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

1. Auflage 2019
ISBN: 978-3-319-98675-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 107 Seiten, eBook

Reihe: Springer Theses

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.

Zielgruppe

Research

Autoren/Hrsg.

Pham, Thuy T.

Inhaltsverzeichnis

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 .