Making Smart Shirts Smarter: Optimal Electrode Placement for Cardiac Assessment

Research output: Contribution to journalArticle

Abstract

The use of smart textiles within clothing offers the facility to monitor patient vital signs in an unobtrusive manner. In the present study we examine the benefits of integrating electrodes into smart shirts taking into consideration aspects of practical limitations in sensor placement. Three practical scenarios are investigated which restrict possible recording sites to the anterior, lateral, and posterior regions, respectively. A wrapper approach incorporating both nearest neighbor and logistic regression models was adopted to search for and extract relevant features. Two discrimination tasks were investigated; identifying between subjects with evidence of old myocardial infarction, and normal healthy subjects; and identifying between subject suffering from left ventricular hypertrophy and healthy subjects. The results from the study indicate that acceptable classification performance is possible even if recording sites are restricted due to practical constraints.
LanguageEnglish
Pages44-52
JournalInternational Journal of Assistive Robotics and Mechatronics
Volume8
Issue number2
Publication statusPublished - 1 Jun 2007

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Logistics
Electrodes
Sensors
Smart textiles

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@article{9479d7718698494a9c6fc506d71b8340,
title = "Making Smart Shirts Smarter: Optimal Electrode Placement for Cardiac Assessment",
abstract = "The use of smart textiles within clothing offers the facility to monitor patient vital signs in an unobtrusive manner. In the present study we examine the benefits of integrating electrodes into smart shirts taking into consideration aspects of practical limitations in sensor placement. Three practical scenarios are investigated which restrict possible recording sites to the anterior, lateral, and posterior regions, respectively. A wrapper approach incorporating both nearest neighbor and logistic regression models was adopted to search for and extract relevant features. Two discrimination tasks were investigated; identifying between subjects with evidence of old myocardial infarction, and normal healthy subjects; and identifying between subject suffering from left ventricular hypertrophy and healthy subjects. The results from the study indicate that acceptable classification performance is possible even if recording sites are restricted due to practical constraints.",
author = "Mark Donnelly and Christopher Nugent and Dewar Finlay and Paul McCullagh and Norman Black",
note = "Reference text: [1] World Health Organisation, {"}The atlas of heart disease and stroke,{"} April 2005 http://www.who/int/cardiovascular_disease/resources/atlas/en/ [2] S. J. Maynard, I. B. A. Menown, G. Manoharan, J. Allen, J. Anderson, and A. A. J. Adgey, {"}Body surface mapping improves early diagnosis of acute myocardial infarction in patients with chest pain and left bundle branch block,{"} Heart, vol. 89, pp. 998-1002, 2003 [3] H. L. Kennedy, P. J. Podrid, {"}Role of Holter monitoring and exercise testing for arrhythmia assessment and management. In: P. J. Podrid P. R. Kowey, eds. Cardiac Arrhythmia. Philadelphia, Pa: Lippincott Williams & Wilkins; pp. 165–193, 2001 [4] C.D. Nugent, P.J. McCullagh, E.T. McAdams and A. Lymberis, Personalised Health Management Systems: The Integration of Innovative Sensing, Textile, Information and Communication Technologies, IOS Press, Amsterdam, 2006 [5] R. Hoekema, G.J.H. Uijen, and A. van Oosterom, “On selecting a body surface mapping procedure,” Journal of Electrocardiology, vol. 32, no. 2, pp. 93-101, 1999 [6] R. L. Lux, C. R. Smith, R. F. Wyatt, and J. A. Abildskov, “Limited lead selection for the estimation of body surface potential maps in electrocardiography,” IEEE Transactions on Biomedical Engineering, vol. 25, no. 3, pp. 270-276, 1978 [7] R. C. Barr, M. S. Spach, and S. Herman-Giddens, “Selection of the number and position of measuring locations for electrocardiography,” IEEE Transactions on Biomedical Engineering, vol. 18, pp. 125-138, 1971 [8] D. D. Finlay, C. D. Nugent, M. P. Donnelly, R. L. Lux, P. J. McCullagh, and N. D. Black, “Selection of optimal recording sites for limited lead body surface potential mapping: A sequential selection approach,” BMC Medical Informatics in Decision Making, vol. 6, no. 9, pp. 1-9, 2006 [9] Finlay D, Nugent C D, Donnelly M, McCullagh PJ, Black ND, (Jun 2006) {"}Selecting optimal recording sites in electrocardiography to enhance home based recovery monitoring with smart clothes{"}, Proc. Of 4th International Conference on Smart homes and health Telematics, Belfast, 26th-28th June, IOS Press (Amsterdam), ISBN 1-58603-623-8, Pages 247-254 [10] F. Kornreich, P. M. Rautaharju, J. Warren, T. J. Montague, and B. M. Horacek, “Identification of best electrocardiographic leads for diagnosing myocardial infarction by statistical analysis of body surface potential maps,” American Journal of Cardiology, vol. 56, pp. 852-856, 1985 [11] F. Kornreich, T. J. Montague, P. M. Rautaharju, P. Block, J. W. Warren, and M. B. Horacek, “Identification of best electrocardiographic leads for diagnosing anterior and inferior myocardial infarction by statistical analysis of body surface potential maps,” American Journal of Cardiology, vol. 58, no. 10, pp. 863-871, 1986 [12] F. Kornreich, T. J. Montague, P. M. Rautaharju, M. Kavadias, and M. B. Horacek, “Identification of best electrocardiographic leads for diagnosing left ventricular hypertrophy by statistical analysis of body surface potential maps,” American Journal of Cardiology, vol. 62, no. 17, pp 1285-1291, 1988 [13] F. Kornreich, “Identification of best electrocardiographic leads for diagnosing acute myocardial ischemia,” Journal of Electrocardiology, vol. 31, suppl., pp: 157-163, 1998 [14] D. D. Finlay, C. D. Nugent, P. J. McCullagh, and N. D. Black, “Mining for diagnostic information in body surface potential maps: A comparison of feature selection techniques,” Journal of BioMedical Engineering Online, vol. 4, no. 51, pp. 1-14, 2005 [15] R. Kohavi, and G. John, “Wrappers for feature subset selection,” Artificial Intelligence Journal, vol. 97, no.1, pp. 273-324, 1996 [16] M. P. Donnelly, C. D. Nugent, D. D. Finlay, and N. D. Black, {"}Optimal electrode placements for the identification of old MI and LVH{"}, Proc. Of the 33rd International Conference of IEEE Computers in Cardiology, Valencia, 17th-20th September, 2006 [17] T. J. Montague, E. R. Smith, D. A. Cameron, P. M. Rautaharju, G. A. Klassen, C. S. Felmington, B. M. Horacek. {"}Isointegral analysis of body surface maps: surface distribution and temporal variability in normal subjects,{"} Circulation, vol. 63, no. 11, pp. 1166-1171, 1981",
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}

Making Smart Shirts Smarter: Optimal Electrode Placement for Cardiac Assessment. / Donnelly, Mark; Nugent, Christopher; Finlay, Dewar; McCullagh, Paul; Black, Norman.

Vol. 8, No. 2, 01.06.2007, p. 44-52.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Making Smart Shirts Smarter: Optimal Electrode Placement for Cardiac Assessment

AU - Donnelly, Mark

AU - Nugent, Christopher

AU - Finlay, Dewar

AU - McCullagh, Paul

AU - Black, Norman

N1 - Reference text: [1] World Health Organisation, "The atlas of heart disease and stroke," April 2005 http://www.who/int/cardiovascular_disease/resources/atlas/en/ [2] S. J. Maynard, I. B. A. Menown, G. Manoharan, J. Allen, J. Anderson, and A. A. J. Adgey, "Body surface mapping improves early diagnosis of acute myocardial infarction in patients with chest pain and left bundle branch block," Heart, vol. 89, pp. 998-1002, 2003 [3] H. L. Kennedy, P. J. Podrid, "Role of Holter monitoring and exercise testing for arrhythmia assessment and management. In: P. J. Podrid P. R. Kowey, eds. Cardiac Arrhythmia. Philadelphia, Pa: Lippincott Williams & Wilkins; pp. 165–193, 2001 [4] C.D. Nugent, P.J. McCullagh, E.T. McAdams and A. Lymberis, Personalised Health Management Systems: The Integration of Innovative Sensing, Textile, Information and Communication Technologies, IOS Press, Amsterdam, 2006 [5] R. Hoekema, G.J.H. Uijen, and A. van Oosterom, “On selecting a body surface mapping procedure,” Journal of Electrocardiology, vol. 32, no. 2, pp. 93-101, 1999 [6] R. L. Lux, C. R. Smith, R. F. Wyatt, and J. A. Abildskov, “Limited lead selection for the estimation of body surface potential maps in electrocardiography,” IEEE Transactions on Biomedical Engineering, vol. 25, no. 3, pp. 270-276, 1978 [7] R. C. Barr, M. S. Spach, and S. Herman-Giddens, “Selection of the number and position of measuring locations for electrocardiography,” IEEE Transactions on Biomedical Engineering, vol. 18, pp. 125-138, 1971 [8] D. D. Finlay, C. D. Nugent, M. P. Donnelly, R. L. Lux, P. J. McCullagh, and N. D. Black, “Selection of optimal recording sites for limited lead body surface potential mapping: A sequential selection approach,” BMC Medical Informatics in Decision Making, vol. 6, no. 9, pp. 1-9, 2006 [9] Finlay D, Nugent C D, Donnelly M, McCullagh PJ, Black ND, (Jun 2006) "Selecting optimal recording sites in electrocardiography to enhance home based recovery monitoring with smart clothes", Proc. Of 4th International Conference on Smart homes and health Telematics, Belfast, 26th-28th June, IOS Press (Amsterdam), ISBN 1-58603-623-8, Pages 247-254 [10] F. Kornreich, P. M. Rautaharju, J. Warren, T. J. Montague, and B. M. Horacek, “Identification of best electrocardiographic leads for diagnosing myocardial infarction by statistical analysis of body surface potential maps,” American Journal of Cardiology, vol. 56, pp. 852-856, 1985 [11] F. Kornreich, T. J. Montague, P. M. Rautaharju, P. Block, J. W. Warren, and M. B. Horacek, “Identification of best electrocardiographic leads for diagnosing anterior and inferior myocardial infarction by statistical analysis of body surface potential maps,” American Journal of Cardiology, vol. 58, no. 10, pp. 863-871, 1986 [12] F. Kornreich, T. J. Montague, P. M. Rautaharju, M. Kavadias, and M. B. Horacek, “Identification of best electrocardiographic leads for diagnosing left ventricular hypertrophy by statistical analysis of body surface potential maps,” American Journal of Cardiology, vol. 62, no. 17, pp 1285-1291, 1988 [13] F. Kornreich, “Identification of best electrocardiographic leads for diagnosing acute myocardial ischemia,” Journal of Electrocardiology, vol. 31, suppl., pp: 157-163, 1998 [14] D. D. Finlay, C. D. Nugent, P. J. McCullagh, and N. D. Black, “Mining for diagnostic information in body surface potential maps: A comparison of feature selection techniques,” Journal of BioMedical Engineering Online, vol. 4, no. 51, pp. 1-14, 2005 [15] R. Kohavi, and G. John, “Wrappers for feature subset selection,” Artificial Intelligence Journal, vol. 97, no.1, pp. 273-324, 1996 [16] M. P. Donnelly, C. D. Nugent, D. D. Finlay, and N. D. Black, "Optimal electrode placements for the identification of old MI and LVH", Proc. Of the 33rd International Conference of IEEE Computers in Cardiology, Valencia, 17th-20th September, 2006 [17] T. J. Montague, E. R. Smith, D. A. Cameron, P. M. Rautaharju, G. A. Klassen, C. S. Felmington, B. M. Horacek. "Isointegral analysis of body surface maps: surface distribution and temporal variability in normal subjects," Circulation, vol. 63, no. 11, pp. 1166-1171, 1981

PY - 2007/6/1

Y1 - 2007/6/1

N2 - The use of smart textiles within clothing offers the facility to monitor patient vital signs in an unobtrusive manner. In the present study we examine the benefits of integrating electrodes into smart shirts taking into consideration aspects of practical limitations in sensor placement. Three practical scenarios are investigated which restrict possible recording sites to the anterior, lateral, and posterior regions, respectively. A wrapper approach incorporating both nearest neighbor and logistic regression models was adopted to search for and extract relevant features. Two discrimination tasks were investigated; identifying between subjects with evidence of old myocardial infarction, and normal healthy subjects; and identifying between subject suffering from left ventricular hypertrophy and healthy subjects. The results from the study indicate that acceptable classification performance is possible even if recording sites are restricted due to practical constraints.

AB - The use of smart textiles within clothing offers the facility to monitor patient vital signs in an unobtrusive manner. In the present study we examine the benefits of integrating electrodes into smart shirts taking into consideration aspects of practical limitations in sensor placement. Three practical scenarios are investigated which restrict possible recording sites to the anterior, lateral, and posterior regions, respectively. A wrapper approach incorporating both nearest neighbor and logistic regression models was adopted to search for and extract relevant features. Two discrimination tasks were investigated; identifying between subjects with evidence of old myocardial infarction, and normal healthy subjects; and identifying between subject suffering from left ventricular hypertrophy and healthy subjects. The results from the study indicate that acceptable classification performance is possible even if recording sites are restricted due to practical constraints.

M3 - Article

VL - 8

SP - 44

EP - 52

IS - 2

ER -