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Electrophysiological devices are connected to the body through electrodes. In some applications, such as nerve stimulation, it is needed to minimally pierce the skin and reach the underneath layers to bypass the impedance of the first layer called stratum corneum. In this study, we have designed and fabricated surface microneedle electrodes for applications such as electrical peripheral nerve stimulation. We used molybdenum for microneedle fabrication, which is a biocompatible metal; it was used for the conductive layer of the needle array. To evaluate the performance of the fabricated electrodes, they were compared with the conventional surface electrodes in nerve conduction velocity experiment. The recorded signals showed a much lower contact resistance and higher bandwidth in low frequencies for the fabricated microneedle electrodes compared to those of the conventional electrodes. These results indicate the electrode-tissue interface capacitance and charge transfer resistance have been increased in our designed electrodes, while the contact resistance decreased. These changes will lead to less harmful Faradaic current passing through the tissue during stimulation in different frequencies. We also compared the designed microneedle electrodes with conventional ones by a 3-dimensional finite element simulation. The results demonstrated that the current density in the deep layers of the skin and the directivity toward a target nerve for microneedle electrodes were much more than those for the conventional ones. Therefore, the designed electrodes are much more efficient than the conventional electrodes for superficial transcutaneous nerve stimulation purposes.

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Sleep stage detection is needed in many sleep studies and clinical assessments. Generally, sleep stages are identified using spectral analysis of electrocephologram (EEG) and electrooculogram (EOG) signals. This study, for the first time, has investigated the feasibility of detecting sleep stages using tracheal breathing sounds, and whether the change of breathing sounds due to sleeping stage differs at different periods of sleeping time; the motivation was seeking an alternative technique for sleep stage identification. The tracheal breathing sounds of 12 individuals, who were referred for full overnight polysomnography (PSG) assessment, were recorded using a microphone placed over the suprasternal notch, and analyzed using higher order statistical analysis. Five noise-and-snore-free breathing cycles from wakefulness, REM and Stage II of sleep were selected from each subject for analysis. Data of the REM and Stage II were selected from beginning, middle and close to end of sleeping time. Hurst exponent was calculated from the bispectra of the inspiratory sounds of each subject at each sleeping stage in different periods of sleeping time. The participants' sleep stage were determined by sleep lab technologists during the PSG study using EEG and EOG signals. The results show separate and non-overlapping clusters for wakefulness, REM and Stage II for each subject. Thus, using a simple linear classifier, we were able to classify REM and Stage II of each subject with 100% accuracy. In addition, the results show that the same pattern existed as long as the REM and Stage II segments were close (less than 3 h) to each other in terms of time.

The main part of each white blood cell (WBC) is its nucleus which contains chromosomes. Although white blood cells (WBCs) with giant nuclei are the main symptom of leukemia, they are not sufficient to prove this disease and other symptoms must be investigated. For example another important symptom of leukemia is the existence of nucleolus in nucleus. The nucleus contains chromatin and a structure called the nucleolus. Chromatin is DNA in its active form while nucleolus is composed of protein and RNA, which are usually inactive. In this paper, to diagnose this symptom and in order to discriminate between nucleoli and chromatins, we employ curvelet transform, which is a multiresolution transform for detecting 2D singularities in images. For this reason, at first nuclei are extracted by means of K-means method, then curvelet transform is applied on extracted nuclei and the coefficients are modified, and finally reconstructed image is used to extract the candidate locations of chromatins and nucleoli. This method is applied on 100 microscopic images and succeeds with specificity of 80.2% and sensitivity of 84.3% to detect the nucleolus candidate zone. After nucleolus candidate zone detection, new features that can be used to classify atypical and blast cells such as gradient of saturation channel are extracted.