Compression of ECG data by vector quantization

Beat-based ECG compression using gain-shape vector quantization

An electrocardiogram (ECG) data compression scheme is presented using the gain-shape vector quantization. The proposed approach utilizes the fact that ECG signals generally show redundancy among adjacent heartbeats and adjacent samples. An ECG signal is QRS detected and segmented according to the detected fiducial points. The segmented heartbeats are vector quantized, and the residual signals are calculated and encoded using the AREA algorithm. The experimental results show that with the proposed method both visual quality and the objective quality are excellent even in low bit rates. An average PRD of 5.97% at 127 b/s is obtained for the entire 48 records in the MIT-BIH database. The proposed method also outperforms others for the same test dataset.

High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms

Biomedical waveforms, such as electrocardiogram (ECG) and arterial pulse, always possess a lot of important clinical information in medicine and are usually recorded in a long period of time in the application of telemedicine. Due to the huge amount of data, to compress the biomedical waveform data is vital. By recognizing the strong similarity and correlation between successive beat patterns in biomedical waveform sequences, an efficient data compression scheme mainly based on pattern matching is introduced in this paper. The waveform codec consists mainly of four units: beat segmentation, beat normalization, two-stage pattern matching and template updating and residual beat coding. Three different residual beat coding methods, such as Huffman/run-length coding, Huffman/run-length coding in discrete cosine transform domain, and vector quantization, are employed. The simulation results show that our compression algorithms achieve a very significant improvement in the performances of compression ratio and error measurement for both ECG and pulse, as compared with some other compression methods.

Data compression for arterial pulse waveform

The arterial pulse possesses important clinical information in traditional Chinese medicine. It is usually recorded for a long period of time in the applications of telemedicine and PACS systems. Due to the huge amount of data, by recognizing the strong correlation between successive beat patterns in arterial pulse waveform sequences, a novel and efficient data compression scheme based mainly on pattern matching is introduced. The simulation results show that our coding scheme can achieve a very high compression ratio and low distortion for arterial pulse waveform.

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.

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.

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.

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.

ECG data compression using cut and align beats approach and 2-D transforms

A new electrocardiogram (ECG) data compression method is presented which employs a two dimensional (2-D) transform. This 2-D transform method utilizes the fact that ECG signals generally show two types of redundancies--between adjacent heartbeats and between adjacent samples. A heartbeat data sequence is cut and beat-aligned to form a 2-D data array. Any 2-D compression method can then be applied. Transform coding using the 2-D discrete cosine transform (DCT) [2-D DCT] is employed here as an example. Using selections from the MIT-BIH arrhythmia and Medtronic databases, results are presented that illustrate substantial improvement in compression ratio over one-dimensional methods for comparable percent root-mean-square difference (PRD).

Mean-shape vector quantizer for ECG signal compression

A direct waveform mean-shape vector quantization (MSVQ) is proposed here as an alternative for electrocardiographic (ECG) signal compression. In this method, the mean values for short ECG signal segments are quantized as scalars and compression of the single-lead ECG by average beat substraction and residual differencing their waveshapes coded through a vector quantizer. An entropy encoder is applied to both, mean and vector codes, to further increase compression without degrading the quality of the reconstructed signals. In this paper, the fundamentals of MSVQ are discussed, along with various parameters specifications such as duration of signal segments, the wordlength of the mean-value quantization and the size of the vector codebook. The method is assessed through percent-residual-difference measures on reconstructed signals, whereas its computational complexity is analyzed considering its real-time implementation. As a result, MSVQ has been found to be an efficient compression method, leading to high compression ratios (CR's) while maintaining a low level of waveform distortion and, consequently, preserving the main clinically interesting features of the ECG signals. CR's in excess of 39 have been achieved, yielding low data rates of about 140 bps. This compression factor makes this technique especially attractive in the area of ambulatory monitoring.