Authors’ Reply

Author

Karthik, Gundllapalli Venkata Sai ; Fathima, Shaik Yasmin ; Rahman, Muhammad Zia Ur ; Shaik, Rafi Ahamed ; Lay-Ekuakille, Aime

Author_Institution

Honeywell Technol. Solutions Lab. Pvt. Ltd., Hyderabad, India

Volume

15

Issue

9

fYear

2015

fDate

Sept. 2015

Firstpage

5351

Lastpage

5351

Abstract

In this paper, we are concerned with the last mean fourth (LMF) algorithm and its variants. Using LMF variants, we have implemented several adaptive noise cancelers (ANCs) to enhance EEG signals those suitable for remote health care monitoring applications. There are no misconceptions in this paper. In this paper, we only concentrated on LMF-based algorithms. Based on several variations, we have implemented various ANCs for EEG signal enhancements. However, when we are applying signum function to error signal, qualitatively there is no difference between sign least mean square, sign sign least mean square and sign least mean fourth, sign sign least mean fourth, respectively. As the analyzed entity is LMF based algorithms, we do not compare the implementations with LMS counter parts.

Keywords

bioelectric potentials; cancer; electroencephalography; health care; least mean squares methods; medical signal processing; neurophysiology; patient monitoring; signal denoising; EEG signal enhancements; LMF variants; LMF-based algorithms; LMS counter parts; adaptive noise cancelers; error signal; last mean fourth algorithm; least mean square methods; remote health care monitoring applications; Brain; Electroencephalography; Least squares approximations; Medical services; Monitoring; Noise; Sensors; Adaptive noise cancelers; LMF algorithm; adaptive noise cancelers; artifact; brain wave; remote health monitoring; signal conditioning;

fLanguage

English

Journal_Title

Sensors Journal, IEEE

Publisher

ieee

ISSN

1530-437X

Type

jour

DOI

10.1109/JSEN.2015.2432453

Filename

7110314