Publications by authors named "Gnanou Florence Sudha"

4 Publications

  • Page 1 of 1

Hybrid algorithm for multi artifact removal from single channel EEG.

Biomed Phys Eng Express 2021 May 11;7(4). Epub 2021 May 11.

Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry-605014, India.

Electroencephalogram (EEG) signals recorded from the ambulatory systems are mostly contaminated by various artifacts like, electrooculogram (EOG), motion artifacts (MA) and electrocardiogram (ECG) artifacts. These artifacts limit the accuracy in further analysis of EEG in practise. So far, several existing methods have been proposed with the combination of decomposition techniques and independent component analysis (ICA) to remove single artifacts and only few methods to remove multiple artifacts from the single channel EEG. As improperly denoised EEG signals can result in wrong diagnosis, in this work, Singular Spectrum Analysis (SSA) and ICA are jointly combined with Generalized Moreau Envelope Total Variation (GMETV) technique to simultaneously remove combinations of different artifacts from single channel EEG. In this work, the SSA is used to decompose the contaminated single channel EEG, while the ICA is employed to separate the various hidden sources as independent components (ICs). Although the ICA is adequate in source separation, there is still, some essential EEG signal data appearing as artifact in the IC. Hence, eliminating this would allow EEG signal information to be lost. The GMETV approach is proposed in this paper, to estimate the actual artifacts in order to address these issues. The estimated actual artifacts are subtracted from the artifact ICs providing the residue of wanted component of EEG. This residue is added back to the remaining ICs, to obtain the denoised EEG. Simulation results demonstrated that the proposed technique performs better compared to the existing techniques. The Relative Root Mean Square Error (RRMSE) is reduced by 12.02% and 7.22% compared to SSA-ICA and SSA-ICA-thresholding respectively. Similarly, the Correlation Coefficient (CC) is increased by 21.48% and 8.25% with respect to SSA-ICA and SSA-ICA-thresholding respectively.
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http://dx.doi.org/10.1088/2057-1976/abfd81DOI Listing
May 2021

Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks.

Artif Intell Med 2020 03 20;103:101809. Epub 2020 Jan 20.

Department of Electronics and Communication Engineering, Pondicherry Engineering College Puducherry, 605 014, India. Electronic address:

Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have been used to analyze the lung sounds. However, an accurate prediction model for respiratory disorders has not been developed so far. In this paper, a pre-trained optimized Alexnet Convolutional Neural Network (CNN) architecture is proposed for predicting respiratory disorders. The proposed approach models the segmented respiratory sound signal into Bump and Morse scalograms from several intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) method. From the extracted intrinsic mode functions, the percentage energy calculated for each wavelet coefficient in the form of scalograms are computed. Subsequently, these scalograms are given as input to the pre-trained optimized CNN model for training and testing. Stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms were examined to check the prediction accuracy on the dataset comprising of four classes of lung sounds, normal, crackles (coarse and fine), wheezes (monophonic & polyphonic) and low-pitched wheezes (Rhonchi). On comparison to the baseline method of standard Bump and Morse wavelet transform approach which produced 79.04 % and 81.27 % validation accuracy, an improved accuracy of 83.78 % is achieved by the virtue of scalogram representation of various IMFs of EMD. Hence, the proposed approach achieves significant performance improvement in accuracy compared to the existing state-of- the-art techniques in literature.
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http://dx.doi.org/10.1016/j.artmed.2020.101809DOI Listing
March 2020

Dual-frequency bioelectrical phase angle to estimate the platelet count for the prognosis of dengue fever in Indian children.

Biomed Tech (Berl) 2020 Aug;65(4):417-428

Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry 605014, India.

A noninvasive investigation to ascertain the platelet (PLT) count was conducted on 44 hospitalized dengue hemorrhagic fever (DHF) subjects, male and female aged between 3 and 14 years using bioelectrical phase angle (BPhA). Among the 44 subjects, 30 subjects were confirmed to be non-structural protein-1 (NS1) positive at the time of admission, whose blood investigations such as hematocrit (HCT) level, PLT count, aspartate aminotransferase (AST) level and alanine aminotransferase (ALT) level were performed for the classification of risk as low-risk (LR) and high-risk (HR) DHF. It was found that the BPhA of the body reflects a linear correlation with the PLT count. To provide a better and more accurate estimate of PLT, a dual-frequency method is proposed to calculate the phase angle of the total body. The resistance at 5 kHz and reactance at 100 kHz were used to estimate the phase angle of the total body. The statistical analysis identified that the PLT count estimated using the proposed dual-frequency method shows a good correlation with the blood investigation results. In addition, statistical analysis of the proposed method on other fever subjects indicated a significant difference with DHF.
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http://dx.doi.org/10.1515/bmt-2018-0203DOI Listing
August 2020

Dual frequency bioelectrical impedance analysis to estimate hematocrit for prognosis of dengue fever in Indian children.

Biomed Tech (Berl) 2019 Aug;64(4):459-469

Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India.

A noninvasive investigation to ascertain the hematocrit (HCT) or packed cell volume (PCV) was conducted on 44 hospitalized dengue hemorrhagic fever (DHF) subjects, male and female aged between 3 and 14 years using bioelectrical impedance analysis (BIA). Among the 44 subjects, 30 subjects were confirmed to be non-structural protein-1 (NS1) positive at the time of admission, whose blood investigations such as HCT level, platelet (PLT) count, aspartate aminotransferase (AST) level and alanine aminotransferase (ALT) level were taken for the classification of risk as low risk (LR) and high risk (HR) DHF. Electrical conductivity of blood reflects a linear correlation with HCT. To provide a better and more accurate estimate of HCT, a dual frequency method is proposed to calculate the conductivities of plasma and blood cells. The resistance at 100 kHz is used to estimate the conductivity of blood cells and the impedance at 5 kHz to estimate the conductivity of plasma. Statistical analysis reveals that the HCT estimated using the proposed dual frequency method shows a significant difference with a single frequency (50 kHz) estimate of HCT and also shows a good correlation with the blood investigation results. In addition, statistical analysis of the proposed method on different fever subjects indicates a significant difference with DHF.
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http://dx.doi.org/10.1515/bmt-2017-0174DOI Listing
August 2019
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