Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
Springer International Publishing (Verlag)
978-3-319-98674-6 (ISBN)
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.
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 .
| Erscheinungsdatum | 02.09.2018 |
|---|---|
| Reihe/Serie | Springer Theses |
| Zusatzinfo | XV, 107 p. 35 illus., 32 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 356 g |
| Themenwelt | Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie |
| Technik | |
| Schlagworte | Anomaly Detection for Biomedical Data • Anomaly Score Based Detector • Automated Feature Selection • Feature Selection Based on Voting • Fog Detection Systems • Forced Oscillation Measurements • Improving Classification Performance • Learning for Detecting Freezing of Gait Events • Novelty detection • Respiratory Artifact Detection • Subject-independent Classifiers • Unsupervised Anomaly Detection • Unsupervised Artifact Detection • Unsupervised Classification of Biomedical Data • Unsupervised Multi-class Sorting • Unsupervised Spike Sorting • Voting Process for Feature Selection |
| ISBN-10 | 3-319-98674-0 / 3319986740 |
| ISBN-13 | 978-3-319-98674-6 / 9783319986746 |
| Zustand | Neuware |
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