De-Noising of ECG Signals by Design of an Optimized Wavelet

Kaviri, Vahid Makhdoomi; Sabaghi, Masoud; Marjani, Saeid

doi:10.4236/cs.2016.711314

Cited by 4 publications

(1 citation statement)

References 21 publications

(9 reference statements)

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“…The study finds that the four-level Daubechies (db4) type is optimal for the Massachusetts institute of technology-Beth Israel arrhythmia (MIT-BIH) database, while the level two of Symlets 4 (sym4) is suitable for ECG signals from the monitoring system. The discrete wavelet transform (DWT) based wavelet denoising technique is used with three different wavelet functions and four different thresholding methods [19]. The study involved the analysis of ECG signals collected from a cohort of ten female participants aged between 20 and 25 years.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

Phuphanin,

Tasakorn,

Srivichai

2024

IJECE

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The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.

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Section: Introductionmentioning

confidence: 99%