The week's pick
Reseach Article
Sudden Cardiac Death (SCD) Prediction based on Fast Sequency Ordered Complex Hadamard Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 156 - Number 14 |
| Year of Publication: 2016 |
| Authors: Padmavathi Kora |
10.5120/ijca2016912510
|
Padmavathi Kora . Sudden Cardiac Death (SCD) Prediction based on Fast Sequency Ordered Complex Hadamard Transform. International Journal of Computer Applications. 156, 14 ( Dec 2016), 1-6. DOI=10.5120/ijca2016912510
Abstract
Electrocardiogram (ECG), a non-invasive diagnostic technique, used for detecting cardiac arrhythmia has gained attention in recent years in medical sciences, industry dealing with Bio-medical instrumentation and research, demanding an advancement in its ability to distinguish different cardiac arrhythmia. Studies conducted in this research work on recent feature extraction methods, such as, Auto Regressive (AR) modeling, Magnitude Squared Coherence (MSC) and Wavelet Coherence (WTC) using standard database (MIT-BIH), also yielded a lot of features. A large number of these features might be insignificant containing some redundant and non-discriminative features that introduce computational burden and loss of performance. A novel technique to classify the ECGs of normal and subjects at risk of SCD using nonlinear technique has been presented. We have predicted SCD by analyzing four minutes of ECG signals prior to SCD occurrence by using Fast CS-SCHT coefficients. This paper presents fast Conjugate Symmetric Sequency Ordered Complex Hadamard Transform (CS-SCHT) for extracting relevant features from the ECG signal. The sparse matrix factorization method is used for developing fast and efficient CS-SCHT algorithm and its computational burden is examined as compared to that of the HT and NCHT. The applications of the CS-SCHT in the ECG based SCD detection is also discussed. In this work, we have achieved good classification accuracy for prediction of SCD. The proposed method is able to detect a person at risk of SCD four minutes earlier.
References
- Nasir Ahmed, KR Rao, and AL Abdussattar. Bifore or hadamard transform. Audio and Electroacoustics, IEEE Transactions on, 19(3):225–234, 1971.
- A Aung and BP Ng. Fast dft implementation using scht. Nanyang Technological Univ., Singapore, 2007.
- Aye Aung and Boon Poh Ng. Conjugate symmetric sequency ordered complex hadamard transform. IEEE Transactions on Signal Processing, 57(7):2582–2593, 2009.
- Aye Aung, Boon Poh Ng, and Susanto Rahardja. Sequencyordered complex hadamard transform: Properties, computational complexity and applications. Signal Processing, IEEE Transactions on, 56(8):3562–3571, 2008.
- Kenneth G Beauchamp. Applications of Walsh and related functions. Academic press, 1984.
- RH Clayton and A Murray. Estimation of the ecg signal spectrum during ventricular fibrillation using the fast fourier transform and maximum entropy methods. In Computers in Cardiology 1993, Proceedings., pages 867–870. IEEE, 1993.
- Elias Ebrahimzadeh, Mohammad Pooyan, and Ahmad Bijar. A novel approach to predict sudden cardiac death (scd) using nonlinear and time-frequency analyses from hrv signals. PloS one, 9(2):e81896, 2014.
- Michelle Effros, Hanying Feng, and Kenneth Zeger. Suboptimality of the karhunen-loeve transform for transform coding. Information Theory, IEEE Transactions on, 50(8):1605– 1619, 2004.
- Hamid Khorrami and Majid Moavenian. A comparative study of dwt, cwt and dct transformations in ecg arrhythmias classification. Expert systems with Applications, 37(8):5751–5757, 2010.
- Jose A Rosendo Macias and Antonio Gomez Exposito. Efficient computation of the running discrete haar transform. Power Delivery, IEEE Transactions on, 21(1):504–505, 2006.
- George Manis, Stavros Nikolopoulos, Petros Arsenos, Konstantinos Gatzoulis, Polychronis Dilaveris, and Christodoulos Stefanadis. Risk stratification for arrhythmic sudden cardiac death in heart failure patients using machine learning techniques. In Computing in Cardiology 2013, pages 141–144. IEEE, 2013.
- C Saritha, V Sukanya, and Y Narasimha Murthy. Ecg signal analysis using wavelet transforms. Bulg. J. Phys, 35(1):68– 77, 2008.
- Tsu-Wang Shen, Hsiao-Ping Shen, Ching-Heng Lin, and Yi- Ling Ou. Detection and prediction of sudden cardiac death (scd) for personal healthcare. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 2575–2578. IEEE, 2007.
- A Voss, J Kurths, HJ Kleiner, A Witt, N Wessel, P Saparin, KJ Osterziel, R Schurath, and R Dietz. The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. Cardiovascular research, 31(3):419–433, 1996.
Index Terms
Keywords