No abstract

No abstract

No abstract

No abstract

This paper presents an efficient method for the detection and delineation of P and T waves in 12-lead electrocardiograms (ECGs) using a support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander. An SVM is used as a classifier for the detection and delineation of P and T waves. The performance of the algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE (Common Standards for Quantitative Electrocardiography) ECG multi-lead measurement library. A significant detection rate of 95.43% is achieved for P wave detection and 96.89% for T wave detection. Delineation performance of the algorithm is validated by calculating the mean and standard deviation of the differences between automatic and manual annotations by the referee cardiologists. The proposed method not only detects all kinds of morphologies of QRS complexes, P and T waves but also delineates them accurately. The onsets and offsets of the detected P and T waves are found to be within the tolerance limits given in the CSE library.

No abstract

Feature extraction is one of the most important step in CAD (Computer Assisted Diagnosis) system. It helps CAD system to take correct decision and increase its accuracy by providing distinguish feature of malignant and benign tumor. Computer based system is proposed in this paper for feature extraction of lung nodule from the X-ray image. In recent years, the image processing mechanisms are widely used in several medical areas for early detection and in deciding treatment stages, where the time and cost factor is very important to discover the disease in the patient. Among the cancer, lung cancer is one of the most common causes of death worldwide. Therefore, early detection using diagnostic tests promises to reduce mortality from lung cancer. Present paper deals with the problem of developing a computer based system for the extraction of maximum features from the segmented suspicious area from the lung X-ray image. Further, these properties can be used to classify lung tumor as benign or malignant from the X-ray image directly. Calculated features will help the CAD system to take correct decision.

No abstract