Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.

List of contents

Introduction .-  Background .- Algorithms .-  Point Anomaly Detection: Application to Freezing of Gait Monitoring .-  Collective Anomaly Detection: Application to Respiratory Artefact Removals.-  Spike Sorting: Application to Motor Unit Action Potential Discrimination .- Conclusion .

Summary

This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.

Product details

• Authors: Thuy T Pham, Thuy T. Pham
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 01.01.2018
• EAN: 9783319986746
• ISBN: 978-3-31-998674-6
• No. of pages: 107
• Dimensions: 167 mm x 13 mm x 241 mm
• Weight: 303 g
• Illustrations: XV, 107 p. 35 illus., 32 illus. in color.
• Series: Springer Theses; Springer Theses
• Subjects: Natural sciences, medicine, IT, technology > Technology > Miscellaneous; B, Künstliche Intelligenz, Data Mining, Artificial Intelligence, DV-gestützte Biologie/Bioinformatik, Wissensbasierte Systeme, Expertensysteme, molecular biology, bioinformatics, engineering, Data Mining and Knowledge Discovery, Information technology: general issues, Computational Intelligence, Biomedical Engineering and Bioengineering, Biomedical engineering, Expert systems / knowledge-based systems, Respiratory Artifact Detection, Unsupervised Spike Sorting, Forced Oscillation Measurements, Fog Detection Systems, Subject-independent Classifiers