Dr.C. Venkatesan - Academia.edu (original) (raw)

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Papers by Dr.C. Venkatesan

Adaptive filters are increasingly popular in electrocardiogram noise cancellation due to their in... more Adaptive filters are increasingly popular in electrocardiogram noise cancellation due to their inherent ability to deal with nonstationary signals. In the past, adaptive filters with least mean square (LMS) algorithm and normalized LMS algorithm have been used for updating coefficients which is simple and provides satisfactory convergence performance. However, the LMS based adaptive filters use a long critical path to obtain the output. The critical path is generally reduced by pipelined structures with delay elements so that the desired sample period can be attained. In this paper, a delayed error normalized LMS adaptive filter is proposed to achieve high speed and low latency design with less computational elements. Simulation results show that the proposed technique provides better convergence performance with least mean square error.

High frequency noise and channel noise are dominant in wireless ECG monitoring systems which can ... more High frequency noise and channel noise are dominant in wireless ECG monitoring systems which can be modeled as white Gaussian noise. Least mean square (LMS) algorithm based adaptive filters are the preferred choice for white Gaussian noise removal, because they require fewer computations and less amount of power consumption. Though LMS algorithm is simple to implement in real time systems, it is necessary to modify the LMS algorithm to reduce the mean square error for improved filtering performance. In this paper, a delayed error normalized LMS (DENLMS) adaptive filter is studied with pipelined architecture to remove the white Gaussian noise from ECG signal. The pipelined VLSI architecture is utilized to boost the operational speed of adaptive filter by reducing the critical path using delay elements. The performance of pipelined DENLMS algorithm is compared with ENLMS and DNLMS algorithms. The pipelined DENLMS filter increases the speed of operation and reduces power consumption at the cost of increase in area due to the presence of latches. Virtex 5 FPGA XC5LVX330 Field programmable gate array has been utilized as target chip to determine the speed, logic utilization and power consumption.

ECG signal abnormality detection is useful for identifying heart related problems. Two popular ab... more ECG signal abnormality detection is useful for identifying heart related problems. Two popular abnormality detection techniques are ischaemic beat classification and arrhythmic beat classification. In this work, ECG signal preprocessing and KNN based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. LMS based adaptive filters are used in ECG signal preprocessing, but they consume more time for processing due to long critical path. To overcome this problem, a novel adaptive filter with delayed error normalized LMS algorithm is utilized to attain high speed and low latency design. Low power design is achieved in this design by applying pipelining concept in the error feedback path. R-peak detection is carried out in the preprocessed signal using wavelets for HRV feature extraction. Arrhythmic beat classification is carried out by KNN classifier on HRV feature extracted signal. Classification performance reveals that the proposed DWT with KNN classifier provides the accuracy of 97.5% which is better than other machine leaning techniques.

Speckle noise removal plays a crucial role in ultrasound (US) image diagnosis, since the visual q... more Speckle noise removal plays a crucial role in ultrasound (US) image diagnosis, since the visual quality of the US images are largely corrupted by speckle noise. Numerous speckle noise removal techniques have been proposed in the literature based on anisotropic filtering, wavelets and morphology; however they have some major problems like loss of edge information, texture information and inability to remove low frequency noise. Despeckling of US images is usually carried out using conventional anisotropic diffusion or speckle reducing anisotropic diffusion. However, despeckling US images may not be able to preserve the edges which comprises of important clinical information. To overcome the issues in speckle noise removal (despeckling) of US images, contourlet transform based anisotropic nonlinear diffusion filtering is proposed in this paper. Contourlet transform improves some important features like multiscale and directionality. Adaptive nonlinear diffusion has been incorporated in anisotropic filtering to improve the filtering performance. The comparison performance of the proposed method with other despeckling techniques indicates that it has better noise removal performance for medical US images.

Advancement in healthcare technologies and biomedical equipment leads to accurate diagnosis of he... more Advancement in healthcare technologies and biomedical equipment leads to accurate diagnosis of heart related diseases. The major challenges associated with telehealthcare technologies are complex computational requirement and large amount of data processing in continuous monitoring. Mobile cloud computing approach is presented in this work to overcome the issues involved in ECG telemonitoring. Mobile cloud approach is superior to telehealth monitoring techniques due to the access to centralized cloud data and report delivery to mobile phones. In this work, ECG telemonitoring and coronary heart disease (CHD) risk assessment are combined using mobile cloud computing approach. CHD risk is identified using feature extraction and adaptive neuro fuzzy inference system (ANFIS) based classification. In feature extraction process, R-peaks are detected using wavelet transform to find heart rate variability (HRV) of the ECG signal. Various HRV parameters are extracted and applied to ANFIS classifier which employs adaptive feature selection to evaluate CHD risk. Since the mobile cloud approach deals with large amount of data, 160 files of MIT-BIH arrhythmia database has been used in this work for the assessment of CHD risk. ECG signal data are classified into two categories (normal and CHD risky) using ANFIS classifier. The classifier performance is evaluated and comparison is established with other similar classifiers.

In this paper, the egg fertilisation is one of the major factors to be considered in the poultry ... more In this paper, the egg fertilisation is one of the major factors to be considered in the poultry farms. The smart incubation system is designed to combine the IoT technology with the smart phone in order to make the system more convenient to the user in monitoring and operation of the incubation system. The incubator is designed first with both setter and the hatcher in one unit and incorporating both still air incubation and forced air incubation which is a controller and monitored by the controller keeping in mind the four factors: temperature, humidity, ventilation and egg turning system. Here we are setting with three different temperatures for the experimental purpose at T1 = 36.5°C, T2 = 37.5°C and T3 = 38°C. The environment is maintained same in all the three cases and which is the best temperature for the incubation of the chicken eggs is noted.

Digital image watermarking plays an important role in digital content protection and security rel... more Digital image watermarking plays an important role in digital content protection and security related applications. Embedding watermark is helpful to identify the copyright of an image or ownership of the digital multimedia content. Both the grey images and colour images are used in digital image watermarking. In this work, discrete stationary wavelet transform and singular value decomposition (SVD) are used to embed watermark into an image. One colour image and one watermark image are considered here for watermarking. Three level wavelet decomposition and SVD are applied and watermarked image is tested under various attacks such as noise attacks, filtering attacks and geometric transformations. The proposed work exhibits good robustness against these attacks and obtained simulation results show that proposed approach is better than the existing methods in terms of bit error rate, normalised cross correlation coefficient and peak signal to noise ratio.

Coronary heart disease (CHD) is a major chronic disease which is directly responsible for myocard... more Coronary heart disease (CHD) is a major chronic disease which is directly responsible for myocardial infarction. Heart rate variability (HRV) has been used for the prediction of CHD risk in human beings. In this work, neuro fuzzy-based CHD risk prediction is performed after performing pre-processing and HRV feature extraction. The pre-processing is used to remove high frequency noise which is modelled as white Gaussian noise. The real-time ECG signal acquisition, pre-processing and HRV feature extraction are performed using NI LabVIEW and DAQ board. A 30 seconds recording of ECG signal was selected in both smokers and non-smokers. Various statistical parameters are extracted from HRV to predict coronary heart disease (CHD) risk among the subjects. The HRV extracted signals are classified into normal and CHD risky subjects using neuro fuzzy classifier. The classification performance of the neuro fuzzy classifier is compared with the ANN, KNN, and decision tree classifiers.

Medical expert systems are part of the portable and smart healthcare monitoring devices used in d... more Medical expert systems are part of the portable and smart healthcare monitoring devices used in day to day life. Arrhythmic beat classification is mainly used in ECG abnormality detection for identifying heart related problems. In this work, ECG signal preprocessing and SVM based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning based classifiers.