Biomedizinische Technik

Blood perfusion is the supply of tissue with blood, and oxygen is a key factor in the field of minor and major wound healing. Reduced perfusion of a wound bed or transplant often causes various complications. Reliable methods for an objective evaluation of perfusion status are still lacking, and insufficient perfusion may remain undiscovered, resulting in chronic processes and failing transplants. Hyperspectral imaging (HSI) represents a novel method with increasing importance for clinical practice. Therefore, methods, software and algorithms for a new HSI system are presented which can be used to observe tissue oxygenation and other parameters that are of importance in supervising healing processes. This could offer an improved insight into wound perfusion allowing timely intervention.

Pay-load deliveries across the skin barrier to the systemic circulation have been one of the most challenging delivery options. Necessitated requirements of the skin and facilitated skin layer cross-over delivery attempts have resulted in development of different non-invasive, non-oral methods, devices and systems which have been standardized, concurrently used and are in continuous upgrade and improvements. Iontophoresis, electroporation, sonophoresis, magnetophoresis, dermal patches, nanocarriers, needled and needle-less shots, and injectors are among some of the methods of transdermal delivery. The current review covers the current state of the art, merits and shortcomings of the systems, devices and transdermal delivery patches, including drugs’ and other payloads’ passage facilitation techniques, permeation and absorption feasibility studies, as well as physicochemical properties affecting the delivery through different transdermal modes along with examples of drugs, vaccines, genes and other payloads.

The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5±3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24±16.71%, BPI: 65.93±24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65±11.28, BPI: 76.03±18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.

Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32±12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios.