Particle Swarm Intelligence Based Univariate Parameter Tuning of Recursive Least Square Algorithm for Optimal Heart Sound Signal Filtering

Mary Mekala ANTONY DHAS; Srimathi CHANDRASEKARAN

doi:https://doi.org/10.35378/gujs.501114

Makale detayları

Particle Swarm Intelligence Based Univariate Parameter Tuning of Recursive Least Square Algorithm for Optimal Heart Sound Signal Filtering

Yazarlar:

Mary Mekala ANTONY DHAS | Srimathi CHANDRASEKARAN

Yayıncı:

Gazi University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Gazi University Journal of Science 2019 Vol.32 Issue 3, pp.928-943

Belge Tipi:

Makale

Yayın Tarihi:

8/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000427957

eISSN:

2147-1762

DOI:

https://doi.org/10.35378/gujs.501114

Özet:

 

Heart Sound Signal (HSS) is considered as one of the important bio-signals. It carries vital information about the heart functions. For bio-acoustic observations, the HSS is diagnosed and recorded with auscultatory procedures. During auscultation, the noisy components gets added along with the reading. The physician’s individual diagnostic experience, ecological noise and the intersection of heart and lung sound signal (LSS) are considered as the major noisy components in HSS diagnosis. Suppression of LSS from the HSS is a challenging task. Due to its quasi stationary nature, adaptive filtering techniques are used for the noise removal. In this paper, Recursive Least Square (RLS) adaptive algorithm is proposed to obtain the HSS from the noisy mixture. Faster convergence is a benefit in selecting RLS algorithm over other adaptive algorithms. The forgetting factor is one of the important parameters of RLS which defines the convergence. The RLS performance is improved by choosing an optimal forgetting factor. A Particle Swarm Optimization (PSO) based search algorithms are deployed for optimization. To enhance the implementation time, a Dynamic Neighbourhood Learning Particle Swarm Optimizer (DNL-PSO) is analysed. In DNL-PSO, each particle studies from its knowledge in dynamically varying neighbourhood that prevents early convergence. The normal HSS with different LSS interference is taken to assess the RLS filter performance. In this paper, the RLS algorithm performance is compared with Least Mean Square (LMS) adaptive algorithms. Various metrics are used to compare the performance of both RLS and optimization algorithms.

Makale detayları

Particle Swarm Intelligence Based Univariate Parameter Tuning of Recursive Least Square Algorithm for Optimal Heart Sound Signal Filtering

Yazarlar: Mary Mekala ANTONY DHAS | Srimathi CHANDRASEKARAN

Yayıncı: Gazi University

Ana Başlıklar: Uygulamalı fizik

Dergi: Gazi University Journal of Science 2019 Vol.32 Issue 3, pp.928-943

Belge Tipi: Makale

Yayın Tarihi: 8/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000427957

eISSN: 2147-1762

DOI: https://doi.org/10.35378/gujs.501114

Sayı

Yazarlar:

Mary Mekala ANTONY DHAS | Srimathi CHANDRASEKARAN

Yayıncı:

Gazi University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Gazi University Journal of Science 2019 Vol.32 Issue 3, pp.928-943

Belge Tipi:

Makale

Yayın Tarihi:

8/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000427957

eISSN:

2147-1762

DOI:

https://doi.org/10.35378/gujs.501114