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Języki publikacji
Abstrakty
Diagnostics of cardiac arrhythmias and frequent interventions may contribute to early detection of diseases or even prevent sudden death. Generally electrocardiograph with several on body electrodes at outpatient clinic is applied and the procedure requires a medical expert. We propose cardiac arrhythmia estimation on the basis of heartbeat detection with optical fibers integrated in the bedding. The modified Michelson's interferometer with error detection was used to measure and maximum a-posteriori probability was used to estimate the beat-to-beat intervals. The consistency of heartbeat intervals was examined with simultaneous measurement with clinical electrocardiograph in 10 healthy volunteers and 10 patients with diagnosed heart arrhythmias. Heart beat interval data obtained in patients were examined and irregularities/arrhythmias were identified from the medical guidelines. The current system enables assessment also in home environment without any on-body sensor placement or required assistance. Thus early intervention is possible as the irregularities are submitted to the nurse on duty and stored in the database for subsequent more detailed analysis.
Wydawca
Czasopismo
Rocznik
Tom
Strony
267--275
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
autor
- University Rehabilitation Institute, Republic of Slovenia, Linhartova 51, Ljubljana, Slovenia
autor
- University of Ljubljana, Faculty of Computer Sciences, Ljubljana, Slovenia
autor
- University Rehabilitation Institute, Republic of Slovenia, Ljubljana, Slovenia
autor
- University of Maribor, Faculty of Electrical Engineering and Computer Sciences, Maribor, Slovenia
Bibliografia
- [1] Watanabe K, Watanabe T, Watanabe H, Ando H, Ishikawa T, Kobayashi K. Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method. IEEE Trans Bio-Med Eng 2005;52(12):2100–7. http://dx.doi.org/10.1109/TBME.2005.857637.
- [2] Sutton CM. Non-linearity in length measurement using heterodyne laser Michelson interferometry. J Phys E: Sci Instrum 1987;20(10):1290–2. http://dx.doi.org/10.1088/0022-3735/20/10/034.
- [3] Dziuda L, Skibniewski FW. A new approach to ballistocardiographic measurements using fiber Bragg grating-based sensors. Biocybern Biomed Eng 2014;34 (2):101–16.
- [4] Shin JH, Hwang SH, Chang MH, Park KS. Heart rate variability analysis using a ballistocardiogram during Valsalva manoeuvre and post exercise. Physiol Meas 2011;32(8):1239–64.
- [5] Mikhelson IV, Bakhtiari S, Elmer TW, Sahakian AV. Remote sensing of heart rate and patterns of respiration on a stationary subject using 94-GHz millimeter-wave interferometry. IEEE Trans Bio-Med Eng 2011;58(6):1671–7. http://dx.doi.org/10.1109/TBME.2011.2111371.
- [6] Quiceno-Manrique AF, Godino-Llorente JI, Blanco-Velasco M, Castellanos-Dominguez G. Selection of dynamic features based on time-frequency representations for heart murmur detection from phonocardiographic signals. Ann Biomed Eng 2010;38(1):118–37. http://dx.doi.org/10.1007/s10439-009-9838-3.
- [7] Rajala S, Lekkala J. Film-type sensor materials PVDF and EMFi in measurement of cardiorespiratory signals – a review. IEEE Sens J 2012;12(3):439–46. http://dx.doi.org/10.1109/JSEN.2010.2089510.
- [8] Šprager S, Zazula D. Detection of heartbeat and respiration from optical interferometric signal by using wavelet transform. Comput Methods Programs Biomed 2013;111 (1):41–51. http://dx.doi.org/10.1016/j.cmpb.2013.03.003.
- [9] Šprager S, Zazula D. Heartbeat and respiration detection from optical interferometric signals by using a multimethod approach. IEEE Trans Bio-Med Eng 2012;59 (10):2922–9. http://dx.doi.org/10.1109/TBME.2012.2213302.
- [10] Flandrin P, Escudié B. An interpretation of the Pseudo-Wigner–Ville distribution. Signal Process 1984; 6(1):27–36. http://dx.doi.org/10.1016/0165-1684(84)90048-3.
- [11] Hariharan P. Basics of interferometry. 2nd ed. Elsevier; 2007.
- [12] Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Bio-Med Eng 1985;32(3):230–6. http://dx.doi.org/10.1109/TBME.1985.325532.
- [13] Brüser C, Winter S, Leonhardt S. Robust inter-beat interval estimation in cardiac vibration signals. Physiol Meas 2013;34(2):123–38. http://dx.doi.org/10.1088/0967-3334/34/2/123.
- [14] Migliorini M, Cabiddu R, Cerutti S, Mainardi L, Kortelainen J, Bianchi A. Automatic arrhythmia detection based on heart beat interval series recorded through bed sensors during sleep; 2011.
- [15] Katz AM. Physiology of the heart, vol. 2010. 2010.
- [16] Pirš C, Cigale B, Zazula D, Usar D. System architecture of unobtrusive sensors for supporting home care and independent living. In: Long CA, Mastorakis NE, Mladenov V, editors. Proceedings of the SCSI 2013. Recent advances in electrical engineering series, vol. 16. European Society for Environmental Research and Sustainable Development; 2013. p. 434–9.
- [17] Achten J, Jeukendrup AE. Heart rate monitoring: applications and limitations. Sports Med (Auckl NZ) 2003;33 (7):517–38.
- [18] Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, et al. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J 1996;17(3):354–81.
- [19] Friedman HS. Heart rate variability in atrial fibrillation related to left atrial size.. Am J Cardiol 2004;93(6):705–9. http://dx.doi.org/10.1016/j.amjcard.2003.11.052.
- [20] Fauchier L, Babuty D, Cosnay P, Fauchier JP. Prognostic value of heart rate variability for sudden death and major arrhythmic events in patients with idiopathic dilated cardiomyopathy. J Am Coll Cardiol 1999;33(5):1203–7. 10.1016/S0735-1097(99)00021-2.
- [21] McCraty R, Atkinson M, Tomasino D, Stuppy WP. Analysis of twenty-four hour heart rate variability in patients with panic disorder. Biol Psychol 2001;56:131–50.
- [22] Šprager S, Zazula D. Optimization of heartbeat detection in fiber-optic unobtrusive measurements by using maximum a posteriori probability estimation. IEEE J Biomed Health Inform 2014;18(4):1161–8. http://dx.doi.org/10.1109/JBHI.2013.2282403.
- [23] Kortelainen JM, Mendez MO, Bianchi AM, Matteucci M, Cerutti S. Sleep staging based on signals acquired through bed sensor. IEEE Trans Inf Technol Biomed: Publ IEEE Eng Med Biol Soc 2010;14(3):776–85. http://dx.doi.org/10.1109/TITB.2010.2044797.
- [24] Patil DD, Patil D, Pandharpatte S, Dhekane R, Mohol T, Wadhai DV. Intelligent arrhythmia diagnostics system. IJCSI Int J Comput Sci Issues 2012;9(1):408–13.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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