Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects' electrogastrogram

Electrogastrography (EGG) is a noninvasive way to record gastric electrical activity of stomach muscle by placing electrodes on the abdominal skin. Our goal was to investigate the frequency of abnormalities of the EGG in real clinical diabetic gastroparesis patients using WT method and to compare performance of STIFT and WT methods in the case of time-frequency resolution. The results showed that WT sonograms can be used to classify patients successfully as healthy or sick. And also, due to the fact that the WT method does not suffer from some intrinsic problems that affect the STFT method, one can see that the WT method can help improve the quality of the sonogram of the EGG signals. (c) 2005 Elsevier Ltd. All rights reserved

Influence of Pre-Orthodontic Trainer treatment on the perioral and masticatory muscles in patients with Class II division 1 malocclusion

The aim of this follow-up study was to evaluate the effects of Pre-Orthodontic Trainer (POT) appliance on the anterior temporal, mental, orbicularis oris, and masseter muscles through electromyography (EMG) evaluations in subjects with Class II division 1 malocclusion and incompetent lips. Twenty patients (mean age: 9.8 +/- 2.2 years) with a Class II division 1 malocclusion were treated with POT (Myofunctional Research Co., Queensland, Australia). A group of 15 subjects (mean age: 9.2 +/- 0.9 years) with untreated Class II division 1 malocclusions was used as a control. EMG recordings of treatment group were taken at the beginning and at the end of the POT therapy (mean treatment period: 7.43 +/- 1.06 months). Follow-up records of the control group were taken after 8 months of the first records. Recordings were taken during different oral functions: clenching, sucking, and swallowing. Statistical analyses were undertaken with Wilcoxon and Mann-Whitney U-tests. During the POT treatment, activity of anterior temporal, mental, and masseter muscles was decreased and orbicularis oris activity was increased during clenching and these differences were found statistically significant when compared to control. Orbicularis oris activity during sucking was increased in the treatment group (P < 0.05). In the control group, significant changes were determined for anterior temporal (P < 0.05) and masseter (P < 0.01) muscle at clenching and orbicularis oris (P < 0.05) muscle at swallowing during observation period. Present findings indicated that treatment with POT appliance showed a positive influence on the masticatory and perioral musculature

Evaluation of Spontaneous Spinal Cerebrospinal Fluid Leaks Disease by Computerized Image Processing

Background: Spontaneous Spinal Cerebro spinal Fluid Leaks (SSCFL) is a disease based on tears on the dura mater. Due to widespread symptoms and low frequency of the disease, diagnosis is problematic. Diagnostic lumbar puncture is commonly used for diagnosing SSCFL, though it is invasive and may cause pain, inflammation or new leakages. T2-weighted MR imaging is also used for diagnosis; however, the literature on T2-weighted MRI states that findings for diagnosis of SSCFL could be erroneous when differentiating the diseased and control. One another technique for diagnosis is CT-myelography, but this has been suggested to be less successful than T2-weighted MRI and it needs an initial lumbar puncture

Machine learning approaches to classify anatomical regions in rodent brain from high density recordings

Identifying different functional regions during a brain surgery is a challenging task usually performed by highly specialized neurophysiologists. Progress in this field may be used to improve in situ brain navigation and will serve as an important building block to minimize the number of animals in preclinical brain research required by properly positioning implants intraoperatively. The study at hand aims to correlate recorded extracellular signals with the volume of origin by deep learning methods. Our work establishes connections between the position in the brain and recorded high-density neural signals. This was achieved by evaluating the performance of BLSTM, BGRU, QRNN and CNN neural network architectures on multisite electrophysiological data sets. All networks were able to successfully distinguish cortical and thalamic brain regions according to their respective neural signals. The BGRU provides the best results with an accuracy of 88.6 % and demonstrates that this classification task might be solved in higher detail while minimizing complex preprocessing steps