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Measurements of ultrasound diaphragmatic motion, amplitude, force, and velocity of contraction may provide important and essential information about diaphragmatic fatigue, weakness, or paralysis. In this paper, we propose and evaluate a semi-automated analysis system for measuring the diaphragmatic motion and estimating the maximum relaxation rate (MRR_SAUS) from ultrasound M-mode images of the diaphragmatic muscle. The system was evaluated on 27 M-mode ultrasound images of the diaphragmatic muscle [20 with no resistance (NRES) and 7 with resistance (RES)]. We computed semi-automated ultrasound MRR measurements on all NRES/RES images, using the proposed system (MRR_SAUS = 3.94 ± 0.91/4.98 ± 1.98 [1/s]), and compared them with the manual measurements made by a clinical expert (MRR_MUS = 2.36 ± 1.19/5.8 ± 2.1 [1/s],) and those made by a reference manual method (MRR_MB = 3.93 ± 0.89/3.73 ± 0.52 [1/sec], performed manually with the Biopac system. MRR_SAUS and MRR_MB measurements were not statistically significantly different for NRES and RES subjects but were significantly different with the MRR-MUS measurements made by the clinical expert. It is anticipated that the proposed system might be used in the future in the clinical practice in the assessment and follow up of patients with diaphragmatic weakness or paralysis. It may thus potentially help to understand post-operative pulmonary dysfunction or weaning failure from mechanical ventilation. Further validation and additional experimentation in a larger sample of images and different patient groups is required for further validating the proposed system.

This paper introduces a novel, open-source middleware framework for communication with medical devices and an application using the middleware named intensive care window (ICW). The middleware enables communication with intensive care unit bedside-installed medical devices over standard and proprietary communication protocol stacks. The ICW application facilitates the acquisition of vital signs and physiological parameters exported from patient-attached medical devices and sensors. Moreover, ICW provides runtime and post-analysis procedures for data annotation, data visualization, data query, and analysis. The ICW application can be deployed as a stand-alone solution or in conjunction with existing clinical information systems providing a holistic solution to inpatient medical condition monitoring, early diagnosis, and prognosis.

Modern e-Health systems require advanced computing and storage capabilities, leading to the adoption of technologies like the grid and giving birth to novel health grid systems. In particular, intensive care medicine uses this paradigm when facing a high flow of data coming from intensive care unit's (ICU) inpatients just like demonstrated by the ICGrid system prototyped by the University of Cyprus. Unfortunately, moving an ICU patient's data from the traditionally isolated hospital's computing facilities to data grids via public networks (i.e., the Internet) makes it imperative to establish an integral and standardized security solution to avoid common attacks on the data and metadata being managed. Particular emphasis must be put on the patient's personal data, the protection of which is required by legislations in many countries of the European Union and the world in general. In this paper, we extend our previous research with the following contributions: 1) a mandatory access control model to protect patient's metadata; 2) a major security revision to our previously proposed privacy protocol by contributing with a "quality of security" quantitative metric to improve fragmented data's assurance; and finally, 3) a set of early results to demonstrate that our protocol not only improves a patient personal data's security and privacy but also achieves a performance comparable with existing approaches.