Lukas Brausch

• In one-dimensional (1-D) Ultrasound (US) measurements, signals are acquired that form the basis of more sophisticated two-dimensional (2-D) or three-dimensional (3-D) US imaging. These 1-D signals contain a lot of raw information about the US wave propagation and interaction with the medium that is only processed in parts during image generation. While image representations are easy to interpret for humans, the analysis of US wave signals is hard to perform without applying algorithms to extract desired features. This work investigates reliable and fast 1-D US signal classifications to distinguish between different stages or states in biomedical US scenarios and shows how the new field of Machine Learning (ML) on raw US wave data provides advantages and different applications. To achieve good results, the input signals are treated as time series, which requires the deployment of comparatively complex Time Series Classification (TSC) algorithms. The literature shows that a lot of research efforts have previously only tackled the classification and segmentation of US Brightness mode (B-Mode) images, while neglecting approaches to classify 1-D signals to a large extent. This research contributes by developing, deploying and evaluating classification approaches for three distinct biomedical US classification tasks and finds that respective signal classifications for different scenarios are possible with varying degrees of accuracies. It entails the comparison of several combinations of data types (e.g. temporal, spectral and statistical features or raw signals), ML models and pre-processing steps to provide a strong foundation for robust, binary classifications of 1-D US signals for scenarios based on low-cost wearable, mobile and stationary devices. This research addresses scientific questions not answered before by informing on detailed descriptions of beneficial domain specific knowledge (domain specific knowledge (DSK)), achieved accuracies and times needed for training and evaluation of the examined ML models. The resulting ML pipelines includes solutions based on data acquired from custom experimental setups or clinical trials. Possible real-world applications might include muscle contraction trackers, muscle fatigue detectors, epiphyseal radius bone closure detectors or devices providing information about advanced liver disease stages. Automated machine-assisted classifications requiring as little DSK as possible from the end user enable application scenarios ranging from fitness or rehabilitation trackers as consumer devices to solutions providing diagnostic support without requiring extensive knowledge from professional medical practitioners. For example, decision support systems for bone age assessments in clinical use or liver health assessment systems for gastroenterologists. This work shows that reliable, robust and fast classifications based on 1-D US signals are possible with high degrees of accuracies depending on the examined scenario with achieved F 1 -scores ranging from ≈ 70% to ≈ 87%. These results prove that real-life applications for recreational purposes are already possible and that critical applications for clinical use are highly likely to be achieved once the presented approaches are further optimized in the future.