Abstract

Abstract

Abstract
This chapter presents a one-way method for synchronization at the media access control (MAC) layer of nodes and a circuit based on that in a wearable sensor network. The proposed approach minimizes the time skew with an accuracy of half of clock cycle in average. The work is intended to be used in a router integrated circuit (IC) designed for wearable systems. In particular, we address the need for good time synchronization in the simultaneous acquisition of surface electromyographic signals of several muscles. In our main application case, the electrodes are embedded in patient clothes connected to sensor nodes (SNs) equipped with analog-to-digital converters. The SNs are connected together in a network using conducting yarns embedded in the clothes. In the context of such wearable sensor networks, the main contributions of this work are the evaluation of existing protocols for synchronization, the description of a simpler, resource-efficient synchronization protocol, and its analysis, including the determination of the average local and global clock skew and of the synchronization probability in the presence of link failures. Both theoretical analysis and experimental results, in wired wearable networks, show that the proposed protocol has a better performance than precision time protocol (PTP), a standard timing protocol for both single and multihop situations. The proposed approach is simpler, requires no calculations, and exchanges fewer messages. Experimental results obtained with an implementation of the protocol in 0.35 µm complementary metal oxide semiconductor (CMOS) technology show that this approach keeps the one-hop average clock skew around 4.6 ns and peak-to-peak skew around 50 ns for a system clock frequency of 20 MHz. © The Institution of Engineering and Technology 2017.

Abstract

Abstract
Background: Blended treatments, combining digital components with face-to-face (FTF) therapy, are starting to find their way into mental health care. Knowledge on how blended treatments should be set up is, however, still limited. To further explore and optimize blended treatment protocols, it is important to obtain a full picture of what actually happens during treatments when applied in routine mental health care. Objective: The aims of this study were to gain insight into the usage of the different components of a blended cognitive behavioral therapy (bCBT) for depression and reflect on actual engagement as compared with intended application, compare bCBT usage between primary and specialized care, and explore different usage patterns. Methods: Data used were collected from participants of the European Comparative Effectiveness Research on Internet-Based Depression Treatment project, a European multisite randomized controlled trial comparing bCBT with regular care for depression. Patients were recruited in primary and specialized routine mental health care settings between February 2015 and December 2017. Analyses were performed on the group of participants allocated to the bCBT condition who made use of the Moodbuster platform and for whom data from all blended components were available (n=200). Included patients were from Germany, Poland, the Netherlands, and France; 64.5% (129/200) were female and the average age was 42 years (range 18-74 years). Results: Overall, there was a large variability in the usage of the blended treatment. A clear distinction between care settings was observed, with longer treatment duration and more FTF sessions in specialized care and a more active and intensive usage of the Web-based component by the patients in primary care. Of the patients who started the bCBT, 89.5% (179/200) also continued with this treatment format. Treatment preference, educational level, and the number of comorbid disorders were associated with bCBT engagement. Conclusions: Blended treatments can be applied to a group of patients being treated for depression in routine mental health care. Rather than striving for an optimal blend, a more personalized blended care approach seems to be the most suitable. The next step is to gain more insight into the clinical and cost-effectiveness of blended treatments and to further facilitate uptake in routine mental health care.

Abstract
Ovarian cancer is one of the most commonly occurring cancer in women. Transvaginal ultrasound is used as a screening test to detect the presence of tumors but, for specific types of ovarian tumors, malignancy can only be asserted through surgery. An automatic method to perform the detection and malignancy assessment of these tumours is thus necessary to prevent unnecessary oophorectomies. This work explores the U-Net's architecture and investigates the selection of different hyperparameters for the ovary and the ovarian follicles segmentation. The effect of applying different post-processing methods on beam-formed radio-frequency (BRF) data is also investigated. Results show that models trained only with BRF data have the worst performance. On the other hand, the combination of B-mode with BRF data performs better for ovary segmentation. As for the hyperparameter study, results show that the U-Net with 4 levels is the architecture with the worst performance. This shows that to achieve better performance in the segmentation of ovarian structures, it is important to select an architecture that takes into account the spatial context of the regions of interest. It is also possible to conclude that the method used to analyse BRF data should be designed to take advantage of the fine-resolution of BRF data.