ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.
BACKGROUND
Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients.


METHODS
The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance.


RESULTS
A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal.


CONCLUSIONS
The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.
ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco. el.bcharri@gmail.com.
Oussama
http://orcid.org/0000-0001-5714-0176
O
El B'charri
ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco.
Rachid
R
Latif
High School of Biomedical Engineering, UM6SS University, Casablanca, Morocco.
Khalifa
K
Elmansouri
ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco.
Abdenbi
A
Abenaou
ESSI-LISTI Laboratory, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco.
Wissam
W
Jenkal
D000465:Algorithms; D016477:Artifacts; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis
De-noising; Dual tree wavelet transform; ECG; Realistic noise; Threshold tuning