Advanced SearchSearch Tips
Quality Level Classification of ECG Measured using Non-Constraint Approach
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Quality Level Classification of ECG Measured using Non-Constraint Approach
Kim, Y.J.; Heo, J.; Park, K.S.; Kim, S.;
  PDF(new window)
Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.
Non-constraint;Capacitively coupled electrode;Health parameter;Quality level;Posture estimation;
 Cited by
K. Krauchi and A. Wirz-Justice, "Circadian clues to sleep onset mechanisms", Neuropsychopharmacology, vol. 25, no. S5, pp. S92-S96, 2001. crossref(new window)

S.Y. Sim, K.M. Joo, H.B. Kim, S.J. Jang, B.O. Kim, S.B. Hong, S. Kim, and K.S. Park, "Estimation of circadian body temperature rhythm based on heart rate in healthy, ambulatory subjects, IEEE J. Biomed. Health Inform.", In press.

Y.J. Kim, H. Jeong, K.S. Park, and S. Kim, "Proposition of novel classification approach and features for improved real-time arrhythmia monitoring", Comput. Biol. Med., vol. 75, pp. 190-202, 2016. crossref(new window)

M.G. Tsipouras and D.I. Fotiadis, "Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability", Comput. Methods Prog. Biomed., vol. 74, no. 2, pp. 95-108, 2004. crossref(new window)

L.A. Geddes, M.H. Voelz, C.F. Babbs, J.D. Bourland, and W.A. Tracker, "Pulse Transit Time as an Indicator of Arterial Blood Pressure", Psychophysiology, vol. 18, no. 1, pp. 71-74, 1981. crossref(new window)

R. McCraty, M. Atkinson, W.A. Tiller, G. Rein, and A.D. Watkins, "The effects of emotions on short-term power spectrum analysis of heart rate variability", Am. J. Cardiol., vol. 76, no. 14, pp. 1089-1093, 1995. crossref(new window)

M.T. La Rovere, J.T. Bigger Jr, F.I. Marcus, A. Mortara, and P.J. Schwartz, "Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction", The Lancet, vol. 351, no. 9101, pp. 478-484, 1998. crossref(new window)

W.K. Lee, H.J. Lee, H.N. Yoon, G.S. Chung, and K.S. Park, "Automatic Noise Removal and Peak Detection Algorithm for ECG measurement from Capacitively Coupled Electrodes Included within a Cloth Mattress Pad", J. Biomed. Eng. Res., vol. 35, no. 4, pp. 87-94, 2014. crossref(new window)

J.S. Lee, W.K. Lee, Y.G. Lim, and K.S. Park, "Adhesive Polyurethane-based Capacitive Electrode for Patch-type Wearable Electrocardiogram Measurement System", J. Biomed. Eng. Res., vol. 35, no. 6, pp. 203-210, 2014. crossref(new window)

J.S. Paul, M.R. Reddy, and V.J. Kumar, "A Transform Domain SVD Filter for Suppression of Muscle Noise Arte-facts in Exercise ECG's", IEEE Trans. Biomed. Eng., vol. 47, no. 5, pp. 654-663, 2000. crossref(new window)

J.L. Talmon, J.A. Kors, and J.H.V. Bemmel, "Adaptive Gaussian Filtering in Routine ECG/VCG Analysis", IEEE Trans. Sig. Process., vol. ASSP-34, no. 3, pp. 527-534, 1986.

N.V. Thakor, and V.S. Zhu, "Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection", IEEE Trans. Biomed. Eng., vol. 38, no. 8, pp. 785-794, 1991. crossref(new window)

C. Vaz, X. Kong, and N. Thakor, "An Adaptive Estimation of Periodic Signals Using a Fourier Linear Combiner", IEEE Trans. Biomed. Eng., vol. 42, no.1, pp. 1-10, 1994.

J.O. Wisbeck, and R.G. Ojeda, "Application of Neural Networks to Separate Interferences and ECG Signals", In: Proceedings of IEEE international Caracas conference on devices, circuits and systems, pp. 291-294, 1998.

Q. Li, C. Rajagopalan, and G.D. Clifford, "A machine learning approach to multi-level ECG signal quality classification", Comput. Methods Programs Biomed., vol. 117, no. 3, pp. 435-447, 2014. crossref(new window)

G.D. Clifford, J. Behar, Q. Li, and I. Rezek, "Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms", Physiol. Meas., vol. 33, no. 9, pp. 1419-1433, 2012. crossref(new window)

J. Behar, J. Oster, Q. Li, and G.D. Clifford, "ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction", IEEE Trans. Biomed. Eng., vol. 60, no. 6, pp. 1660-1666, 2013. crossref(new window)

Y.G. Lim, J.S. Lee, S.M. Lee, H.J. Lee, and K.S. Park, "Capacitive Measurement of ECG for Ubiquitous Healthcare", Ann. Biomed. Eng., vol. 42, no.11, pp. 2218-2227, 2014. crossref(new window)

J.S. Lee, "Adhesive Polyurethane-based Capacitive Electrode for Patch-type Wearable Electrocardiogram Measurement System", J. Biomed. Eng. Res., vol. 35, no. 6, pp. 203-210, 2014. crossref(new window)