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Stream-based Biomedical Classification Algorithms for Analyzing Biosignals
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 Title & Authors
Stream-based Biomedical Classification Algorithms for Analyzing Biosignals
Fong, Simon; Hang, Yang; Mohammed, Sabah; Fiaidhi, Jinan;
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 Abstract
Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.
 Keywords
Data Stream Mining;VFDT;OVFDT;C4.5 and Biomedical Domain;
 Language
English
 Cited by
1.
Feature Selection in Life Science Classification: Metaheuristic Swarm Search, IT Professional, 2014, 16, 4, 24  crossref(new windwow)
2.
Incremental Optimization Mechanism for Constructing a Decision Tree in Data Stream Mining, Mathematical Problems in Engineering, 2013, 2013, 1  crossref(new windwow)
3.
Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy, BioMed Research International, 2013, 2013, 1  crossref(new windwow)
4.
Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis, International Journal of Distributed Sensor Networks, 2014, 2014, 1  crossref(new windwow)
5.
A time series pre-processing methodology with statistical and spectral analysis for classifying non-stationary stochastic biosignals, The Journal of Supercomputing, 2016, 72, 10, 3887  crossref(new windwow)
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