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

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Papers by Dr.VENKATESAN C

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.

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.

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.

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.

The main focus of this work is to propose suitable architectures for the decimation filter networ... more The main focus of this work is to propose suitable architectures for the decimation filter networks of digital receivers that use the reduced logic and are capable of receiving multiple communication standard signals. It also involves the design, simulation, and implementation of multi-stage multi-rate filter architectures with reduced Very Large Scale Integrated (VLSI) cost functions. In the first multi-stage architecture namely, the Multi-Standard Decimation Filter (MSDF) structure is proposed to cater to the need of reception of multistandard receiver signals. The MSDF architecture is designed for GSM and WiMAX wireless communication specifications and its first stages are designed using Cascaded Integrator Comb (CIC) filters. In the second architecture, a modified MSDF structure is implemented using polynomial CIC filters to meet the multi-standard requirements. The third architecture concentrates on design parameters of polyphase CIC-based decimation filter and its implementation concepts. Spartan FPGA-based implementation results that the proposed polynomial CIC-based MSDF architecture provides 32.11% of area reduction when related with the multistage MSDF. The proposed polyphase CIC-based MSDF architecture provides 28.57% of dynamic power-saving and a 15.5% increase in speed when compared to polynomial-based MSDFC architecture. Thus, the proposed polyphase MSDF architecture provides low power and lesser delay solutions using a multistage decimation approach and it is best suited for multi-standard communication applications in digital receivers.

In bioinformatics research, cancer classification is a crucial domain. The use of microarray tech... more In bioinformatics research, cancer classification is a crucial domain. The use of microarray technology to identify specific illnesses is common. A small number of genes uncovered in clinical applications can lead to low-cost medicines that can help estimate a patient's survival time or diagnose cancer. Because there are more genes and fewer samples in microarray data, high dimensionality is a serious concern. The genes in the microarray data were evaluated using F-statistics,T-Statistics, and Signal-to-Noise Ratio (SNR) in this study.The top-m rated genes are analyzed using optimization approaches to retrieve useful information. The genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and shuffling frog leaping with rapid flying are among the methods employed (SFLLF). Classification is done using the Support vector machine (SVM), the K-Nearest Neighbor classifier (KNN), and the Naive Bayes classifier (NBC). Lung Cancer Michigan, AML-ALL, Colon Tumour, Lung Harvard2, and others are among the datasets utilized for experimental analysis. The classifiers are assessed using a 5-fold cross-validation approach. The findings demonstrate that the suggested two-step feature selection approaches are effective in selecting relevant genes from microarray data for cancer classification.