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PubMed1992

Data compression of ECG's by high-degree polynomial approximation.

A method for the compression of ECG data is presented. The method is based on high-degree polynomial expansions. Data rates of about 350 bits per second are achievable at an acceptable signal quality. The high compression is obtained by a carefully selected subdivision of the ECG signal into intervals that make optimal use of the special properties of the polynomial base functions. Each interval corresponds to one ECG period. The method is compared to the discrete cosine transform and is found to yield a significantly higher data compression for a given signal quality (quantified by mean squared error and peak error).

Electrocardiography↗
PubMed1992

On AR modelling for MEG spectral estimation, data compression and classification.

The use of the autoregressive (AR) model for magnetoencephalogram (MEG) processing is examined and compared to other methods. Spectral estimation, classification and data compression of MEG signals are studied. In application to spectral estimation the AR model is compared to the classical modified periodogram method. Also, AR modelling appears to perform very successfully when used for the classification of normal and epileptic MEG signals. Finally, the 17:1 to 23:1 data compression achieved by AR modelling, along with the above-mentioned advantages, render it suitable for storage applications. For comparison, the method of feature selection via orthogonal expansion is used as a tool to achieve data reduction. It is seen that while effective, this is less drastic than the compression of data volume achieved by AR modelling.

Brain Mapping↗
PubMed2000

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

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 data base 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(-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.

Cardiology↗
PubMed1991

Data compression: effect on diagnostic accuracy in digital chest radiography.

High-resolution digital images make up very large data sets that are relatively slow to transmit and expensive to store. Data compression techniques are being developed to address this problem, but significant image deterioration can occur at high compression ratios. In this study, the authors evaluated a form of adaptive block cosine transform coding, a new compression technique that allows considerable compression of digital radiographs with minimal degradation of image quality. To determine the effect of data compression on diagnostic accuracy, observer tests were performed with 60 digitized chest radiographs (2,048 x 2,048 matrix, 1,024 shades of gray) containing subtle examples of pneumothorax, interstitial infiltrate, nodules, and bone lesions. Radiographs with no compression, with 25:1 compression, and with 50:1 compression ratios were presented in randomized order to 12 radiologists. The results suggest that, with this compression scheme, compression ratios as high as 25:1 may be acceptable for primary diagnosis in chest radiology.

Humans↗
PubMed1996

[Evaluation of data compression in gastrointestinal examinations using computed radiography].

The purpose of this study was to evaluate the clinical utility of Type III irreversible data compression of computed radiography (CR) in gastrointestinal examinations. Simulated gastric lesions created on a specially made phantom gastric mucosa as well as normal and abnormal cases selected from upper gastrointestinal radiographic examinations and barium enemas were used. Twenty-five sets of original and compressed CR images were obtained for each category of normal and abnormal cases. Visual ranking and a receiver operating characteristic (ROC) study using these images were carried out by 5 radiologists. By visual ranking, deterioration of image quality on the compressed images was recognized in 32.8 to 58.4% of the phantom gastric mucosa images, 23.2 to 75.2% of the upper gastrointestinal examinations, and 21.6 to 81.6% of the barium enemas. However, this deterioration was subtle and the analysis of ROC curves revealed no significant difference in detectability of the lesion between original and compressed images. It is concluded that this Type III irreversible data compression is clinically applicable and useful in gastrointestinal examinations using CR.

Barium Sulfate↗
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↗
PubMed1993

ECG data compression by modeling.

The paper presents a novel algorithm for data compression of single lead electrocardiogram (ECG) data. The method is based on parametric modeling of the discrete cosine transformed ECG signal. Improved high frequency reconstruction is achieved by separately modeling the low and the high frequency regions of the transformed signal. Differential pulse code modulation is applied on the model parameters to obtain a further increase in the compression. Compression ratios up to 1:40 were achieved without significant distortion.

Algorithms↗
PubMed1992

ECG data compression by modeling.

This paper presents a novel algorithm for data compression of single lead Electrocardiogram (ECG) data. The method is based on Parametric modeling of the Discrete Cosine Transformed ECG signal. Improved high frequency reconstruction is achieved by separately modeling the low and the high frequency regions of the transformed signal. Differential Pulse Code Modulation is applied on the model parameters to obtain a further increase in the compression. Compression ratios up to 1:40 were achieved without significant distortion.

Algorithms↗
PubMed1995

Data compression of x-ray cardio-angiographic image series.

Medical x-ray images are increasingly stored and transmitted in a digital format. To reduce the required storage space and transmission bandwidth, data compression can be applied. In this paper we describe a new method for data compression of cardio-angiographic x-ray image series. The method is based on so-called overlapped-transform coding. A comparison with the well-known block-based transform-coding methods JPEG and MPEG is presented. We found that overlapped-transform coding does not introduce any blocking artefacts, in contrast to block-based transform coding, which introduces clearly visible blocking artefacts at compression ratios above 8. Clinical evaluations of the new method have pointed out that the image quality obtained at a compression ratio of 12 is adequate for diagnostic applications.

Angiocardiography↗
PubMed1992

ECG data compression by corner detection.

An ECG sampled at a rate of 360, 500 samples s-1 or more produces a large amount of redundant data that are difficult to store and transmit. A process is therefore required to represent the signals with clinically acceptable fidelity and with the least code bits possible. In the paper, a real-time ECG data compressing algorithm, CORNER, is presented. CORNER is an efficient algorithm which locates significant samples and at the same time encodes the linear segments between them using linear interpolation. The samples selected include, but are not limited to, the samples that are significantly displaced from the encoded signal such that the allowed maximum error is limited to a constant epsilon which is specified by the users. The way in which CORNER computes the displacement of a sample from the encoded signal guarantees that the high activity regions are more accurately coded. The results are compared with those of the well known data compression algorithm, AZTEC, which is also a real-time algorithm. It is found that, under the same bit rate, a considerable improvement of the signal-to-noise ratio (SNR) and root mean square error (RMSerr) can be achieved by employing the proposed CORNER algorithm. An average value of SNR (RMSerr) of 27.0 dB (5.668) can be achieved even at an average bit rate of 0.79 bit sample-1 by employing CORNER, whereas the average value of SNR (RMSerr) achieved by AZTEC under the same bit rate is 16.60 dB (19.368).

Algorithms↗
PubMed2002

Dynamic image data compression in the spatial and temporal domains: clinical issues and assessment.

In our previous work, we developed a novel approach to dynamic image data compression, and demonstrated that very high compression ratios can be achieved while preserving relevant kinetic information. However, the technique has not yet been assessed with clinical data. Many issues need to be addressed to tailor the method for clinical use. In this paper, we apply the compression technique to dynamic [18F] 2-fluoro-deoxy-glucose (FDG) brain positron emission tomography (PET) data, using a five-parameter model to include cerebral blood volume (CBV) and partial volume (PV) effects. Functional images generated from the compressed data are compared with those from the original uncompressed data. We show that the storage requirements for a typical clinical dynamic PET image data set can be reduced by more than 95%, without degradation of image quality. Furthermore, the technique greatly reduces the computational complexity of further clinical image postprocessing such as smoothing and generation of functional images. It is expected that the compression technique will be of benefit in image data management and telemedicine.

Algorithms↗
PubMed1997

Image data compression.

The tutorial will expand on all of the topics outlined above and will provide additional information on other compression techniques. The practical implications of data compression and the important considerations in choosing a compression scheme will also be discussed. Development of new compression algorithms remains an active area of investigation and will continue to increase the degree of compression possible.

Algorithms↗
PubMed1988

A data compression algorithm for the electroencephalogram.

This paper describes a data compression algorithm for the EEG using a local error measure. The algorithm discards input signal samples provided they can be reconstructed from the stored samples with an error smaller than a given threshold, and according to simple reconstruction functions (the hold, the ramp, and the cosine). An equivalent (in terms of storage) sampling rate of 44 Hz is achieved without noticeable degradation of the signal quality for visual analysis. The method can be easily implemented for real time multichannel data compression/reconstruction.

Algorithms↗
PubMed1993

Compressing data volume of left ventricular cineangiograms.

In this paper a method of compressing data volume for left ventricular cineangiograms is proposed. This method enables digital-optical discs to store the cineangiograms with an equivalent recording density to that of HDTV VTR's while preserving the quality of the original image. The data volume of the cineangiograms is compressed by approximating function and storing its coefficients. With this method, each cineangiogram frame is first decomposed into three regions: the inner part, the inner wall, and the background (using a statistical method previously reported by the authors). Each region is then compressed by means of a difference operation and an adaptive approximation with smooth functions. Performance is tested on 20 cases using actual cineangiograms. The specifications were verified for a spatial resolution of 1000 TV lines, a dynamic range of 60 dB, an SNR of 40 dB [pp/rms], volume compression to 7% of the original volume, and 0.19 [second/frame] for decoding on a 1.7 MFLOPS computer.

Algorithms↗
PubMedDate not supplied

Diagnostic acceptability of FFT-based ECG data compression.

There are three signal domains in which ECG data compression can be performed, namely time domain, frequency domain and parameter extraction. The present paper deals with the frequency domain method of compression using fast Fourier transform. The algorithm has been tested on the third set of the CSE database library. A performance evaluation has been made using two important parameters, namely compression ratio and percent root-mean-square difference besides visual comparison. Further, in order to know the clinical acceptable quality of the reconstructed signal peak, boundary and interval measurements were made both on the reconstructed and the original signal of the same record for comparison. The visual examination reveals that most of the noise in the original signal had been filtered out during the compression. This amounts to reduction of electromyographic noise to a considerable extent. The experimental observations show that a compression ratio of 8 is feasible while ensuring clinical acceptability.

Algorithms↗
PubMed2004

Testing time symmetry in time series using data compression dictionaries.

Time symmetry, often called statistical time reversibility, in a dynamical process means that any segment of time-series output has the same probability of occurrence in the process as its time reversal. A technique, based on symbolic dynamics, is proposed to distinguish such symmetrical processes from asymmetrical ones, given a time-series observation of the otherwise unknown process. Because linear stochastic Gaussian processes, and static nonlinear transformations of them, are statistically reversible, but nonlinear dynamics such as dissipative chaos are usually statistically irreversible, a test will separate large classes of hypotheses for the data. A general-purpose and robust statistical test procedure requires adapting to arbitrary dynamics which may have significant time correlation of undetermined form. Given a symbolization of the observed time series, the technology behind adaptive dictionary data compression algorithms offers a suitable estimate of reversibility, as well as a statistical likelihood test. The data compression methods create approximately independent segments permitting a simple and direct null test without resampling or surrogate data. We demonstrate the results on various time-series-reversible and irreversible systems.

Journal Article↗
PubMed2006

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 4 < or = CR < or = 12 and 17.6% for 14 < or = CR < or = 20.

Algorithms↗
PubMed2006

A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces.

This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.

Action Potentials↗