T. G. Keshavamurthy

Bio: T. G. Keshavamurthy is an academic researcher from Sri Siddhartha Institute of Technology. The author has contributed to research in topics: Signal processing & Signal. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

Review paper on denoising of ECG signal

Abstract: All the real time signals are non-periodic and non-stationary, which gives more information for any signal processing techniques. Electrocardiogram (ECG) signal is an example of real time signal. ECG signal gives electrical activities that are useful information about the functioning of heart. ECG signals helps in diagnosing the complex cardiac diseases. ECG signals are low frequency signals and contain lot of clinical information. The important characteristic information called features are extracted from ECG signal and used for medical diagnosis. The denoising of signal becomes important stage in any signal processing technique and lot of scope involved in computer aided diagnosis of heart. Many research work focusing on denoising the signal for extracting important features like Extended Kalman Filter, Wavelet Transformation and Singular Value Decomposition (SVD). The denoising signal processing techniques evaluated using mean square error and signal to noise ratio. In addition, ECG signal is used for person authentication. The proposed paper focuses on the study of different techniques of denoising and an identification of person through an ECG signal.

Classification of Arrhythmia Using Machine Learning Techniques

Abstract: Arrhythmia and heart problems are one of the most important health problems in the whole world which leads to various other severe complications, for example, heart attack. As arrhythmia is a type of cardiologic disease, it can be used for pointing out the abnormality from normal heart activity and try to understand about heartbeat whether the heartbeat is normal or not. The main element that only a less number of people informed being discovered as a result of screening indicates that there are missing opportunities to prevent heart disease. There are different methods present for heart. Heart diseases are recognized by capturing information from patient’s body and forward results to doctors to reduce the risk of heart attack. So, the researcher always keeps trying to find out the best solution for this problem. The researchers have done huge research on this area, so according to the comparison between various techniques which are used for classification of arrhythmia, they prefer to use machine learning algorithm to achieve high performance and better accuracy.

ECG-Enhanced Multi-sensor Solution for Wearable Sports Devices

Abstract: Electrocardiogram (ECG) plays a crucial role in the prevention of cardiovascular diseases in humans and various types of ECG monitoring equipment are continuously being developed. Most monitoring methods place the electrodes near the heart or on both arms, based on standard ECG leads. Although accurate monitoring effect has been achieved by these conventional approaches, the wearable performance still need to be improved. This paper proposed a novel ECG-enhanced multi-sensor solution for wearable sports devices. A wireless and wearable ECG detection system based on signal acquisition from left upper-arm was designed to verify this solution method. The system has been evaluated with solid experiments proving that the system has outperformed existing similar system. Moreover, the inertial measurement unit (IMU) and electromyography (EMG) data were detected and fused by the system to determine the validity of the ECG signal. It indicated that the system can achieve a good performance of heart rate detection under different body states.

Dual Electrodes System for acquisition of ECG Waveform

Abstract: Biological signals from the human body play a significance role in monitoring health condition of person. Among these signals which are derived from heart are coined as Electrocardiogram (ECG). The ECG signals allow cardiologist physician to know about the condition of the heart such as stroke and arrhythmia. But the problem in existing ECG unit in hospital care unit have three to twelve electrodes system with the wet Ag/AgCl electrode which needs well trained person. The research objective is to develop and design self-monitoring ECG system with dual electrode from the finger site for people who are suffering from and have a history of a cardio abnormality at home or workplace. Since all biological signals have noise and low frequency so the acquired signal is passed through designed filter and amplifiers. Further acquired signal are display and analyzed interfacing with NI myDAQ and biomedical workbench. 20 subjects of age under 30year ECG signal are acquired using developed prototype and heart rate is calculated. The ECG signals from developed prototype are compared with conventional ECG unit and almost similar results are obtained. Hence, the developed prototype can be used for monitor cardiovascular disease status for people suffering from arrhythmia as well as the athletes and soldiers can benefit to keep track of their heart condition. The developed ECG system is economical and safe to use.

Reduction of Noise of Cloud Medical Images Using Image Enhancement Technique

Abstract: The medical images are commonly available on cloud by researchers and doctors for better diagnosis and find new cures to diseases. However, due to blurriness and noises presented in such images, the intended purpose is not served. This paper presents stationary wavelet transform based two techniques i.e. Daubechies (DB) and HAAR wavelets for Gaussian noise removal from medical images. The computer simulations are carried out on a set of 20 medical images. The remarkable rise in entropy value of every image is noticed. The comparative analysis of MATLAB results suggest that DB is better than HAAR wavelet transform based method to improve the medical images and make them much more useful.