R. Shantha Selva Kumari

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Biomedical signal processing is the process of extracting clinically useful information from biosignals for the aspect of medical procedures. Biomedical signals like electrocardiogram wave commonly change their statistical properties over time tending to be nonstationary. For analyzing this kind of signal wavelet transforms are a powerful tool. The design(More)
The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity(More)
This paper deals with a wavelet based method is used for detecting an Myocardial Infarction (MI) along with user identity. The multilayer Electrocardiogram signals is decomposed using Daubechies wavelet transform which segments the ECG into different sub bands. The inversion of the ST segment, QRS complex and PQ changes normally occur in a abnormal signal.(More)