Biomedical subjects

S G Miaou

Publications and source records attributed to S G Miaou.

4 recordsLinked to original sources

PubMed2001

Improving storage efficiency of vector quantization codebook for physiological quasi-periodic signals.

A quasi-periodic signal is a periodic signal with period and amplitude variations. The electrocardiogram (ECG) and several physiological signals can be treated as quasi-periodic. Vector quantization (VQ) is a valuable and universal tool for signal compression. However, the periodicity of a quasi-periodic signal causes data redundancy in the VQ codebook, where many codevectors are highly correlated. This paper explores the codebook (CB) redundancy in order to increase storage efficiency for physiological quasi-periodic signals. A quantitative CB redundancy measure and two redundancy reducing algorithms are proposed. Both algorithms use a mixed CB structure containing one and two-dimensional CBs. The first algorithm is applied to a CB directly, and the second one uses an LBG-like training algorithm to obtain a storage-efficient CB from a set of training vectors. With the MIT/BIH ECG database, the experimental results show that both algorithms can reduce the CB redundancy effectively with essentially no loss of signal quality. For comparison, the mean-shape VQ (MSVQ) proposed by Cárdenas-Barrera and Lorenzo-Ginori for ECG compression is implemented and the resulting average percent of the root-mean-square difference (PRD) is 10.78%. By using the first algorithm, the CB storage space is reduced by 40% and the resulting average PRD is 10.87%. The second algorithm can reduce the CB storage space by 75% and the average PRD is 10.27%, which is even better than the original MSVQ.

Algorithms↗

PubMed2001

Multichannel ECG compression using multichannel adaptive vector quantization.

Adaptive vector quantization (AVQ) is a recently proposed approach for electrocardiogram (ECG) compression. The adaptability of the approach can be used to control the quality of reconstructed signals. However, like most of other ECG compression methods, AVQ only deals with the single-channel ECG, and for the multichannel (MC) ECG, coding ECG signals on a channel by channel basis is not efficient, because the correlation across channels is not exploited. To exploit this correlation, an MC version of AVQ is proposed. In the proposed approach, the AVQ index from each channel is collected to form a new input vector. The vector is then vector quantized adaptively using one additional codebook called index codebook. Both the MIT/BIH database and a clinical Holter database are tested. The experimental results show that, for exactly the same quality of reconstructed signals, the MC-AVQ performs better than single-channel AVQ in terms of bit rate. A theoretical analysis supporting this result is also demonstrated in this paper. For the same and relatively good visual quality, the average compressed data rate/channel is reduced from 293.5 b/s using the single-channel AVQ to 238.2 b/s using the MC-AVQ in the MIT/BIH case.

Algorithms↗

PubMed2000

Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted codevectors.

A discrete semi-periodic signal can be described as x(n) = x(n + T + delta T) + delta x, [symbol: see text] n, where T is the fundamental period, delta T represents a random period variation, and delta x is an amplitude variation. Discrete ECG signals are treated as semi-periodic, where T and delta x are associated with the heart beat rate and the baseline drift, respectively. These two factors cause coding inefficiency for ECG signal compression using vector quantisation (VQ). First, the periodic characteristic of ECG signals creates data redundancy among codevectors in a traditional two-dimensional codebook. Secondly, the fixed codevectors in traditional VQ result in low adaptability to signal variations. To solve these two problems simultaneously, an adaptive VQ (AVQ) scheme is proposed, based on a one-dimensional (1D) codebook structure, where codevectors are overlapped and linearly shifted. To further enhance the coding performance, the delta x term is extracted and encoded separately, before 1D-AVQ is applied. The data in the first 3 min of all 48 ECG records from the MIT/BIH arrhythmic database are used as the test signals, and no codebook training is carried out in advance. The compressed data rate is 265.2 +/- 92.3 bits s-1 at 10.0 +/- 4.1% PRD. No codebook storage or transmission is required. Only a very small codebook storage space is needed temporarily during the coding process. In addition, the linearly shifted nature of codevectors makes this easier to be hardware implemented than any existing AVQ method.

Arrhythmias, Cardiac↗

PubMed2000

Quality driven gold washing adaptive vector quantization and its application to ECG data compression.

The gold washing (GW) adaptive vector quantization (AVQ) (GW-AVQ) is a relatively new scheme for data compression. The adaptive nature of the algorithm provides the robustness for wide variety of the signals. However, the performance of GW-AVQ is highly dependent on a preset parameter called distortion threshold (dth) which must be determined by experience or trial-and-error. We propose an algorithm that allows us to assign an initial dth arbitrarily and then automatically progress toward a desired dth according to a specified quality criterion, such as the percent of root mean square difference (PRD) for electrocardiogram (ECG) signals. A theoretical foundation of the algorithm is also presented. This algorithm is particularly useful when multiple GW-AVQ codebooks and, thus, multiple dth's are required in a subband coding framework. Four sets of ECG data with entirely different characteristics are selected from the MIT/BIH database to verify the proposed algorithm. Both the direct GW-AVQ and a wavelet-based GW-AVQ are tested. The results show that a user specified PRD can always be reached regardless of the ECG waveforms, the initial selection of dth or whether a wavelet transform is used in conjunction with the GW-AVQ. An average result of 6% in PRD and 410 bits/s in compressed data rate is obtained with excellent visual quality.

Algorithms↗