No abstract

Discrimination of murmurs in heart sounds is accomplished by means of time-frequency representations (TFR) which help to deal with non-stationarity. Nevertheless, classification with TFR is not straightforward given their large dimension and redundancy. In this paper we compare several methodologies to apply Principal Component Analysis (PCA) to TFR as a dimensional reduction scheme, which differ in the form that features are represented. Besides, we propose a method which maximizes information among TFR preserving information within TFRs. Results show that the methodologies that represent TFRs as matrices improve discrimination of heart murmurs, and that the proposed methodology shrinks variability of the results.

A large proportion of cardiovascular diseases might be preventable, however, majority of this diseases occurs in rural areas where there is a poor presence of cardiologists. To overcome this issue, the use of wearable devices within the telemedicine framework would be of benefit. However, implementation of processing algorithms in smart-phones at mobile environments imposes restrictions ensuring high measurement quality of acquired ECG data, while maintaining low computation burden. This work presents an algorithm for scoring the quality of measured ECG recordings is developed. Particularly, a quality score is provided that takes into account the proportional correlation observed in acceptable signals based on a diversity scheme, and their inverse relation with standard deviation. Testing of proposed algorithm is carried out upon two different databases, the first one is of own production, while the second one is obtained from Physionet. As a result, high values of sensitivity and specificity are achieved.

Wearable sensor systems will soon become part of the available medical tools for remote and long term physiological monitoring. However, the set of variables involved in the performance of these systems are usually antagonistic, and therefore the design of usable wearable systems in real clinical applications entails a number of challenges that have to be addressed first. This paper describes a method to optimise the design of these systems for the specific application of cardiac monitoring. The method proposed is based on the selection of a subset of 5 design variables, sensor contact, location, and rotation, signal correlation, and patient comfort, and 2 objective functions, functionality and wearability. These variables are optimised using linear and nonlinear models to maximise those objective functions simultaneously. The methodology described and the results achieved demonstrate that it is possible to find an optimal solution and therefore overcome most of the design barriers that prevent wearable sensor systems from being used in normal clinical practice.

Wearable monitoring devices are a promising trend for ambulatory and real time biosignal processing, because they improve access and coverage by means of comfortable sensors, with real-time communication via mobile networks. In this paper, we present a garment for ambulatory electrocardiogram monitoring, a smart t-shirt with a textile electrode that conducts electricity and has a coating designed to preserve the user's hygiene, allowing long-term mobile measurements. Silicon dioxide nanoparticles were applied on the surface of the textile electrodes to preserve conductivity and impart superhydrophobic properties. A model to explain these results is proposed. The best result of this study is obtained when the contact angles between the fluid and the fabric exceeded 150°, while the electrical resistivity remained below 5 Ω·cm, allowing an acquisition of high quality electrocardiograms in moving patients. Thus, this tool represents an interesting alternative for medium and long-term measurements, preserving the textile feeling of clothing and working under motion conditions.