Electrocardiographic Data Compression Via Orthogonal Transforms

Ahmed, N.; Milne, Paul J.; Harris, Stanley G.

doi:10.1109/tbme.1975.324469

Cited by 126 publications

(28 citation statements)

References 4 publications

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“…We do this using Potts neurons, where the winner "shares" its step with its neighbors. The Potts neuron, , encodes the relative strengths according to (14) The distance (in the output space) between the output units defines the topology of the network. The artificial "temperature" or width is decreased (annealed) as learning proceeds.…”

Section: ) Topological Mapsmentioning

confidence: 99%

“…Electrocardiographic feature extraction by basis function representation was suggested already in the 1960's when Laguerre orthogonal functions were proposed [12]. Later, the Karhunen-Loeve (KL) expansion, which provides an optimal signal representation in the mean square error sense, was found to be suitable for this purpose, e.g., [13] and [14]. It is well-known that the KL basis functions constitute an orthonormal set and, therefore, each coefficient in the expansion represents independent information.…”

Section: Introductionmentioning

confidence: 99%

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Clustering ECG complexes using Hermite functions and self-organizing maps

Lagerholm

Peterson

Braccini

et al. 2000

IEEE Trans. Biomed. Eng.

472

267

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Abstract-An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.

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Section: ) Topological Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering ECG complexes using Hermite functions and self-organizing maps

Lagerholm

Peterson

Braccini

et al. 2000

IEEE Trans. Biomed. Eng.

472

267

View full text Add to dashboard Cite

show abstract

“…The DCT is a signal independent, real-valued, orthogonal transform that is asymptotically equivalent to the optimal principal component analysis (PCA) for highly correlated first-order stationary autoregressive signals [10]. The orthonormal basis vectors !…”

Section: A Dct -Based Feature Extraction Proceduresmentioning

confidence: 99%

Motor Imagery Task Classification Using a Signal-Dependent Orthogonal Transform Based Feature Extraction

Mesbah

Khorshidtalab

Baali³

et al. 2015

Neural Information Processing

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Abstract. In this paper, we present the results of classifying electroencephalographic (EEG) signals into four motor imagery tasks using a new method for feature extraction. This method is based on a signal-dependent orthogonal transform, referred to as LP-SVD, defined as the left singular vectors of the LPC filter impulse response matrix. Using a logistic tree based model classifier, the extracted features are mapped into one of four motor imagery movements, namely left hand, right hand, foot, and tongue. The proposed technique-based classification performance was benchmarked against those based on two widely used linear transform for feature extraction methods, namely discrete cosine transform (DCT) and adaptive autoregressive (AAR). By achieving an accuracy of 67.35%, the LP-SVD based method outperformed the other two by large margins (+25% compared to DCT and +6 % compared to AAR-based methods).

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“…Signal reconstruction is achieved by an inverse transformation process. This category includes traditional transform coding techniques applied to ECG signals such as the KarhunenLoève transform [2], Fourier transform [3], Cosine transform [4], subband-techniques [5], vector quantization (VQ) [6], and more recently the wavelet transform (WT) [7]. The wavelet decomposition splits the signal into approximation and detail coefficients, using finite impulse response digital filters.…”

Section: Open Accessmentioning

confidence: 99%

Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

Abo‐Zahhad¹,

Abdelhamid²,

Mohamed³

2014

IJCNS

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This paper presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code Modulation (DPCM), and run-length coding techniques for the compression of different parts of the signal; where lossless compression is adopted in clinically relevant parts and lossy compression is used in those parts that are not clinically relevant. The proposed compression algorithm begins by segmenting the ECG signal into its main components (P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The resulting waves are grouped into Region of Interest (RoI) and Non Region of Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes are applied to the RoI and NonRoI parts respectively. Ideally we would like to compress the signal losslessly, but in many applications this is not an option. Thus, given a fixed bit budget, it makes sense to spend more bits to represent those parts of the signal that belong to a specific RoI and, thus, reconstruct them with higher fidelity, while allowing other parts to suffer larger distortion. For this purpose, the correlation between the successive samples of the RoI part is utilized by adopting DPCM approach. However the NonRoI part is compressed using DWT, thresholding and coding techniques. The wavelet transformation is used for concentrating the signal energy into a small number of transform coefficients. Compression is then achieved by selecting a subset of the most relevant coefficients which afterwards are efficiently coded. Illustrative examples are given to demonstrate thresholding based on energy packing efficiency strategy, coding of DWT coefficients and data packetizing. The performance of the proposed algorithm is tested in terms of the compression ratio and the PRD distortion metrics for the compression of 10 seconds of data extracted from records 100 and 117 of MIT-BIH database. The obtained results revealed that the proposed technique possesses higher compression ratios and lower PRD compared to the other wavelet transformation techniques. The principal advantages of the proposed approach are: 1) the deployment of different compression schemes to compress different ECG parts to reduce the correlation between consecutive signal samples; and 2) getting high compression ratios with acceptable reconstruction signal quality compared to the recently published results.

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Electrocardiographic Data Compression Via Orthogonal Transforms

Cited by 126 publications

References 4 publications

Clustering ECG complexes using Hermite functions and self-organizing maps

Clustering ECG complexes using Hermite functions and self-organizing maps

Motor Imagery Task Classification Using a Signal-Dependent Orthogonal Transform Based Feature Extraction

Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

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