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January 1, 2019International Journal of Advanced Computer Science and ApplicationsOpen Access

NLMS achieved the highest SNR (22.17 dB) with a computation time of 3.05s, whereas Sign LMS had the lowest computation time (1.15s) with an SNR of 16.5 dB.

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Why the study?

Accurate computer analysis of ECG signals is challenging due to high frequency noise and artifacts, which impact the signal-to-noise ratio required by machine learning algorithms in remote healthcare systems.

Comparison

Several adaptive filtering algorithms for removing high frequency noise

Authors

ZZia-ul-HaqueRQRizwan QureshiMNMehmood Nawaz

Discussion

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Overview

NLMS may enhance ECG SNR for remote ML analysis; leaves open optimal filtering choice without clinical validation.

Structured PICO

P
Population
ECG signals from the MITDB database (n=5 signals used for average results)
I
Intervention
Adaptive filtering algorithms (LMS, NLMS, Log LMS, Sign LMS) for high-frequency noise removal
C
Comparator
Comparison among the four algorithms
O
Outcome
Signal-to-Noise-Ratio (SNR) and computation timesurrogate

The Normalized Least Mean Square (NLMS) algorithm provides superior signal-to-noise ratio for ECG de-noising, while Sign LMS offers better computational efficiency.

Cite This Study

Zia-ul-Haque et al. (2019) studied this question.

synapsesocial.com/papers/6a70d64535aa2c282ce22924https://doi.org/10.14569/ijacsa.2019.0100370
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