Abs trsc t An algorithm for differentiating ECGs with atriul fibrillation (A F) that will spontaneously terminate within 60 seconds from signals, where it won't, has been developed using the AF Termiliation Challenge Database from PhysioNet. The algorithm was based on the calculation of the major AF frequency by canceling out the QRS complexes and T waves from the original ECGs and then applying short time Fourier transform techniques to the remaining signals. The mujor AF frequency and the mean RR interval were considered for classification.Validation of the algorithm was done by sending the algorithm's results for test-set-u of the AF Termination Challenge Database to PhysiuNet. We found, that for ECGs with a low AF frequency it was more likely, that AF would terminate spontuneously than for ECGs with higher frequencies. Our algorithm was able to correctly classifv 93.3% (28/30) of the signals of the fest-set-a.

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Heart failure (HF) is a chronic disease with high hospitalizations rates and increased mortality. The six-minute walk test (6MWT) is a standardized method to assess the functional health of HF patients by measuring the distance walked in six minutes [1]. The improved 6MWT with new filter algorithms, based on the development of smartphone application [9], can be used everywhere with a good global navigation satellite system (GNSS) signal. This paper tests multiple filtering approaches to reduce the measured distance error in the application.We were able to decrease the average relative error of the application from 4.5% to 2.0%. Additionally, the number of experiments with relative error over our chosen clinically significant difference of 5% is reduced by 90%. Keeping these results in mind, the system can be considered clinically acceptable.

Background: Chronic low back pain is a global health problem having a tremendous effect on the quality of life of patients. Objectives: An online therapy management system (TMS) is developed for comprehensive management of chronic low back pain patients. Methods: A smartphone and a web app are built based on the Keep-In-Touch Telehealth Platform. The smartphone app allows entering patient reported outcomes and connection to third party devices to monitor physiological data and parameters of therapy. Results: The TMS has been realized and a wearable auricular vagus nerve stimulation device has been integrated. The TMS is currently evaluated in a randomized clinical trial.