Abstract
In extracorporeal blood circulation intensive care treatments, the occurrence of gas within the circulation is one known major hazard. This gas volume can cause severe harm to the patient like infarctions. Consequently, within risk assessment for these treatments gas bubbles are usually addressed by either constructive or signal based approaches. All signal-based approaches do have in common that they need a sufficient amount of data to be parameterized. These data can only be acquired in animal trials or laboratory experiments, as they could result in harm to patients. Hence, we designed a mock loop, which is automatically able to create annotated data of gas bubbles injected into an extracorporeal circulation. We were able to run this setup with a periodicity of 15 seconds, which results in 240 annotated measurements per hour. For the evaluation, we created 1095 bubbles of varying sizes (0.3 to 0.5 ml). The elaborated setup enables us to produce a great amount of annotated data, which is shown to be comparable to manually generated data in a convenient and fully automated manner.
© 2018 by Walter de Gruyter Berlin/Boston
