The growing rate of public health problem for increasing number of people afflicted with poor sleep quality suggests the importance of developing portable sleep electroencephalogram (EEG) monitoring systems. The system could record the overnight EEG signal, classify sleep stages automatically, and grade the sleep quality. We in our laboratory collected the signals in an easy way using a single channel with three electrodes which were placed in frontal position in case of the electrode drop-off during sleep. For a test, either silver disc electrodes or disposable medical electrocardiographic electrodes were used. Sleep EEG recorded by the two types of electrodes was compared to each other so as to find out which type was more suitable. Two algorithms were used for sleep EEG processing, i.e. amplitude-integrated EEG (aEEG) algorithm and sample entropy algorithm. Results showed that both algorithms could perform sleep stage classification and quality evaluation automatically. The present designed system could be used to monitor overnight sleep and provide quantitative evaluation.
Real-time continuous glucose monitoring can help diabetics to control blood sugar levels within the normal range. However, in the process of practical monitoring, the output of real-time continuous glucose monitoring system is susceptible to glucose sensor and environment noise, which will influence the measurement accuracy of the system. Aiming at this problem, a dual-calibration algorithm for the moving-window double-layer filtering algorithm combined with real-time self-compensation calibration algorithm is proposed in this paper, which can realize the signal drift compensation for current data. And a real-time continuous glucose monitoring instrument based on this study was designed. This real-time continuous glucose monitoring instrument consisted of an adjustable excitation voltage module, a current-voltage converter module, a microprocessor and a wireless transceiver module. For portability, the size of the device was only 40 mm × 30 mm × 5 mm and its weight was only 30 g. In addition, a communication command code algorithm was designed to ensure the security and integrity of data transmission in this study. Results of experiments in vitro showed that current detection of the device worked effectively. A 5-hour monitoring of blood glucose level in vivo showed that the device could continuously monitor blood glucose in real time. The relative error of monitoring results of the designed device ranged from 2.22% to 7.17% when comparing to a portable blood meter.
ObjectiveTo analysis the affecting factors of Acupuncture Deqi by Data Mining. MethodsLiteratures about Acupuncture Deqi, which published from October 1949 to November 2013, were searched from Chinese-language databases (CNKI, WanFang, VIP and CBM) and PubMed database with main keywords "deqi" or "needle sensation" etc. The relational Modern Literatures Database about Acupucture Deqi database was established via Data Enging of Microsoft SQL Server 2005 Express Edition, and correlated documents were excavated via Apriori algorithm in Weka. ResultsThree hundred and thirty-seven studies were selected. Analyzed by Apriori algorithm, frequencies ranking of needle sensation among patients were swelling, numbness, conduction and soreness etc. from high to low and similarly hereinafter; and among health adults were pain, soreness, numbness and heaviness etc. Frequencies ranking of correlation analysis results among patients were heaviness-pain-numbness, soreness-pain-numbness, heaviness-soreness etc. and among health adults were swelling-soreness, heaviness-soreness-numbness, heaviness-soreness etc. ConclusionFunctional status of human body is an important affecting factor of Acupuncture Deqi.
The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.
This paper presents a unit free-form deformation (FFD) method applied to rapid three-dimensioanl (3D) bone reconstruction, which was based on traditional FFD. With the femur as an example, we reconstructed a 3D model of femur from two X-ray images and a standardized model by taking advantage of unit FFD algorithm. The X-ray images and its parameters were taken by C-arm device. Those parameters and X-ray contour are contributed to 3D reconstruction. The out contours of X-ray image and standard model were connected by point matching algorithm. The unit-FFD lattice was built to reconstruct standard model and finally made the contour of X-ray image and standard model exactly the same. Experiments on shape accuracy, robustness and time consuming, carried out by 35 specimen from cadaver, showed that mean error of shape (0.52 mm) and mean construction time (112 s) were lower than those using traditional method (0.7-2.6 mm, 8-20 min). The method proposed in this paper shows a good prospect in clinical application and related research.
The aim of this study was to propose an algorithm for three-dimensional projection onto convex sets (3D POCS) to achieve super resolution reconstruction of 3D lung computer tomography (CT) images, and to introduce multi-resolution mixed display mode to make 3D visualization of pulmonary nodules. Firstly, we built the low resolution 3D images which have spatial displacement in sub pixel level between each other and generate the reference image. Then, we mapped the low resolution images into the high resolution reference image using 3D motion estimation and revised the reference image based on the consistency constraint convex sets to reconstruct the 3D high resolution images iteratively. Finally, we displayed the different resolution images simultaneously. We then estimated the performance of provided method on 5 image sets and compared them with those of 3 interpolation reconstruction methods. The experiments showed that the performance of 3D POCS algorithm was better than that of 3 interpolation reconstruction methods in two aspects, i.e. subjective and objective aspects, and mixed display mode is suitable to the 3D visualization of high resolution of pulmonary nodules.
Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.
ObjectiveTo construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction using deep learning algorithms. Methods We collected breast ultrasound images of 178 patients with thyroid dysfunction (969 images) from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to February 2024, which served as the training set. The deep learning algorithm was used to construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction. In addition, we collected breast ultrasound images of 81 patients with thyroid dysfunction (445 images) from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from March 2024 to January 2025, which served as the validation set. The above system was used as validation set to diagnose whether patients with thyroid dysfunction had breast nodules, and the diagnostic efficacy of imaging physicians’ diagnosis and the intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction was analyzed. The consistency between the diagnosis of ultrasound physicians, intelligent ultrasound diagnosis system and the “gold standard” was tested by Kappa test. ResultsThere was no statistically significant difference in age, type of thyroid dysfunction, disease duration, number of breast nodules, and other clinical data between the training set and the validation set (P>0.05). The time required for the training set intelligent ultrasound diagnostic system to diagnose a single breast ultrasound image was (0.04±0.01) min, which was shorter than that of an ultrasound physicians [(12.36±2.58) min], t=63.709, P＜0.001. The sensitivity, specificity, accuracy, and area under the curve (AUC) of detecting breast nodules in patients with thyroid dysfunction using an intelligent ultrasound diagnostic system were 97.87% (46/47), 100% (34/34), 98.77% (80/81), and 0.997 [95%CI: (0.951, 1.00)], respectively. The sensitivity, specificity, accuracy, and AUC of detecting breast nodules by ultrasound physicians were 89.36% (42/47), 91.18% (31/34), 90.12% (73/81), and 0.904 [95%CI: (0.818, 0.958)], respectively. The AUC of the intelligent ultrasound diagnosis system was higher than that of the ultrasound physician (Z=2.673, P=0.008). The detection results of breast nodules in patients with thyroid dysfunction diagnosed by ultrasound physicians were generally consistent with the “gold standard” (Kappa value=0.799, P＜0.001), while the intelligent ultrasound diagnosis system was in good agreement with the “gold standard” (Kappa value=0.975, P＜0.001). The confusion matrix results showed that the number of false positives was 3 and 0 for the ultrasound department physicians and the intelligent ultrasound diagnostic system, respectively, while the number of false negatives was 5 and 1. The calibration curve results indicated a high consistency between the diagnostic probability and the actual probability of the intelligent ultrasound diagnostic system, with the calibration curve fitting well with the ideal curve (Hosmer-Lemeshow test: χ2=1.246, P=0.997). ConclusionThe intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction constructed by deep learning algorithm has good diagnostic efficacy, which can help ultrasound physicians improve screening efficiency and accuracy.
The nondestructive reconstruction of three-dimensional (3D) temperature field in biological tissue is always an important problem to be resolved in biomedical engineering field. This paper presents a novel method of nondestructive reconstruction of 3D temperature field in biological tissue based on multi-island genetic algorithm (MIGA). By this method, the resolving of inverse problem of bio-heat transfer is transformed to be a solving process of direct problem. An experiment and its corresponding simulation were carried out to verify the feasibility and reliability. In the experiment a high purity polypropylene material, whose thermophysical parameters were similar to the fat tissue being tested, were adopted so that it could avoid the negative results created by the other factors. We set the position P(x, y, z) as the point heat source in the biological tissue and its temperature t as optimization variable, got the experimental temperature values of the points in a module surface, subtracted them from the corresponding simulating temperature values in the same module surface, and then took the sum of absolute value. We took it as the objective function of successive iteration. It was found that the less the target value was, the more optimal the current variables, i.e. the heat source position and the temperature values, were. To improve the optimization efficiency, a novel establishment method of objective function was also provided. The simulating position and experimental position of heat source were very approximate to each other. When the optimum values are determined, the corresponding 3D temperature field is also confirmed, and the temperature distribution of arbitrary section can be acquired. The MIGA can be well applied in the reconstruction of 3D temperature field in biological tissue. Because of the differences between the MIGA and the traditional numerical methods, we do not have to acquire all the data of surface. It is convenient and fast, and shows a prosperous application future.
Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.