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B. R. Shankara Reddy

Bio: B. R. Shankara Reddy is an academic researcher from Drexel University. The author has contributed to research in topics: Fourier transform & Data compression. The author has an hindex of 1, co-authored 1 publications receiving 175 citations.

Papers
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Journal ArticleDOI
TL;DR: The method of Fourier descriptors (FD's) is presented for ECG data compression, resistant to noisy signals and is simple, requiring implementation of forward and inverse FFT.
Abstract: The method of Fourier descriptors (FD's) is presented for ECG data compression. The two-lead ECG data are segmented into QRS complexes and S-Q intervals, expressed as a complex sequence, and are Fourier transformed to obtain the FD's. A few lower order descriptors symmetrically situated with respect to the dc coefficient represent the data in the Fourier (compressed) domain. While compression ratios of 10:1 are feasible for the S-Q interval, the clinical information requirements limit this ratio to 3:1 for the QRS complex. With an overall compression ratio greater than 7, the quality of the reconstructed signal is well suited for morphological studies. The method is resistant to noisy signals and is simple, requiring implementation of forward and inverse FFT. The results of compression of ECG data obtained from more than 50 subjects with rhythm and morphological abnormalities are presented.

183 citations


Cited by
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Journal ArticleDOI
TL;DR: The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods and a framework for evaluation and comparison of ECG compression schemes is presented.
Abstract: Electrocardiogram (ECG) compression techniques are compared, and a unified view of these techniques is established. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are ECG differential pulse code modulation (DPCM) and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods include Fourier, Walsh, and Karhunen-Loeve transforms. The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods. A framework for evaluation and comparison of ECG compression schemes is presented. >

690 citations

Journal ArticleDOI
TL;DR: The extraction of fetal electrocardiogram (ECG) from the composite maternal ECG signal obtained from the abdominal lead is discussed, and the proposed method employs singular value decomposition (SVD) and analysis based on the singular value ratio (SVR) spectrum.
Abstract: The extraction of fetal electrocardiogram (ECG) from the composite maternal ECG signal obtained from the abdominal lead is discussed. The proposed method employs singular value decomposition (SVD) and analysis based on the singular value ratio (SVR) spectrum. The maternal ECG (M-ECG) and the fetal ECG (F-ECG) components are identified in terms of the SV-decomposed modes of the appropriately configured data matrices, and elimination of the M-ECG and determination of F-ECG are achieved through selective separation of the SV-decomposed components. The unique feature of the method is that only one composite maternal ECG signal is required to determine the P-ECG component. The method is numerically robust and computationally efficient.

304 citations

Journal ArticleDOI
TL;DR: A new algorithm for ECG signal compression is introduced that can be considered a generalization of the recently published average beat subtraction method, and was found superior at any bit rate.
Abstract: A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The periodicity of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent RMS difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction-STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method. >

262 citations

Journal ArticleDOI
01 Dec 2001
TL;DR: The results showed that truncated SVD method can provide an efficient coding with high-compression ratios and demonstrated the method as an effective technique for ECG data storage or signals transmission.
Abstract: 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.

194 citations

Journal ArticleDOI
TL;DR: Because the proposed real-time data compression and transmission algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.
Abstract: This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

173 citations