Data Compression of Long Time ECG Recording Using BP and PCA Neural Networks

Yasunori NAGASAKA; Akira IWATA

Data Compression of Long Time ECG Recording Using BP and PCA Neural Networks

Yasunori NAGASAKA, Akira IWATA

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The performances of BPNN (neural network trained by back propagation) and PCANN (neural network which computes principal component analysis) for ECG data compression have been investigated from several points of view. We have compared them with an existing data compression method TOMEK. We used MIT/BIH arrhythmia database as ECG data. Both BPNN and PCANN showed better results than TOMEK. They showed 1.1 to 1.4 times higher compression than TOMEK to achieve the same accuracy of reproduction (13.0% of PRD and 99.0% of CC). While PCANN showed better learning ability than BPNN in simple learning task, BPNN was a little better than PCANN regarding compression rates. Observing the reproduced waveforms, BPNN and PCANN had almost the same performance, and they were superior to TOMEK. The following characteristics were obtained from the experiments. Since PCANN is sensitive to the learning rate, we had to precisely control the learning rate while the learning is in progress. We also found the tendency that PCANN needs larger amount of iteration in learning than BPNN for getting the same performance. PCANN showed better learning ability than BPNN, however, the total learning cost were almost the same between BPNN and PCANN due to the large amount of iteration. We analyzed the connection weight patterns. Since PCANN has a clear mathematical background, its behavior can be explained theoretically. BPNN sometimes generated the connection weights which were similar to the principal components. We supposed that BPNN may occasionally generate those patterns, and performs well while doing that. Finally we concluded as follows. Although the difference of the performances is smal, it was always observed and PCANN never exceeded BPNN. When the ease of analysis or the relation to mathematics is important, PCANN is suitable. It will be useful for the study of the recorded data such as statistics.

Publication: IEICE TRANSACTIONS on Information Vol.E76-D No.12 pp.1434-1442

Publication Date: 1993/12/25

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Type of Manuscript: Special Section PAPER (Special Section on ECG Data Compression)

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Yasunori NAGASAKA, Akira IWATA, "Data Compression of Long Time ECG Recording Using BP and PCA Neural Networks" in IEICE TRANSACTIONS on Information, vol. E76-D, no. 12, pp. 1434-1442, December 1993, doi: .
Abstract: The performances of BPNN (neural network trained by back propagation) and PCANN (neural network which computes principal component analysis) for ECG data compression have been investigated from several points of view. We have compared them with an existing data compression method TOMEK. We used MIT/BIH arrhythmia database as ECG data. Both BPNN and PCANN showed better results than TOMEK. They showed 1.1 to 1.4 times higher compression than TOMEK to achieve the same accuracy of reproduction (13.0% of PRD and 99.0% of CC). While PCANN showed better learning ability than BPNN in simple learning task, BPNN was a little better than PCANN regarding compression rates. Observing the reproduced waveforms, BPNN and PCANN had almost the same performance, and they were superior to TOMEK. The following characteristics were obtained from the experiments. Since PCANN is sensitive to the learning rate, we had to precisely control the learning rate while the learning is in progress. We also found the tendency that PCANN needs larger amount of iteration in learning than BPNN for getting the same performance. PCANN showed better learning ability than BPNN, however, the total learning cost were almost the same between BPNN and PCANN due to the large amount of iteration. We analyzed the connection weight patterns. Since PCANN has a clear mathematical background, its behavior can be explained theoretically. BPNN sometimes generated the connection weights which were similar to the principal components. We supposed that BPNN may occasionally generate those patterns, and performs well while doing that. Finally we concluded as follows. Although the difference of the performances is smal, it was always observed and PCANN never exceeded BPNN. When the ease of analysis or the relation to mathematics is important, PCANN is suitable. It will be useful for the study of the recorded data such as statistics.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e76-d_12_1434/_p

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@ARTICLE{e76-d_12_1434,
author={Yasunori NAGASAKA, Akira IWATA, },
journal={IEICE TRANSACTIONS on Information},
title={Data Compression of Long Time ECG Recording Using BP and PCA Neural Networks},
year={1993},
volume={E76-D},
number={12},
pages={1434-1442},
abstract={The performances of BPNN (neural network trained by back propagation) and PCANN (neural network which computes principal component analysis) for ECG data compression have been investigated from several points of view. We have compared them with an existing data compression method TOMEK. We used MIT/BIH arrhythmia database as ECG data. Both BPNN and PCANN showed better results than TOMEK. They showed 1.1 to 1.4 times higher compression than TOMEK to achieve the same accuracy of reproduction (13.0% of PRD and 99.0% of CC). While PCANN showed better learning ability than BPNN in simple learning task, BPNN was a little better than PCANN regarding compression rates. Observing the reproduced waveforms, BPNN and PCANN had almost the same performance, and they were superior to TOMEK. The following characteristics were obtained from the experiments. Since PCANN is sensitive to the learning rate, we had to precisely control the learning rate while the learning is in progress. We also found the tendency that PCANN needs larger amount of iteration in learning than BPNN for getting the same performance. PCANN showed better learning ability than BPNN, however, the total learning cost were almost the same between BPNN and PCANN due to the large amount of iteration. We analyzed the connection weight patterns. Since PCANN has a clear mathematical background, its behavior can be explained theoretically. BPNN sometimes generated the connection weights which were similar to the principal components. We supposed that BPNN may occasionally generate those patterns, and performs well while doing that. Finally we concluded as follows. Although the difference of the performances is smal, it was always observed and PCANN never exceeded BPNN. When the ease of analysis or the relation to mathematics is important, PCANN is suitable. It will be useful for the study of the recorded data such as statistics.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - Data Compression of Long Time ECG Recording Using BP and PCA Neural Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1434
EP - 1442
AU - Yasunori NAGASAKA
AU - Akira IWATA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 1993
AB - The performances of BPNN (neural network trained by back propagation) and PCANN (neural network which computes principal component analysis) for ECG data compression have been investigated from several points of view. We have compared them with an existing data compression method TOMEK. We used MIT/BIH arrhythmia database as ECG data. Both BPNN and PCANN showed better results than TOMEK. They showed 1.1 to 1.4 times higher compression than TOMEK to achieve the same accuracy of reproduction (13.0% of PRD and 99.0% of CC). While PCANN showed better learning ability than BPNN in simple learning task, BPNN was a little better than PCANN regarding compression rates. Observing the reproduced waveforms, BPNN and PCANN had almost the same performance, and they were superior to TOMEK. The following characteristics were obtained from the experiments. Since PCANN is sensitive to the learning rate, we had to precisely control the learning rate while the learning is in progress. We also found the tendency that PCANN needs larger amount of iteration in learning than BPNN for getting the same performance. PCANN showed better learning ability than BPNN, however, the total learning cost were almost the same between BPNN and PCANN due to the large amount of iteration. We analyzed the connection weight patterns. Since PCANN has a clear mathematical background, its behavior can be explained theoretically. BPNN sometimes generated the connection weights which were similar to the principal components. We supposed that BPNN may occasionally generate those patterns, and performs well while doing that. Finally we concluded as follows. Although the difference of the performances is smal, it was always observed and PCANN never exceeded BPNN. When the ease of analysis or the relation to mathematics is important, PCANN is suitable. It will be useful for the study of the recorded data such as statistics.
ER -