Details

Autor(en) / Beteiligte

• Swetha, R.
• Ramakrishnan, Sabitha

Titel

Unfolding VLSI Architecture for Mixed Noise Removal and Multiple Classification of ECG Signals

Ist Teil von

• Circuits, systems, and signal processing, 2024-04, Vol.43 (4), p.1993-2015

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Denoising electrocardiogram (ECG) signals is a challenge because of the unavoidable artifacts. In this paper, an adaptive and effective signed error normalized least mean square algorithm (SE-NLMS) is proposed to improve denoising and reduce the mean square error (MSE). The performance of the SE-NLMS algorithm with unfolding technique (SE-UNLMS) has been compared with that of the normalized least mean square algorithm with unfolding structure (UNLMS) as well as the least mean square algorithm with unfolding structure (ULMS). SE-NLMS has the advantage of NLMS, namely an adaptive convergence rate and reduced computational complexity. We have implemented all three algorithms, namely LMS, NLMS and SE-NLMS, using an unfolding architecture that offers minimum delay and is suitable for real-time applications. Performance metrics such as an MSE of 0.0062 and a signal-to-noise ratio (SNR) improvement of about 10.2% are achieved through the proposed method of SE-UNLMS compared to other denoising methods. Additionally, exploration has been carried out for the application of denoised ECG signals under various arrhythmia conditions. The time-domain features were extracted from various denoised ECG signals and classified with support vector machines (SVM) to yield a good classification accuracy of 96.5%. The SE-UNLMS denoising method is demonstrated to be a valuable implementation for the detection and diagnosis of arrhythmias in real-world medical situations via this classification process. For real-time validation, the proposed method SE-UNLMS was implemented on a field-programmable gate array (FPGA) using Spartan 6 VPTB-20. The FPGA implementation highlights the feasibility of the proposed SE-UNLMS for real-time ECG monitoring applications.