Fetal heart abnormality detection based on subspace separation and Wiener filtering

Gunarathne, M.S.L.; Wansekara, Wmgm; Wimalarathna, Ghi; Godaliyadda, G. M. R. I.; Ekanayake, M. P. B.; Wijayakulasooriya, J. V.; Rathnayake, R.M.C.J.

doi:10.1109/iciinfs.2017.8300349

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Combination of discrete wavelet transform and Wiener filter had the best performance on HRECG denoising in aspect of Least squares [9]. A two-step ECG denoising algorithm from Nikolaev [10] and adaptive Wiener filter from Wang [11] and Gunarathne [12] were used to denoise ECG. However, most methods are based on the stability of ECG.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Electrocardiogram Enhancement in Strong Noise Environment

Li¹,

Wang²,

Yang³

et al. 2022

Computing in Cardiology Conference (CinC)

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The electrocardiogram (ECG) is a common and important indicator for diagnosing cardiovascular diseases. The wearable ECG monitoring equipment provides patients with long-term ECG monitoring. But the acquisition signals are susceptible to motion artifact (MA). Reducing MA while ECG processing will help accurately analyse the ECG and make a correct judgment on patients. This paper mainly analyses how to enhance the ECG collected under long-term monitoring and tries to propose an adaptive ECG enhancement method which is composed of adaptive division of human motion state and a modified adaptive Wiener filter based on Bayesian estimation. The method is evaluated on MITDB and CPSC2019 database, as well synchronous ECG, and three-axis acceleration data in the real world. The heart rate performance index is designed, and it is found that the heart rate calculation accuracy can be improved by 24.5% after the ECG is enhanced. It is proved that the method can achieve a good performance of ECG enhancement under different body motion states.

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