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Electrocardiogram (ECG) is used to assess the heart arrhythmia. Accurate detection of beats helps determine different types of arrhythmia which are relevant to diagnose heart disease. Automatic assessment of arrhythmia for patients is widely studied. This paper presents an ECG classification method for arrhythmic beat classification using RR interval. The methodology is based on discrete cosine transform (DCT) conversion of RR interval. The RR interval of the beat is extracted from the ECG and used as feature. DCT conversion of RR interval is applied and the beats are classified using random tree. Experiments were conducted using MIT-BIH arrhythmia database.

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Problem statement: With increasing bandwidth, digital medical image storage and transmission is a boon to patients and health professionals alike. Medical images are available instantly and avoid the need to carry the data physically. Popular imaging techniques extensively used in medicine include X-Ray, Magnetic Resonance Imaging (MRI), Ultrasound and Computed Tomography (CT). The images produced from the above techniques can be segregated into spatial regions with some regions more important for diagnosis compared to other regions. The region of interest for diagnosis is usually a small area compared to the whole image captured. Compression techniques play a very important role for fast and efficient transfer of medical images. Lossless compression techniques ensure no data loss but have the limitations of low compression rate. Lossy compression techniques on the other hand provide better compression ratios but the cost of wrong diagnosis is very high. In this study it is proposed to explore multiple compression techniques based on Region OF Interest (ROI). Approach: In this study a novel active contour method is proposed which is adaptive and marks the outer region of interest without edges. Based on the ROI, the active area of interest is compressed using lossless compression and the other areas compressed with lossy wavelet compression techniques. Results and Conclusion: Our proposed procedure was applied to different MRI images obtaining overall compression ratios of 70-80% without losing the originality in the ROI

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