STUDY OF BIOMEDICAL SIGNAL MEASUREMENT & ANALYSIS TECHNIQUES

Abstract

Biomedical signal is a summarizing term for all kinds of signals that can be continually measured and monitored from living creatures including human beings. Electrocardiogram (ECG), perhaps is the most common and popular 1-D biomedical signals as it is directly associated with one of the most important organs of human body that is activities of the heart. ECG has a wide range of applications in cardiac diagnostics and is preferred over other methods as it is largely non-invasive, safe to the patient, easy to obtain, provides instantaneous results with highest level of accuracy. The appropriate analysis of the ECG signals using suitable means is of utmost importance, before any diagnostics. Therefore, to fulfill this requirement the higher order cumulants are an effective mathematical tool for the analysis of nonlinear and non-stationary ECG signals. The proposed method in this thesis, classifies dataset of ECG signals based upon higher order statistics i.e., using cumulants. It provides a quality detection technique in comparison to the other methods used earlier in this research domain. However, ECG very easily gets contaminated by various types of noises and artefacts during the process of its acquisition. Therefore, this contaminated ECG must be “cleaned” to the appropriate level, it means to minimize the counter-productive effects of embedded noise and artefacts so as to enhance the required information, before using it for any further processing for diagnosis or interpretation. There are different types of noises or artefacts present in any ECG signal, but baseline wander is considered as severest one. This research work is mainly focused on proposing novel methods for removal of baseline wander and other types of noises from ECG signal. Accordingly, a new method is proposed for baseline wander artefact denoising from ECG using cascaded combination of Complete Ensemble Empirical Mode Decomposition (CEEMD) and Artificial Neural Networks [ANN]. The proposed method maintains morphology of ECG signal during denoising and thus there is no loss of vital information from the ECG signal. The denoising of ECG signal is further enhanced by another novel approach making use of Genetic Particle filter improved fuzzy-AEEMD, which has been proposed in this thesis. The performance of proposed methods is tested with different types of readily available ECG datasets and compared with other state-of-the-art methods and these proposed methods proved to be more effective and efficient denoising methods. ABSTRACT 6 Biomedical signal measurement is one of the most important aspects of biomedical signal analysis and interpretation to support scientific hypotheses and medical diagnoses. Biomedical signal measurement aims at appropriately acquiring and measuring biomedical signals for accurate and improved diagnosis and proper medicine management. Extensive research is going on in the field of Bio-Medical Measurements and Instrumentation to find out new non-invasive ways and methods for diagnosis and measurement of health parameters for the welfare of the mankind. Non-invasive techniques are more suitable than the invasive ones if sufficient accuracy can be achieved using them. Among the available non-invasive medical devices and techniques, perhaps bioimpedance based diagnostics is still highly unexplored and underrated owing to insufficient research efforts. Keeping above scenario in mind accordingly, an efficient low-cost bioelectrical impedance measuring instrument was developed, implemented, and tested in this study. Primarily, it is based upon the low-cost component-level approach so that it can be easily used by researchers and investigators in the specific domain. The measurement setup of instrument was tested on adult human subjects to obtain the impedance signal of the forearm which is under investigation in this case. However, depending on the illness or activity under examination, the instrument can be used on any other part of the body. The technique is easy and user-friendly, and it does not necessitate any special training, therefore it can be effectively used to collect bioimpedance data and interpret the findings for medical diagnostics.