Classification of physiological sensor signals using artificial neural networks

Nandita Sharma; Tom Gedeon

doi:10.1007/978-3-642-42042-9_63

Classification of physiological sensor signals using artificial neural networks

Nandita Sharma, Tom Gedeon

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

1 Citation (Scopus)

Abstract

Physiological signals have certain prominent characteristics that distinguish them from other types of physiological signals which are familiar to experts and assessed by inspection. The aim of this paper is to develop a computational model that can distinguish electrocardiogram, galvanic skin response and blood pressure signals acquired from sensors as well as detect corrupted signals which can arise due to hardware problems including sensor malfunction. Our work also investigates the impact of the signal modeling for various time lengths and determines an optimal signal time length for classification. This provides a method for automatic detection of corrupted signals during signal data collection which can be incorporated as a support tool during real-time sensor data acquisition.

Original language	English
Title of host publication	Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Pages	504-511
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-42042-9_63
Publication status	Published - 2013
Event	20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of Duration: 3 Nov 2013 → 7 Nov 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8227 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Neural Information Processing, ICONIP 2013
Country/Territory	Korea, Republic of
City	Daegu
Period	3/11/13 → 7/11/13

Access to Document

10.1007/978-3-642-42042-9_63

Cite this

Sharma, N., & Gedeon, T. (2013). Classification of physiological sensor signals using artificial neural networks. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings (PART 2 ed., pp. 504-511). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8227 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-42042-9_63

Sharma, Nandita ; Gedeon, Tom. / Classification of physiological sensor signals using artificial neural networks. Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 2. ed. 2013. pp. 504-511 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Classification of physiological sensor signals using artificial neural networks",

abstract = "Physiological signals have certain prominent characteristics that distinguish them from other types of physiological signals which are familiar to experts and assessed by inspection. The aim of this paper is to develop a computational model that can distinguish electrocardiogram, galvanic skin response and blood pressure signals acquired from sensors as well as detect corrupted signals which can arise due to hardware problems including sensor malfunction. Our work also investigates the impact of the signal modeling for various time lengths and determines an optimal signal time length for classification. This provides a method for automatic detection of corrupted signals during signal data collection which can be incorporated as a support tool during real-time sensor data acquisition.",

keywords = "Artificial neural networks, Physiological signals, Signal classification, Signal modeling, Time series data",

author = "Nandita Sharma and Tom Gedeon",

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Sharma, N & Gedeon, T 2013, Classification of physiological sensor signals using artificial neural networks. in Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8227 LNCS, pp. 504-511, 20th International Conference on Neural Information Processing, ICONIP 2013, Daegu, Korea, Republic of, 3/11/13. https://doi.org/10.1007/978-3-642-42042-9_63

Classification of physiological sensor signals using artificial neural networks. / Sharma, Nandita; Gedeon, Tom.
Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 2. ed. 2013. p. 504-511 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8227 LNCS, No. PART 2).

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

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AU - Gedeon, Tom

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N2 - Physiological signals have certain prominent characteristics that distinguish them from other types of physiological signals which are familiar to experts and assessed by inspection. The aim of this paper is to develop a computational model that can distinguish electrocardiogram, galvanic skin response and blood pressure signals acquired from sensors as well as detect corrupted signals which can arise due to hardware problems including sensor malfunction. Our work also investigates the impact of the signal modeling for various time lengths and determines an optimal signal time length for classification. This provides a method for automatic detection of corrupted signals during signal data collection which can be incorporated as a support tool during real-time sensor data acquisition.

AB - Physiological signals have certain prominent characteristics that distinguish them from other types of physiological signals which are familiar to experts and assessed by inspection. The aim of this paper is to develop a computational model that can distinguish electrocardiogram, galvanic skin response and blood pressure signals acquired from sensors as well as detect corrupted signals which can arise due to hardware problems including sensor malfunction. Our work also investigates the impact of the signal modeling for various time lengths and determines an optimal signal time length for classification. This provides a method for automatic detection of corrupted signals during signal data collection which can be incorporated as a support tool during real-time sensor data acquisition.

KW - Artificial neural networks

KW - Physiological signals

KW - Signal classification

KW - Signal modeling

KW - Time series data

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BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings

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Sharma N, Gedeon T. Classification of physiological sensor signals using artificial neural networks. In Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 2 ed. 2013. p. 504-511. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-42042-9_63

Classification of physiological sensor signals using artificial neural networks

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