Along with the advancing of technology in wireless and miniature camera, Wireless Capsule Endoscopy (WCE), the
combination of both, enables a physician to diagnose patient's digestive system without actually perform a surgical
procedure. Although WCE is a technical breakthrough that allows physicians to visualize the entire small bowel
noninvasively, the video viewing time takes 1 - 2 hours. This is very time consuming for the gastroenterologist. Not
only it sets a limit on the wide application of this technology but also it incurs considerable amount of cost. Therefore, it
is important to automate such process so that the medical clinicians only focus on interested events. As an extension
from our previous work that characterizes the motility of digestive tract in WCE videos, we propose a new assessment
system for energy based events detection (EG-EBD) to classify the events in WCE videos. For the system, we first
extract general features of a WCE video that can characterize the intestinal contractions in digestive organs. Then, the
event boundaries are identified by using High Frequency Content (HFC) function. The segments are classified into WCE
event by special features. In this system, we focus on entering duodenum, entering cecum, and active bleeding. This
assessment system can be easily extended to discover more WCE events, such as detailed organ segmentation and more
diseases, by using new special features. In addition, the system provides a score for every WCE image for each event.
Using the event scores, the system helps a specialist to speedup the diagnosis process.