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A novel generic algorithm for robust physiological signal classification

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A novel generic algorithm for robust physiological signal classification. / Mahdiani, Shadi; Vanhala, J.; Viik, J.

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st–April 2nd 2016, Paphos, Cyprus. Springer Verlag, 2016. s. 1038-1043 (IFMBE Proceedings; Vuosikerta 57).

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Harvard

Mahdiani, S, Vanhala, J & Viik, J 2016, A novel generic algorithm for robust physiological signal classification. julkaisussa XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st–April 2nd 2016, Paphos, Cyprus. IFMBE Proceedings, Vuosikerta. 57, Springer Verlag, Sivut 1038-1043, 1/01/00. https://doi.org/10.1007/978-3-319-32703-7_205

APA

Mahdiani, S., Vanhala, J., & Viik, J. (2016). A novel generic algorithm for robust physiological signal classification. teoksessa XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st–April 2nd 2016, Paphos, Cyprus (Sivut 1038-1043). (IFMBE Proceedings; Vuosikerta 57). Springer Verlag. https://doi.org/10.1007/978-3-319-32703-7_205

Vancouver

Mahdiani S, Vanhala J, Viik J. A novel generic algorithm for robust physiological signal classification. julkaisussa XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st–April 2nd 2016, Paphos, Cyprus. Springer Verlag. 2016. s. 1038-1043. (IFMBE Proceedings). https://doi.org/10.1007/978-3-319-32703-7_205

Author

Mahdiani, Shadi ; Vanhala, J. ; Viik, J. / A novel generic algorithm for robust physiological signal classification. XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st–April 2nd 2016, Paphos, Cyprus. Springer Verlag, 2016. Sivut 1038-1043 (IFMBE Proceedings).

Bibtex - Lataa

@inproceedings{78dddd7aa2b546d3834dadde9012b4ea,
title = "A novel generic algorithm for robust physiological signal classification",
abstract = "The last decade has witnessed a significant interest in widespread usage of wearable monitoring devices that could provide continuous measurements of physiological parameters. The design and development of these devices has attracted lots of attention in industry and scientific associations. Advanced and miniaturized electronics with signal acquisition technologies provide a possibility for designing only one device for several physiological measurement purposes. Therefore for designing such an automatic system, a simple generic algorithm for physiological signal classification is required. In this paper, a novel generic algorithm for robust physiological signal classification is presented. The architecture of the proposed system includes preprocessing, feature extraction and a neural network method. Our generic algorithm was able to distinguish different physiological signals such as electrocardiogram (ECG), respiratory signal, seismocardiogram (SCG), electromyogram (EMG) and photoplethysmogram with 100{\%} accuracy. The algorithm was also evaluated by noisy signals with 10 and 20 dB levels of added noise and the same results were achieved. The algorithm could be implemented in healthcare monitoring systems and it can provide the possibility of monitoring various physiological signals with only one device.",
keywords = "Classifier, Generic algorithm, Neural network, Physiological signals, Wearable devices",
author = "Shadi Mahdiani and J. Vanhala and J. Viik",
note = "INT=elt,{"}Mahdiani, Shadi{"} JUFOID=58152",
year = "2016",
doi = "10.1007/978-3-319-32703-7_205",
language = "English",
isbn = "978-3-319-32701-3",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
pages = "1038--1043",
booktitle = "XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016",
address = "Germany",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - A novel generic algorithm for robust physiological signal classification

AU - Mahdiani, Shadi

AU - Vanhala, J.

AU - Viik, J.

N1 - INT=elt,"Mahdiani, Shadi" JUFOID=58152

PY - 2016

Y1 - 2016

N2 - The last decade has witnessed a significant interest in widespread usage of wearable monitoring devices that could provide continuous measurements of physiological parameters. The design and development of these devices has attracted lots of attention in industry and scientific associations. Advanced and miniaturized electronics with signal acquisition technologies provide a possibility for designing only one device for several physiological measurement purposes. Therefore for designing such an automatic system, a simple generic algorithm for physiological signal classification is required. In this paper, a novel generic algorithm for robust physiological signal classification is presented. The architecture of the proposed system includes preprocessing, feature extraction and a neural network method. Our generic algorithm was able to distinguish different physiological signals such as electrocardiogram (ECG), respiratory signal, seismocardiogram (SCG), electromyogram (EMG) and photoplethysmogram with 100% accuracy. The algorithm was also evaluated by noisy signals with 10 and 20 dB levels of added noise and the same results were achieved. The algorithm could be implemented in healthcare monitoring systems and it can provide the possibility of monitoring various physiological signals with only one device.

AB - The last decade has witnessed a significant interest in widespread usage of wearable monitoring devices that could provide continuous measurements of physiological parameters. The design and development of these devices has attracted lots of attention in industry and scientific associations. Advanced and miniaturized electronics with signal acquisition technologies provide a possibility for designing only one device for several physiological measurement purposes. Therefore for designing such an automatic system, a simple generic algorithm for physiological signal classification is required. In this paper, a novel generic algorithm for robust physiological signal classification is presented. The architecture of the proposed system includes preprocessing, feature extraction and a neural network method. Our generic algorithm was able to distinguish different physiological signals such as electrocardiogram (ECG), respiratory signal, seismocardiogram (SCG), electromyogram (EMG) and photoplethysmogram with 100% accuracy. The algorithm was also evaluated by noisy signals with 10 and 20 dB levels of added noise and the same results were achieved. The algorithm could be implemented in healthcare monitoring systems and it can provide the possibility of monitoring various physiological signals with only one device.

KW - Classifier

KW - Generic algorithm

KW - Neural network

KW - Physiological signals

KW - Wearable devices

U2 - 10.1007/978-3-319-32703-7_205

DO - 10.1007/978-3-319-32703-7_205

M3 - Conference contribution

SN - 978-3-319-32701-3

T3 - IFMBE Proceedings

SP - 1038

EP - 1043

BT - XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

PB - Springer Verlag

ER -