Notes

Options, limitations, and suggestions in lower symptoms investigation.
RESULTS Mean square error (MSE) and correlation coefficient (CC) are used to evaluate the correlation between C-band sensing technique and contact respiratory sensor. The results show that all the MSE are less than 0.6 and all CC are more than 0.8, yielding a significant correlation between the two used for detecting each breathing pattern. Clinical Impact C-band sensing technique is not only used to determine respiratory rates but also to identify breathing patterns, regarding as a preferred noncontact alternative approach to the traditional contact sensing methods. C-band sensing technique also provides a basis for the non-invasive detection of certain respiratory disorders. 2168-2372 © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http//www.ieee.org/publications_standards/publications/rights/index.html for more information.OBJECTIVE Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported that most of PD patients have voice impairments. But these voice impairments are not perceptible to common listeners. Therefore, different machine learning methods have been developed for automated PD detection. However, these methods either lack generalization and clinically significant classification performance or face the problem of subject overlap. METHODS To overcome the problems discussed above, we attempt to develop a hybrid intelligent system that can automatically perform acoustic analysis of voice signals in order to detect PD. The proposed intelligent system uses linear discriminant analysis (LDA) for dimensionality reduction and genetic algorithm (GA) for hyperparameters optimization of neural network (NN) which is used as a predictive model. Moreover, to avoid subject overlap, we use leave one subject out (LOSO) validation. RESULTS The proposed method namely LDA-NN-GA is evaluated in numerical experiments are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http//www.ieee.org/publications_standards/publications/rights/index.html for more information.INTRODUCTION The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. OBJECTIVES We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. METHODS 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. RESULTS The validation demonstrates very high correlation values of several standard ECG parameters PR interval 0.984 +/-0.021 (p-value less then 0.001), QRS interval 1+/- SD (p-value less then 0.001), QT interval 0.981 +/- 0.023 p-value less then 0.001, and RR interval 1 +/- 0.001 p-value less then 0.001. check details CONCLUSION Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease. 2168-2372 © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http//www.ieee.org/publications_standards/publications/rights/index.html for more information.A reliable, accessible, and non-intrusive method for tracking respiratory and heart rate is important for improving monitoring and detection of sleep apnea. In this study, an algorithm based on motion analysis of infrared video recordings was validated in 50 adults referred for clinical overnight polysomnography (PSG). The algorithm tracks the displacements of selected feature points on each sleeping participant and extracts respiratory rate using principal component analysis and heart rate using independent component analysis. For respiratory rate estimation (mean ± standard deviation), 89.89 % ± 10.95 % of the overnight estimation was accurate within 1 breath per minute compared to the PSG-derived respiratory rate from the respiratory inductive plethysmography signal, with an average root mean square error (RMSE) of 2.10 ± 1.64 breaths per minute. For heart rate estimation, 77.97 % ± 18.91 % of the overnight estimation was within 5 beats per minute of the heart rate derived from the pulse oximetry signal from PSG, with mean RMSE of 7.47 ± 4.79 beats per minute. No significant difference in estimation of RMSE of either signal was found according to differences in body position, sleep stage, or amount of the body covered by blankets. This vision-based method may prove suitable for overnight, non-contact monitoring of respiratory rate. However, at present, heart rate monitoring is less reliable and will require further work to improve accuracy. 2168-2372 © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http//www.ieee.org/publications_standards/publications/rights/index.html for more information.Background Diabetes is known to cause delayed wound healing, and chronic non-healing lower extremity ulcers may end with lower limb amputations and mortalities. Given the increasing prevalence of diabetes mellitus worldwide, it is critical to focus on underlying mechanisms of these debilitating wounds to find novel therapeutic strategies and thereby improve patient outcome. Methods This study aims to design a label-free optical fluorescence imager that captures metabolic indices (NADH and FAD autofluorescence) and monitors the in vivo wound healing progress noninvasively. Furthermore, 3D optical cryo-imaging of the mitochondrial redox state was utilized to assess the volumetric redox state of the wound tissue. Results The results from our in vivo fluorescence imager and the 3D cryo-imager quantify the differences between the redox state of wounds on diabetic mice in comparison with the control mice. These metabolic changes are associated with mitochondrial dysfunction and higher oxidative stress in diabetic wounds.
Homepage: https://www.selleckchem.com/products/methylene-blue-trihydrate.html