Automatische Feature-Erzeugung und -Auswahl bei Hyperspectral Imaging-Anwendungen

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersDiese Diplomarbeit präsentiert AutoFeature, einen neuen Algorithmus, der materialspezifische spektroskopische Charakteristika aus annotierten Infrarotspektroskopie-Daten völlig automatisch zu extrahieren vermag. Mithilfe dieser Charakteristika können anschließend die jeweiligen Materialien in hyperspektralen Bildern identifiziert werden. Eine Expertise in spektroskopischen Eigenschaften der Materialien ist demnach für den Anwender nicht nötig. Der AutoFeature Algorithmus generiert einerseits tausende Features mittels Template Matching und wählt andererseits, basierend auf statistischen Methoden und maschinellem Lernen, die vielversprechendsten Features aus. Für das Template Matching wurden vier Arten von Templates konzipiert: Dreiecke, Gauß’sche Glockenkurven, allgemeine Gauß’sche Glockenkurven und Geraden. Das Template Matching erfolgt an allen Positionen des Infrarotspektrums und beruht auf dem Pearson Korrelationskoeffizienten. Die anschließende Auswahl der relevanten Features erfolgt methodisch entweder durch Fast Function Extraction, Embedded Random Forest Modelling oder durch eine der drei Filtermethoden ReliefF, Fisher Score und HSIC Lasso. Die Studie untersucht zunächst das Verhalten des AutoFeature Algorithmus hinsichtlich Datensatzgröße und Rauschen mithilfe künstlicher Daten. Anschließend werden Features aus drei realen Datensätzen aus Mikroplastik- und Hautgewebeproben automatisch extrahiert. Diese werden für das Erstellen von Random Forest Modellen verwendet, anhand derer im ersten Experiment fünf Polymere, im zweiten Experiment Melanoma und Nicht-Melanoma und im dritten Experiment Bindegewebe und Nicht-Bindegewebe klassifiziert werden. Bei den künstlichen Datensätzen mit Samplegröße 16 konnte der Algorithmus die korrekten Features bis zu einem Rauschniveau von 10% erkennen, bei Samplegröße 100 bis zu einem Rauschniveau von 25%. Für reale Daten wurden Features aller vier Templates extrahiert, die sich ausschließlich in charakteristischen Absorptionsbändern befinden. Die genauen Positionen und Breiten mancher Features fallen dennoch unerwartet aus. Die Validierung der Random Forest Modelle mit Testdaten resultierte in einer Klassifikationsgenauigkeit von mindestens 99.6% im Fall der Polymere und in perfekten Klassifikationen bei den Melanoma- und Bindegewebsdaten. Mittels unterschiedlicher Selektionsmethoden wurden Features mit variablen Dichteeigenschaften ausgewählt, die jedoch alle eine überzeugende Unterscheidbarkeit der Klassen aufweisen. Insgesamt konnten mithilfe des AutoFeature Algorithmus sowohl bei künstlichen als auch bei realen Daten Features automatisch extrahiert werden, die nicht nur chemisch sinnvoll, sondern auch für Klassifikationen geeignet sind. Um das Potential des AutoFeature Algorithmus festzustellen, bedarf es weiterer Untersuchungen mit vielfältigeren Datensätzen. Durch das Erstellen zusätzlicher Templates und die Anpassung der Selektionsparameter ist eine algorithmische Weiterentwicklung möglich.This master’s thesis presents Autofeature, a novel algorithm that enables the automatic extraction of material specific spectroscopic characteristics from an annotated infrared spectroscopy dataset. With these characteristics the material can then be identified in hyperspectral images. Accordingly, no expertise of the user in the spectroscopic properties of the material is necessary. On the one hand, the AutoFeature algorithm generates thousands of features based on template matching and on the other hand, selects the most promising features based on statistical and machine learning methods. Four types of templates are designed: triangles, Gaussian bells, general Gaussian bells and straight lines. The matching is performed at all possible infrared spectrum positions by employing the Pearson correlation coefficient. The subsequent feature selection is carried out with fast function extraction, embedded random forest modelling or with one of the following three filter selection methods ReliefF, Fisher score and HSIC lasso. The study first investigates the properties of the AutoFeature algorithm concerning sample size and noise. Next, features are automatically extracted from three real-world data sets containing microplastic and skin tissue specimens. These features are then used to train random forest classification models for class predictions of five polymers in the first experiment, melanoma and non-melanoma in the second experiment, and connective tissue and non-connective tissue in the third experiment. For artificial data, the algorithm was able to extract correct features for noise levels of 10% for a sample size of 16 respectively 25% for sample size 100. For real-world data, features of all four types are extracted and the features are only located at characteristic absorption bands of the substances being investigated. The exact positions and widths of some features are unexpected though. The validation of the random forest models with unseen test data yielded classification accuracies of 99.6% or higher for the polymer predictions and a perfect classification for the melanoma and connective tissue predictions. While the different selection methods result in features with different probability density functions, they all yield features with convincing class discrimination properties. Overall, the AutoFeature algorithm was able to automatically extract features that were chemically meaningful and suited for prediction tasks for both artificial and real-world data. To evaluate further potential of the algorithm, examinations with datasets of greater variety need to be performed. We believe, by designing additionaltemplates and adapting parameters of the selection methods, further algorithmic progress can be made.8

Algorithm for automatic detection of self-similarity and prediction of residual central respiratory events during continuous positive airway pressure

STUDY OBJECTIVES: Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies "expressed/manifest" HLG via a cyclical self-similarity feature in effort-based respiration signals. METHODS: Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 10. Central apnea labels are obtained both from manual scoring by sleep technologists and from an automated algorithm developed for this study. The Massachusetts General Hospital sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. RESULTS: Diagnostic CAI based on technologist labels predicted REC with an area under the curve (AUC) of 0.82 ± 0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ± 0.02. A subanalysis was performed on a population with technologist-labeled diagnostic CAI higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels. CONCLUSIONS: The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC

Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks

Objective: Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning. Methods: Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings. Results: For binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.417.8 and a r<sup>2</sup> of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas. Conclusion: Our fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation. Significance: The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations

Cortical responses to noninvasive perturbations enable individual brain fingerprinting

In recent years, it has become increasingly apparent that characterizing individual brain structure, connectivity and dynamics is essential for understanding brain function in health and disease. However, the majority of neuroimaging and brain stimulation research has characterized human brain function by averaging measurements from groups of subjects and providing population-level inferences. External perturbations applied directly to well-defined brain regions can reveal distinctive information about the state, connectivity and dynamics of the human brain at the individual level. In a series of studies, we aimed to characterize individual brain responses to MRI-guided transcranial magnetic stimulation (TMS), and explore the reproducibility of the evoked effects, differences between brain regions, and their individual specificity. In the first study, we administered single pulses of TMS to both anatomically (left dorsolateral prefrontal cortex- 'L-DLPFC', left Intra-parietal lobule- 'L-IPL) and functionally (left motor cortex- 'L-M1', right default mode network- 'R-DMN, right dorsal attention network- 'R-DAN') defined cortical nodes in the frontal, motor, and parietal regions across two identical sessions spaced one month apart in 24 healthy volunteers. In the second study, we extended our analyses to two independent data sets (n = 10 in both data sets) having different sham-TMS protocols. In the first study, we found that perturbation-induced cortical propagation patterns are heterogeneous across individuals but highly reproducible within individuals, specific to the stimulated region, and distinct from spontaneous activity. Most importantly, we demonstrate that by assessing the spatiotemporal characteristics of TMS-induced brain responses originating from different cortical regions, individual subjects can be identified with perfect accuracy. In the second study, we demonstrated that subject specificity of TEPs is generalizable across independent data sets and distinct from non-transcranial neural responses evoked by sham-TMS protocols. Perturbation-induced brain responses reveal unique "brain fingerprints" that reflect causal connectivity dynamics of the stimulated brain regions, and may serve as reliable biomarkers of individual brain function

Optimal spindle detection parameters for predicting cognitive performance

STUDY OBJECTIVES: Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition. METHODS: Adult patients (n = 167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores. RESULTS: Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r = 0.503) and age-adjusted fluid cognition scores (r = 0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings. CONCLUSIONS: Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition