Open Access Open Access  Restricted Access Subscription or Fee Access

ECG Diagnostic using Multi-Scale Principal Components Analysis

H. Chaouch, K. Ouni, L. Nabli



In this paper, a method of ECG diagnostic is proposed. This method is based on three parts: the simplification using multi-scale PCA, the default detection and localization by classic linear PCA. The first part consists of applying a multi-scale analysis based on continuous wavelet transform (CWT); it allows to decompose the signal into wavelet coefficients on five levels of multi-scale resolution. Then, we apply the principal component analysis PCA wholes on the coefficients obtained to determine the number of principal components to retain and which provide more information. The signal is reconstructed by referring only scales of resolution whose main components are found. In the second part, we introduce the resulting signal for detecting and locating defects by PCA by introducing two statistics: SPE and T ². Variables are determined defective by the method of calculating contributions by the same previous statistics. Comparing the results obtained by this approach and the data of the expert proves its reliability in the diagnosis of ECG signal.


Diagnostic, ECG, Multi-Scale PCA, Linear PCA,Default Detection, Default Location, Calculating Contributions.

Full Text:



A.Algra and H.L.B.C Zeelenberg. An algorithm for computer measurement of QT intervals in 24 hour ECG. In Computer in Cardiology. Los Alamitos, CA, IEE computer Society Press, pp 117-119, 1987.

M.Bahoura, M.Hassani, and M.Hubin. DSP implementation of wavelets transform for real time ECG waveforms detection and heart rate analysis. Comput.Meth.Programs Biomed, n°52, pp 35-44, 1997.

L.Clavier and J.M Boucher. Segmentation of electrocardiograms using a Hidden Markov Model. In 18th annual international conference of the IEEE Engineering in Medicine and Biology Society October 31-November 3, 1996..

Z.Dokur, T.Olmez, E.Yazgan, and O.Ersoy. Detection of ECG waveswaveforms by neural networks.Med.Eng.Phys, vol 19,n°8, pages 738-741, 1997.

Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. AICHE Journal, 44(7), pp 1596-1610, 1998.

P.Nomikos et J.F.MacGregor. «Monitoring batch processes using multiway principal component analysis”, AICHE Journal, vol.40, N°8, pp.1361-1375, 1994.

H.F. kaiser. « the application of electronic computers to factor analysis », Educational and Psychological Measurement, vol.20.pp 145-151, 1960.

D.Giancarlo et T.Chiara. « cross-validation methos in principal component analysis : a comparision », Statistical Method and Applications, vol.11, pp71-82, 2002.

G. Xu et T. Kailath. "Fast estimation of principal eigenspace using lanczos algorithm",SIAM Journal on Matrix Analysis and Applications, Vol. 15, N°3, pp. 975-994, 1994.

H. F. Kaiser. "The application of electronic computers to factor analysis", Educational and Psychological Measurement, Vol. 20, pp. 141-151, 1960.

D. Giancarlo et T. Chiara. "Cross-validation methods in principal component analysis: acomparison", Statistical Method and Applications, Vol. 11, pp. 71-82, 2002.

D.Zumoffen et M.Basualdo. « From large chemical plant data to fualt diagnosis integrated to decentralized fault tolerant control: pulp millprocess application », Industrial and Engineering Chemistry Research, vol. 15, pp.1201-1220, 2007.

J.E.Jackson et G.S. Mudeholkar. « Control procedure for residuals associated with principal component analysis », Technometrics, vol.40, N°20, pp. 457-469,1998.

S. J. Qin, S. Valle, et M. Piovoso. "On unifying multi-block analysis with applications to decentralized process monitoring”, Journal of chemometrics, vol. 15, N°9,pp.715-742, 2001.

Qin.S. « Joint diagnosis of process and sensor faults using principal components analysis.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.