A Methodology for Clustering Transient Biomedical Signals by Variable

Biomedical signals which help monitor patients’ physical conditions are a crucial part of the healthcare industry. The healthcare professionals’ ability to monitor patients and detect early signs of conditions such as blocked arteries and abnormal heart rhythms can be accomplished by performing data clustering on biomedical signals. More importantly, clustering on streams of biomedical signals make it possible to look for patterns that may indicate developing conditions. While there are a number of clustering algorithms that perform data streams clustering by example, few algorithms exist that perform clustering by variable. This paper presents POD-Clus, a clustering method which uses a model-based clustering principle and, in addition to clustering by example, also cluster data streams by variable. The clustering result from POD-Clus was superior to the result from ODAC, a baseline algorithm, for both with and without cluster evolutions. DOI: 10.4018/jcmam.2012010103 International Journal of Computational Models and Algorithms in Medicine, 3(1), 32-71, January-March 2012 33 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. leading to a total of 1.2 million data points. This is an enormous amount of data collected within a short period of time. As various heart conditions can be revealed from the analysis of heartbeats. For example, a heart-related condition such as blocked blood supply will generate tissue death and reflect in the abnormal height of heartbeat waves. Therefore, analyzing these large amounts of data in a timely manner for a quick diagnosis is challenging, as the data may become too large to either deliver over a network (Sun & Sclabassi, 1999) or store in the main memory of the device. For this reason, data streams processing needs to happen real-time while data streams arrive. As various types of biomedical signals can be considered data streams, there exists a need for effective data streams mining techniques that can handle such data streams efficiently. Data streams’ characteristics (Domingos & Hulten, 2000; Gama, Rodrigues, & Aguilar-Ruiz, 2007) can be described as follows: • Data from the streams usually come in at a detailed level, e.g., 1000 Hz. • Streaming data arrives at a fast pace, therefore agile data management and utilization is key. • Observations of data are potentially unbounded. • Storage and memory resources for processing data streams are possibly limited. Data streams clustering can be incorporated into a computer-aided analysis used by physicians to cluster biomedical signals for diagnosis on the patients. By grouping biomedical signals into homogeneous clusters, we learn about data characteristics which may indicate developing conditions. Results from clustering can then be developed into classification models or predictive models useful in healthcare diagnoses. As Chaovalit (2010) has proposed the POD-Clus algorithm (Probability and Distribution-based Clustering) for data streams clustering, the algorithm was for only its ability to cluster data streams by example. This paper focuses on the ability of POD-Clus to cluster by variable. Clustering by variable is another clustering perspective for data streams. POD-Cus shows a significant improvement on clustering results compared to the baseline competing algorithm from the same category.