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

1. Auflage 2019
ISBN: 978-3-319-98674-6
Verlag: Springer International Publishing

Buch, Englisch, 107 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 360 g

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.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

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.