What is the clinical evidence from this study?

Study design: Other. Population: ECG noise and baseline wandering. Intervention: Multiadaptive Bionic Wavelet Transform (MABWT) vs. Wavelet Transform (WT). Primary outcome: Signal-to-Noise Ratio (SNR) improvement (dB) (1.69 dB).

March 19, 2007Open Access

Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction

OSOmid SayadiMassachusetts General Hospital MSMohammad Bagher ShamsollahiSharif University of Technology

Key Result

The multiadaptive bionic wavelet transform with soft thresholding improved the average signal-to-noise ratio of ECG signals by 1.69 dB compared to traditional wavelet transform methods.

Structured PICO

Does the multiadaptive bionic wavelet transform (MABWT) improve noise reduction in ECG signals compared to standard wavelet transform?

Population

ECG signals from the MIT-BIH arrhythmia database and simulated signals used to validate a novel denoising algorithm.

Intervention

Multiadaptive bionic wavelet transform (MABWT)

Comparator

Wavelet transform (WT)

Outcome

Noise reduction (Signal-to-Noise Ratio improvement) and baseline wandering cancellationsurrogate

The proposed MABWT algorithm outperforms standard wavelet transform methods for ECG denoising and baseline wandering reduction.

Main Result

Mean Difference: 1.69

Absolute Event Rate: 7.67% vs 5.98%

Limitations

Abrupt changes in abnormal ECG beats may not be tracked efficiently unless adaptation includes all decomposition subbands.
The step-by-step multiadaptation implementation is possibly time consuming.

Abstract

We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the -function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.

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