Analysis & Interpretation of Biomedical Signals using component extraction techniques

Biomedical signals can arise from one or many sources including heart, brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The analysis of these signals is important both for research and for medical diagnosis and treatment. The applications of Independent Component Analysis (ICA) to biomedical signals is a rapidly expanding area of research and many groups are now actively engaged in exploring the potential of blind signal separation and signal deconvolution for revealing new information about the brain and body .The Biomedical time series signal like elctroencephalogram(EEG), electrocardiogram(ECG), etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The immense scope in the field of biomedical-signal processing Independent Component Analysis( ICA ) is gaining momentum due to huge data base requirement for quality testing . The diagnosis of patient is based on visual observation of recorded ECG, EEG, etc. may not be accurate. To achieve better understanding PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals. This paper describes some algorithms of ICA in brief, such as Fast-ICA, Kernel-ICA, MS –ICA, JADE, EGLD-ICA ,etc. The experimental results presented in the paper show that the SNR proposed here to indentify the various components with higher accuracy in the particular algorithm based on classifying biomedical data.