Over the last century, physiological signals have been broadly analyzed and processed not only to assess the function of the human physiology, but also to better diagnose illnesses or injuries and provide treatment options for patients. In particular, Electrocardiogram (ECG), blood pressure (BP) and impedance are among the most important biomedical signals processed and analyzed. The majority of studies that utilize these signals attempt to diagnose important irregularities such as arrhythmia or blood loss by processing one of these signals. However, the relationship between them is not yet fully studied using computational methods. Therefore, a system that extract and combine features from all physiological signals representative of states such as arrhythmia and loss of blood volume to predict the presence and the severity of such complications is of paramount importance for care givers. This will not only enhance diagnostic methods, but also enable physicians to make more accurate decisions; thereby the overall quality of care provided to patients will improve significantly. In the first part of the dissertation, analysis and processing of ECG signal to detect the most important waves i.e. P, QRS, and T, are described. A wavelet-based method is implemented to facilitate and enhance the detection process. The method not only provides high detection accuracy, but also efficient in regards to memory and execution time. In addition, the method is robust against noise and baseline drift, as supported by the results. The second part outlines a method that extract features from ECG signal in order to classify and predict the severity of arrhythmia. Arrhythmia can be life-threatening or benign. Several methods exist to detect abnormal heartbeats. However, a clear criterion to identify whether the detected arrhythmia is malignant or benign still an open problem. The method discussed in this dissertation will address a novel solution to this important issue. In the third part, a classification model that predicts the severity of loss of blood volume by incorporating multiple physiological signals is elaborated. The features are extracted in time and frequency domains after transforming the signals with Wavelet Transformation (WT). The results support the desirable reliability and accuracy of the system.

No description available.

Bike fitting can have a major impact on the performance of cyclists and can reduce the risk of non-traumatic injuries. This study shows significant changes in lower body biomechanics of road cyclists during and after fatigue and therefore expands the research from a more practical view. These findings support the expansion of future research using sensor-based analyses of road cycling (e.g., IMUs, oxygen saturation). / Die Einstellung des Fahrrads kann einen großen Einfluss auf die Leistung von Radfahrern haben und das Risiko nichttraumatischer Verletzungen verringern. Diese Studie zeigt signifikante Veränderungen in der Biomechanik des Unterkörpers von Rennradfahrern während und nach der Ermüdung und erweitert damit die Forschung aus einer eher praktischen Sicht. Diese Ergebnisse unterstützen die Ausweitung zukünftiger Forschung unter Verwendung sensorbasierter Analysen des Straßenradsports (z. B. IMUs, Sauerstoffsättigung).