Publications by authors named "Samiappan Dhanalakshmi"

4 Publications

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An improved parallel sub-filter adaptive noise canceler for the extraction of fetal ECG.

Biomed Tech (Berl) 2021 May 4. Epub 2021 May 4.

Faculty of Engineering and Technology, Department of ECE, College of Engineering and Technology, SRM Institute of Science and Technology, Kancheepuram,Tamil Nadu, India.

Non-invasive extraction of fetal electrocardiogram (FECG) by processing the abdominal signals is emerging as a promising approach in the areas of obstetrics and gynecology. This paper presents a two-stage improved non-linear adaptive filter for FECG extraction. The reference input to the adaptive noise canceler (ANC) is first processed using an adaptive neuro-fuzzy inference system (ANFIS) to estimate the non-linear maternal component in abdominal signals. A parallel sub-filter (PSF) ANC is proposed to assess the fetal ECG from the abdominal signal. The PSF-ANC decomposes a single adaptive filter into multiple sub-filters to improve the convergence performance. The filter coefficients of PSF-ANC adaptively obtained using normalised least mean square algorithm by minimizing the mean square error. Different error and common error algorithms are proposed based on the computation of the error signal. A synthetic data from the FECG synthetic database is used to evaluate the convergence performance. Two real-time data from the Daisy database and the Non-invasive FECG database from Physionet are used to evaluate the proposed ANFIS-PSF's performance qualitative and quantitatively. The results justify the performance improvement of proposed ANFIS-PSF ANC compared to the state of art techniques. The proposed scheme achieves a sensitivity of 97.92%, 94.52% accuracy, a positive predictive value of 94.66%, and an F1 score of 96.12%.
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http://dx.doi.org/10.1515/bmt-2020-0313DOI Listing
May 2021

Despeckling of Carotid Artery Ultrasound Images with a Calculus Approach.

Curr Med Imaging Rev 2019 ;15(4):414-426

Electronics and Communication Engineering Department, SRM Institute of Science and Technology, Kancheepuram, Tamil Nadu, India.

Background: Carotid artery images indicate any presence of plaque content, which may lead to atherosclerosis and stroke. Early identification of the disease is possible by taking B-mode ultrasound images in the carotid artery. Speckle is the inherent noise content in the ultrasound images, which essentially needs to be minimized.

Objective: The objective of the proposed method is to convert the multiplicative speckle noise into additive, after which the frequency transformations can be applied.

Method: The method uses simple differentiation and integral calculus and is named variable gradient summation. It differs from the conventional homomorphic filter, by preserving the edge features to a great extent and better denoising. The additive image is subjected to wavelet decomposition and further speckle filtering with three different filters Non Local Means (NLM), Vectorial Total Variation (VTV) and Block Matching and 3D filtering (BM3D) algorithms. By this approach, the components dependent on the image are identified and the unwanted noise content existing in the high frequency portion of the image is removed.

Results & Conclusion: Experiments conducted on a set of 300 B-mode ultrasound carotid artery images and the simulation results prove that the proposed method of denoising gives enhanced results as compared to the conventional process in terms of the performance evaluation methods like peak signal to noise ratio, mean square error, mean absolute error, root mean square error, structural similarity, quality factor, correlation and image enhancement factor.
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http://dx.doi.org/10.2174/1573405614666180402124438DOI Listing
June 2020

Carotid artery ultrasound image analysis: A review of the literature.

Proc Inst Mech Eng H 2020 May 21;234(5):417-443. Epub 2020 Jan 21.

Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India.

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima-media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima-media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning-based approaches like self-organizing map, -nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.
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http://dx.doi.org/10.1177/0954411919900720DOI Listing
May 2020

Fusion of WPT and MFCC feature extraction in Parkinson's disease diagnosis.

Technol Health Care 2019 ;27(4):363-372

Background: Parkinson's disease (PD) is a neurological disorder, progressive in nature. In order to provide customized patient care, diagnosis and monitoring using smart gadgets, smartphones, and smartwatches, there is a need for a system that works in natural as well as controlled environments.

Objective And Methods: The primary purpose is to record speech signal, and identify whether the speech signal is Parkinson or not. For this work, a comparison of three feature extraction methods, i.e. Wavelet Packets, MFCC, and a fusion of MFCC and WPT, were carried out. Apart from the feature extraction, two classifiers were used, i.e. HMM and SVM.

Results: In this study, a fusion of MFCC, WPT with HMM shows the best performance parameters.

Conclusion: The best of the three feature extraction and classifier results are described in this paper.
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http://dx.doi.org/10.3233/THC-181306DOI Listing
February 2020