Abstract-Sleep spindles are the hallmark of N2 stage of sleep. They are transient waveforms observed on sleep electroencephalogram and their identification is required for sleep staging. Due to the large number of sleep spindles appearing on an overnight sleep EEG, automating the detection of sleep spindles would be desirable, not only to save specialist time but also for fully automated sleep staging systems. A simple algorithm for automatic sleep spindle detection is presented in this paper using only one channel of EEG input. This algorithm uses Teager energy and spectral edge frequency to mark sleep spindles and results in a sensitivity of 80% and specificity of about 98%. It is also shown that more than 91% of spindles detected by the algorithm were in N2 and N3 stages combined.
Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automatically processed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patient movement, sensor placement and interference from other sources. Owing to the large volume of data these artefacts need to be automatically identified so that the analysis systems and doctors are aware of them while making medical diagnosis. Three important factors are explored that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality, interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm, whereas the third is particularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filtering algorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstrate how different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.
The use of inductors operating in the radio frequency region of 10 MHz to 1 GHz for detection of a change in concentration of ionic species present in a liquid sample is reported here. Liquid samples under constant ionic strength and varying pH along with constant pH and varying ionic strength were used for testing purposes. Y-parameters were extracted and used for data analysis. A change of 15.3 MHz in the resonant frequency of the inductor under varying pH and a change of 20MHz under the constant pH condition was obtained.The results from this study show that inductors can be employed in designing simple, scalable and integrable sensing systems to perform label free analysis without the need for surface activation or perturbing the system under study. This design has the potential in being used for certain applications like monitoring DNA hybridization or gastric juice.
The use of closely coupled inductors, operating in the region of 10 MHz to 1 GHz, for detection of change in the concentration of ionic species present in a liquid sample, is reported here. The S-parameters were measured for the coupled inductors with liquid samples under varying pH conditions on one inductor only. S12 parameters were used as an indicator for the coupling between the two inductors. The results from this study show that coupled coil systems can be employed in simple sensing systems to monitor electrolyte solutions without the need for direct physical contact to the sensor itself. This design has the potential of being used for applications such as monitoring DNA hybridization or monitoring the conductance of electrolyte solutions with high conductivity while simplifying packaging steps required.
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