Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets. / Akhbardeh, Alireza; Junnila, Sakari; Koivistoinen, Teemu; Värri, Alpo.

2006 IEEE International Conference on Systems, Man and Cybernetics. 2007. p. 878-883 4273947.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Harvard

Akhbardeh, A, Junnila, S, Koivistoinen, T & Värri, A 2007, Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets. in 2006 IEEE International Conference on Systems, Man and Cybernetics., 4273947, pp. 878-883, 2006 IEEE International Conference on Systems, Man and Cybernetics, Taipei, Taiwan, Province of China, 8/10/06. https://doi.org/10.1109/ICSMC.2006.384500

APA

Akhbardeh, A., Junnila, S., Koivistoinen, T., & Värri, A. (2007). Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets. In 2006 IEEE International Conference on Systems, Man and Cybernetics (pp. 878-883). [4273947] https://doi.org/10.1109/ICSMC.2006.384500

Vancouver

Akhbardeh A, Junnila S, Koivistoinen T, Värri A. Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets. In 2006 IEEE International Conference on Systems, Man and Cybernetics. 2007. p. 878-883. 4273947 https://doi.org/10.1109/ICSMC.2006.384500

Author

Akhbardeh, Alireza ; Junnila, Sakari ; Koivistoinen, Teemu ; Värri, Alpo. / Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets. 2006 IEEE International Conference on Systems, Man and Cybernetics. 2007. pp. 878-883

Bibtex - Download

@inproceedings{8562e4eb3e50470091fda4e3b1141cca,

title = "Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets",

abstract = "To design a heart diseases diagnosing system, we applied compactly supported Biorthogonal wavelet transform to extract essential features of the Ballistocardiogram (BCG) signal and to classify them using two novel supervised learning algorithms called SF-ART and QuickLearn. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that both SF-ART and Quicklearn algorithms can classify the subjects into three classes with high accuracies, high learning speeds, and very low computational loads compared to the well-known neural networks such as Multilayer Perceptrons. The proposed heart diseases diagnosing systems are almost insensitive to latency and nonlinear disturbance. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computational complexity and training time are reduced.",

author = "Alireza Akhbardeh and Sakari Junnila and Teemu Koivistoinen and Alpo V{\"a}rri",

year = "2007",

month = "8",

day = "28",

doi = "10.1109/ICSMC.2006.384500",

language = "English",

isbn = "1424401003",

pages = "878--883",

booktitle = "2006 IEEE International Conference on Systems, Man and Cybernetics",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Design an intelligent ballistocardiographic chair using novel QuickLearn and SF-ART algorithms and biorthogonal wavelets

AU - Akhbardeh, Alireza

AU - Junnila, Sakari

AU - Koivistoinen, Teemu

AU - Värri, Alpo

PY - 2007/8/28

Y1 - 2007/8/28

N2 - To design a heart diseases diagnosing system, we applied compactly supported Biorthogonal wavelet transform to extract essential features of the Ballistocardiogram (BCG) signal and to classify them using two novel supervised learning algorithms called SF-ART and QuickLearn. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that both SF-ART and Quicklearn algorithms can classify the subjects into three classes with high accuracies, high learning speeds, and very low computational loads compared to the well-known neural networks such as Multilayer Perceptrons. The proposed heart diseases diagnosing systems are almost insensitive to latency and nonlinear disturbance. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computational complexity and training time are reduced.

AB - To design a heart diseases diagnosing system, we applied compactly supported Biorthogonal wavelet transform to extract essential features of the Ballistocardiogram (BCG) signal and to classify them using two novel supervised learning algorithms called SF-ART and QuickLearn. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that both SF-ART and Quicklearn algorithms can classify the subjects into three classes with high accuracies, high learning speeds, and very low computational loads compared to the well-known neural networks such as Multilayer Perceptrons. The proposed heart diseases diagnosing systems are almost insensitive to latency and nonlinear disturbance. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computational complexity and training time are reduced.

U2 - 10.1109/ICSMC.2006.384500

DO - 10.1109/ICSMC.2006.384500

M3 - Conference contribution

SN - 1424401003

SN - 9781424401000

SP - 878

EP - 883

BT - 2006 IEEE International Conference on Systems, Man and Cybernetics

ER -

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