For speech detection in Parkinson’s patients, we proposed a method based on time-frequency domain gradient statistics to analyze speech disorders of Parkinson’s patients. In this method, speech signal was first converted to time-frequency domain (time-frequency representation). In the process, the speech signal was divided into frames. Through calculation, each frame was Fourier transformed to obtain the energy spectrum, which was mapped to the image space for visualization. Secondly, deviations values of each energy data on time axis and frequency axis was counted. According to deviations values, the gradient statistical features were used to show the abrupt changes of energy value in different time-domains and frequency-domains. Finally, KNN classifier was applied to classify the extracted gradient statistical features. In this paper, experiments on different speech datasets of Parkinson’s patients showed that the gradient statistical features extracted in this paper had stronger clustering in classification. Compared with the classification results based on traditional features and deep learning features, the gradient statistical features extracted in this paper were better in classification accuracy, specificity and sensitivity. The experimental results show that the gradient statistical features proposed in this paper are feasible in speech classification diagnosis of Parkinson’s patients.
Objective To propose an innovative self-supervised learning method for vascular segmentation in computed tomography angiography (CTA) images by integrating feature reconstruction with masked autoencoding. Methods A 3D masked autoencoder-based framework was developed, where in 3D histogram of oriented gradients (HOG) was utilized for multi-scale vascular feature extraction. During pre-training, random masking was applied to local patches of CTA images, and the model was trained to jointly reconstruct original voxels and HOG features of masked regions. The pre-trained model was further fine-tuned on two annotated datasets for clinical-level vessel segmentation. Results Evaluated on two independent datasets (30 labeled CTA images each), our method achieved superior segmentation accuracy to the supervised neural network U-Net (nnU-Net) baseline, with Dice similarity coefficients of 91.2% vs. 89.7% (aorta) and 84.8% vs. 83.2% (coronary arteries). Conclusion The proposed self-supervised model significantly reduces manual annotation costs without compromising segmentation precision, showing substantial potential for enhancing clinical workflows in vascular disease management.
In developed nations, aortic stenosis (AS) is the most common valvular heart disease presentation, and its prevalence is increasing due to aging populations. Accurate diagnosis of the disease process and determination of its severity are essential in clinical decision-making. Although current guidelines recommend measuring transvalvular gradients, maximal velocity, and aortic valve area in determining the disease severity, inconsistent grading of disease severity remains a common problem in clinical practice. Recent studies suggest that patients with paradoxical low-flow and/or low-gradient, severe AS are at a more advanced stage of the disease process and have a poorer prognosis. This mode of presentation may lead to an underevaluation of symptoms and inappropriate delay of AVR. Therefore, this challenging clinical situation should be carefully assessed in particular in symptomatic patients and clinical decisions should be tailored individually.
ObjectiveTo investigate the value of proton magnetic resonance spectroscopy (1H-MRS), gradient dual-echo, and triple-echo sequences in the quantitative evaluation of treatment effect of fatty liver at 3.0T MR.MethodsThirty patients with fatty liver diagnosed by CT or ultrasound who admitted in Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital between August 2017 and May 2018, were enrolled and undergone gradient dual-echo, triple-echo, and 1H-MRS examination before and 3 months after treatment. The fat index (FI) and relative lipid content (RLC) were measured. Fatty liver index (FLI) was calculated from blood biochemical indicators, waist circumference, and BMI at the same time. With the reference standard of FLI, the results before and after treatment measured from MRI were analyzed.ResultsThere were significantly differences of FLI, FIdual, FItriple, and RLC before and after treatment (t=5.281, P<0.001; Z=–3.651, P<0.001; Z=–3.630, P<0.001; Z=–4.762, P<0.001), all indexes decreased after treatment. FIdual and FItriple were positively correlated with FLI before (rs=0.413, P=0.023; rs=0.396, P=0.030) and after treatment (rs=0.395, P=0.031; rs=0.519, P=0.003), the highest correlation factor was FItriple to FLI after treatment. There were no significant correlation between RLC and FLI before and after treatment (P>0.05).ConclusionsIt is feasible to quantitatively evaluate the treatment effect of fatty liver by using 1H-MRS, gradient dual-echo, and triple-echo sequences. Gradient triple-echo sequences has better accuracy, which is technically easy to implement and more suitable for clinical development.
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.
Pulsed magnetic field gradients generated by gradient coils are widely used in signal location in magnetic resonance imaging (MRI). However, gradient coils can also induce eddy currents in final magnetic field in the nearby conducting structures which lead to distortion and artifact in images, misguiding clinical diagnosis. We tried in our laboratory to measure the magnetic field of gradient-induced eddy current in 1.5 T superconducting magnetic resonance imaging device; and extracted key parameters including amplitude and time constant of exponential terms according to inductance-resistance series mathematical module. These parameters of both self-induced component and crossing component are useful to design digital filters to implement pulse pre-emphasize to reshape the waveform. A measure device that is a basement equipped with phantoms and receiving coils was designed and placed in the isocenter of the magnetic field. By applying testing sequence, contrast experiments were carried out in a superconducting magnet before and after eddy current compensation. Sets of one dimension signal were obtained as raw data to calculate gradient-induced eddy currents. Curve fitting by least squares method was also done to match inductance-resistance series module. The results also illustrated that pulse pre-emphasize measurement with digital filter was correct and effective in reducing eddy current effect. Pre-emphasize waveform was developed based on system function. The usefulness of pre-emphasize measurement in reducing eddy current was confirmed and the improvement was also presented. All these are valuable for reducing artifact in magnetic resonance imaging device.
Objective To quantitatively evaluate the effect of 2 types of pressures induced injury by using threedimensional (3D) reconstruction of rats loaded tibial is anterior muscle from two-dimensional (2D) image of serial histological sections. Methods Twenty female or male Sprague Dawley rats, aged 10-12 weeks and weighing 280-300 g, were randomlydivided into experimental group (n=10) and control group (n=10). The random side of tibial is anterior muscle was givenintermittent gradient (8.0-21.3 kPa) and sustained (13.3 kPa) pressure in 0.12 cm2 area in experimental group and controlgroup, respectively; the experiment was terminated and the general condition of rats was observed after 3 cycles, and a single cycle included 2 hours of compression and 30 minutes of release. The general observations of pressed skin and tibial is anterior muscle were done after 24 hours of pressure rel ief, and the tibial is anterior muscle was harvested integrally from the loaded side, then made into interval 4 μm serial sections. After HE staining, 2D images were obtained. Necrosis and injury areas were distinguished by Image Pro Plus (IPP) 6.0 software and image registration was conducted by Photoshop 8.0.1 after 2D panorama images acquired by digital microscope (× 40) and IPP mosaic software. 3D reconstruction was establ ished via data processing using Mimics 10.1 software so as to get the volume, the surface area, and 3D images of the whole piece of tibial is anterior muscle and injury areas respectively. Results All rats of 2 groups survived till experiment terminated and no skin ulcers occurred after 24 hours. Edema and indentation were observed on press side skin and tibial is anterior muscles of 2 groups, fadeless maroon area was observed in control group. A total of 994 sl ices were obtained from 20 samples of tibial is anterior muscles. 3D images suggested that injury of control group was severe, which penetrated the whole piece of tibial is anterior muscle and expandedalong the tibia bony prominence. By contrast, injury of experimental group was less, but had similar width to the contact surface of indentor. There was no significant difference in the volume and the surface area of tibial is anterior muscle between 2 groups (P gt; 0.05), while the injury volume and the injury surface area were significantly smaller in experimental group than in control group (P lt; 0.05). Conclusion 3D reconstruction is an effective method to quantitatively evaluate pathological changes inside the integrity tissue and can provide the visual basis for the mechanical property distributed in the loaded muscle. Intermittent gradient pressure can reduce deep tissue injury.
The dose data produced by treatment plan system (TPS) in intensity-modulated radiation therapy (IMRT) has many gradient edge points. Considering this feature we proposed a new interpolation algorithm called treatment plan dose interpolation algorithm based on gradient feature in intensity-modulated radiation therapy (TDAGI), which improves the Canny algorithm to detect the gradient edge points and non-edge points by using the gradient information in the dose data plane. For each gradient edge point, the corresponding gradient profile was traced and the profile's sharpness was calculated, and for each non-edge point, the dispersion was calculated. With the sharpness or dispersion, the kernel coefficients of bi-cubic interpolation can be obtained and can be used as the central point to complete the bi-cubic interpolation calculation. Compared with bi-cubic interpolation and bilinear interpolation, the TDAGI algorithm is more accurate. Furthermore, the TDAGI algorithm has the advantage of gradient keeping. Therefore, TDAGI can be used as an alternative method in the dose interpolation of TPS in IMRT.
ObjectiveTo explore the variation of the structure of the intestinal flora between healthy people and patients with obstructive jaundice perioperatively.
MethodsFrom February 2013 to August 2014, 20 patients with obstructive jaundice and 10 healthy persons (normal control group) in our hospitol were selected as the research object. The first stool specimens of the research object after admission were obtained and the total fecal bacteria DNA were extracted. After polymerase chain reaction amplification, the changes in the structure of bacterial flora were dynamic observed by using denaturing gradient gel electrophoresis (DGGE), and the gel bands were analyzed by using Quantity One software. The similarity and diversity of flora structure, and principal component analysis (PCA) were analyzed.
ResultsSignificant differences of colonic microflora were found between patients with obstructive jaundice and healthy people; advantage intestinal flora in obstructive jaundice patients was significant lower than the normal control group. With the extension of time and degree of obstruction aggravated, a descending trend was found in number, abundance, and diversity of the intestinal microflora (P < 0.05).
ConclusionThere is significant differences in the structure of colon bacteria in patients with obstructive jaundice and healthy persons.
Objective
To analyze the relationship between end-tidal carbon dioxide partial pressure (PETCO2) and arterial CO2 pressure (PaCO2) in invasive ventilated patients.
Methods
An observational study was conducted in adult patients admitted to Intensive Care Unit (ICU) between June 2016 and March 2017. Samples were immediately analyzed for PaCO2 using a blood gas analyzer. At the same time the arterial to end-tidal CO2 gradient was determined. Relationship in different mechanical ventilation modes, disease categories and PaO2/FiO2 were analyzed in this study.
Results
A total of 225 arterial blood gases were obtained from the 104 patients. In each of these modes the PETCO2 was generally lower than the PaCO2. There was a positive correlation between PaCO2 and PETCO2 regardless of different mode (r=0.70, Y=11.08+0.77X). A positive correlation was found in SIMV and SPONT modes, but not in A/C mode. The relationship between PaCO2 and PETCO2 in COPD, trauma, cerebrovascular disease and severe pneumonia patients shown a positive correlation (r value was 0.76, 0.64, 0.53, and 0.56, respectively). There was a significant correlation whether PaCO2/FiO2<200 mm Hg (r=0.69, P<0.001) or ≥200 mm Hg (r=0.71, P<0.001).
Conclusions
The results of this study show that PETCO2 monitoring accurately reflects PaCO2 during mechanical ventilation. A positive correlation is found in different ventilation modes, regardless of disease categories or PaCO2/FiO2.
