Based ECG Data Compression System With Linear Quality Control Scheme

Maintaining reconstructed signals at a desired level of quality is crucial for lossy ECG data compression. Wavelet-based approaches using a recursive decomposition process are unsuitable for real-time ECG signal recoding and commonly obtain a nonlinear compression performance with distortion sensitive to quantization error. The sensitive response is caused without compromising the influences of word-length-growth (WLG) effect and unfavorable for the reconstruction quality control of ECG data compression. In this paper, the 1-D reversible round-off nonrecursive discrete periodic wavelet transform is applied to overcome the WLG magnification effect in terms of the mechanisms of error propagation resistance and significant normalization of octave coefficients. The two mechanisms enable the design of a multivariable quantization scheme that can obtain a compression performance with the approximate characteristics of linear distortion. The quantization scheme can be controlled with a single control variable. Based on the linear compression performance, a linear quantization scale prediction model is presented for guaranteeing reconstruction quality. Following the use of the MIT-BIH arrhythmia database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better reconstruction quality control than other wavelet-based methods.

A Novel ECG Data Compression Method Based on Nonrecursive Discrete Periodized Wavelet Transform

In this paper, a novel electrocardiogram (ECG) data compression method with full wavelet coefficients is proposed. Full wavelet coefficients involve a mean value in the termination level and the wavelet coefficients of all octaves. This new approach is based on the reversible round-off nonrecursive one-dimensional (1-D) discrete periodized wavelet transform (1-D NRDPWT), which performs overall stages decomposition with minimum register word length and resists truncation error propagation. A nonlinear word length reduction algorithm with high compression ratio (CR) is also developed. This algorithm supplies high and low octave coefficients with small and large decimal quantization scales, respectively. This quantization process can be performed without an extra divider. The two performance parameters, CR and percentage root mean square difference (PRD), are evaluated using the MIT-BIH arrhythmia database. Compared with the SPIHT scheme, the PRD is improved by 14.95% for 4lesCRles12 and 17.6% for 14lesCRles20

EP-based wavelet coefficient quantization for linear distortion ECG data compression

Reconstruction quality maintenance is of the essence for ECG data compression due to the desire for diagnosis use. Quantization schemes with non-linear distortion characteristics usually result in time-consuming quality control that blocks real-time application. In this paper, a new wavelet coefficient quantization scheme based on an evolution program (EP) is proposed for wavelet-based ECG data compression. The EP search can create a stationary relationship among the quantization scales of multi-resolution levels. The stationary property implies that multi-level quantization scales can be controlled with a single variable. This hypothesis can lead to a simple design of linear distortion control with 3-D curve fitting technology. In addition, a competitive strategy is applied for alleviating data dependency effect. By using the ECG signals saved in MIT and PTB databases, many experiments were undertaken for the evaluation of compression performance, quality control efficiency, data dependency influence. The experimental results show that the new EP-based quantization scheme can obtain high compression performance and keep linear distortion behavior efficiency. This characteristic guarantees fast quality control even for the prediction model mismatching practical distortion curve.

Real-Time Vehicle License Plate Recognition Based on 1-D Discrete Periodic Wavelet Transform

This paper is the use of the scanning window and 1-D discrete periodic wavelet transform and artificial neural network for the license plate recognition. By scanning the image through the one-dimensional discrete periodic wavelet transform, select the image low-frequency coefficients. Therefore, it can improve the rapid license plate recognition of implementation. This article is a directly scanning for the license plate recognition, without make individual character recognition. Therefore, this new method is a kind of real-time recognition, license plate recognition rate of the experimental results can be as high as 94.7%.

An AOCA-based VLSI architecture for non-recursive 2D discrete periodized wavelet transform

All traditional VLSI architectures of the 2D discrete wavelet transform (DWT) are based on the recursive pyramid algorithm. They need an interleaving technique to solve the confliction problem of multilevel input data. This increases circuit complexity and time latency as the decomposition stage is increased. Instead, this paper presents a non-recursive algorithm of separable 2D DPWT (discrete periodized wavelet transform), by which each stages decomposition can be performed independently and the 2D DPWT coefficients of all stages can be obtained simultaneously. Based on the AOCA process, an efficient process called segment accumulation algorithm (SAA) is proposed to overcome the filter growing problem. With the property of using the same original data for all stages, a data sharing technique can be applied in the parallel processing scheme of the SAA for circuit complexity reduction. The SAA provides three fundamental 1D DPWT VLSI architectures with the advantages of requiring no multiplex, and fewer multiplier, adder, and non-interleaving processes. Moreover, the latency of the architecture is independent of the decomposition levels and can be very short.

A linear quality control design for high efficient wavelet-based ECG data compression

In ECG data compression, maintaining reconstructed signal with desired quality is crucial for clinical application. In this paper, a linear quality control design based on the reversible round-off non-recursive discrete periodized wavelet transform (RRO-NRDPWT) is proposed for high efficient ECG data compression. With the advantages of error propagation resistance and octave coefficient normalization, RRO-NRDPWT enables the non-linear quantization control to obtain an approximately linear distortion by using a single control variable. Based on the linear programming, a linear quantization scale prediction model is presented for the quality control of reconstructed ECG signal. Following the use of the MIT-BIH arrhythmia database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better quality control performance than that of other wavelet-based systems.

A high efficient non-recursive DPWT for extracting the transformed coefficients of coarser resolution levels

For the pattern recognition application, a high efficient 1-D non-recursive DPWT (NRDPWT) based on the segment accumulation algorithm (SAA) and the reversible round-off transformation theorem is presented. The SAA is developed to overcome the filter growth problem and facilitate the hardware implementation with the merit of requiring very small amount of multipliers and adders. The latency needed for the SAA-based 1-D NRDPWT is one system clock cycle for any resolution level. In addition, the reversible round-off NRDPWT is developed for solving the bit growth problem existing in the RPA

A Modified Run-Length Coding for the Realization of Wavelet-based ECG Data Compression System

With high compression performance, realization of wavelet-based data compression system is crucial for multi-lead ECG signal recording. In this paper, a modified run-length coding (MRLC) algorithm associated with an efficient quantization scheme is proposed for the realization of a RRO-NRDPWT-based ECG data compression system. The MRLC with regularity and low computational complexity is suitable for hardware implementation at a cost of compression performance. This sacrifice will be compensated by the new quantization scheme. By using the MIT-BIH arrhythmia database, the experimental results show that the proposed scheme can be competitive to other wavelet-based approaches in compression performance. In addition, the MRLC can improve traditional run-length coding by about 13%.

A Modified Run-Length Coding towards the Realization of a RRO-NRDPWT-Based ECG Data Compression System

The wavelet-based approach that combines a reversible round-off nonrecursive discrete periodized wavelet transform (RRO-NRDPWT) and the set partitioning in hierarchical trees (SPIHT) scheme is an efficient ECG data compression. However, this RRO-NRDPWT-based system suffers from the high complexity of the SPIHT scheme during realization. In this paper, a modified run-length coding (MRLC) algorithm is proposed towards the realization of a RRO-NRDPWT-based ECG data compression system. The MRLC with its regularity and low computational complexity is suitable for hardware implementation, but at a cost of compression performance. This sacrifice is compensated by an efficient quantization scheme. By using the MIT-BIH arrhythmia database, the experimental results show that the proposed scheme can compete with the SPIHT scheme for a compression ratio (CR) greater than 8. Hardware simulations are taken using both the Verilog logic simulator with Cadence design platform, and a Xilinx FPGA EP2C35F672C6.

Wavelet octave energy for breast tumor classification on sonography : A new shape feature

Infiltrative nature of lesions is a significant feature of malignant breast lesion in ultrasound images. Characterizing infiltrative nature is crucial for the realization of computer-aided diagnosis system. In this study, the infiltrative nature is regarded as an energy that produces irregularly and considerably local variances in a 1-D signal. The local variances can be enhanced by few high octave energies in 1-D discrete periodized wavelet transform (DPWT). A test dataset of breast sonograms with the lesion contour delineated by an experienced physician and two inexperienced students are built for feature efficacy evaluation. A high individual performance result implies that the proposed feature is well correlated with radiologists perception and closer to match those in trained physician than morphometric parameters. Experimental results also reveal that with a great performance improvement, the proposed feature is suitable for the combination with some morphometric parameters.