Cardiac Myxoma, a Rare But Most Common Encountered Cardiac Tumor: A Single Center Experience

Introduction: Cardiac myxoma is a benign and rare tumor, which can present with a grim phenomenon if the presentation is late or the diagnosis and surgery are delayed. The purpose of this study was to share our institutional experience of cardiac myxoma. Material and Methods: This retrospective study was conducted to evaluate patients undergoing procedures at a single tertiary care centre for the treatment of cardiac myxoma during January, 2007 to December, 2017. Preoperative diagnosis was made by assessing clinical presentation and doing echocardiography. Complete tumor excision was performed, and all the patients were followed up for recurrence and complications. Results: A total of 45 cases of cardiac myxoma (13 males and 32 females) with the mean age of 37.5 years old (ranged between 16 and 60 years old) were operated over the period of 10 years. Cardiac myxoma constituted about 0.69% of all cardiac cases operated at our institute. Out of all the subjects, 41, 3, and 1 cases had left atrial, right atrial, and right ventricular involvements, respectively. Additionally, 43 patients (95%) survived the surgery, one recurrence was observed during the follow-up period. Conclusion: Cardiac myxoma is the most common cardiac tumor account for very small percentage of patients with heart disease. Early clinical suspicion and the use of imaging modalities are key to early diagnosis of this condition. Although these tumors have a risk for severe cardiac and systemic symptoms, referral to experienced centers for prompt surgical resection under cardiopulmonary bypass provides excellent early and long-term results

Development of a Methodology for Identification of Indian Musical Instruments

In this work, an attempt is made to develop a methodology for Identication of Indian Musical Instruments. Given a digital audio le with mono recording of an Indian Instrument, we identify the instrument played. The approach involves feature extraction from the signal based on Digital Signal Processing techniques. The spectral moments and pitch of the music signal are used as features. The features extracted from the training data are stored in a database for a learning system based on the k-Nearest Neighbor classier (k-NN). The k-NN method uses a priori information from the training data set to estimate posterior probabilities for an unknown data. We implement the same and test our approach for 4 Indian Instruments - Sitar, Sarod, Tabla and Bansuri. A total of 60 les consisting of 15 recordings of each of the 4 instruments were tested. The recognition was as high as 73.33% for the Tabla and as low as 26.67% for the Sitar