PAF (paroxysmal atrial fibrillation) is a dangerous form of cardiac arrhythmia that poses severe health risks, sometimes leading to heart attacks, the recognized number-one killer in the developed world. The technical challenges for detecting and predicting PAF include accurate sensing, speedy analysis, and a workable classification system. To address these issues, electrocardiogram (ECG) data from the PhysioNet Online Database will be analyzed using new spectrum estimation techniques to develop a program able to predict, as well as recognize, the onset of specific cardiac arrhythmias such as PAF. The system could then be incorporated into wearable/mobile medical devices, allowing for interventions before cardiac episodes occur, and potentially saving many lives.