Due to the minimum free energy model, it is very important to predict the RNA secondary structure accurately and efficiently from the suboptimal foldings. Using clustering techniques in analyzing the suboptimal structures could effectively improve the prediction accuracy. An improved k-medoids cluster method is proposed to make this a better accuracy with the RBP score and the incremental candidate set of medoids matrix in this paper. The algorithm optimizes initial medoids through an expanding medoids candidate sets gradually.The predicted results indicated this algorithm could get a higher value of CH and significantly shorten the time for calculating clustering RNA folding structures.
We applied Demons and accelerated Demons elastic registration algorithm in radiotherapy cone beam CT (CBCT) images, We provided software support for real-time understanding of organ changes during radiotherapy. We wrote a 3D CBCT image elastic registration program using Matlab software, and we tested and verified the images of two patients with cervical cancer 3D CBCT images for elastic registration, based on the classic Demons algorithm, minimum mean square error (MSE) decreased 59.7%, correlation coefficient (CC) increased 11.0%. While for the accelerated demons algorithm, MSE decreased 40.1%, CC increased 7.2%. The experimental verification with two methods of demons algorithm obtained the desired results, but the small difference appeared to be lack of precision, and the total registration time was a little long. All these problems need to be further improved for accuracy and reducing of time.
As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.
ObjectiveTo analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. MethodsA total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.ResultsOf the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.ConclusionThe ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.
Electroencephalogram (EEG) analysis has been widely used in disease diagnosis. The EEG detection of the patients with epilepsy can be used to make judgments about patients' conditions in time, which is of great practical value. Therefore, the techniques of automatic detection, diagnosis and classification of epileptic EEG signals are urgently needed. In order to realize fast and accurate automatic detection and classification of the EEG signals during the normal, interictal and ictal periods of epilepsy, we propose an automatic classification and recognition method which combines the Real Adaboost algorithm based on error-correcting output codes (ECOC) with a feature extraction method based on sample entropy (SampEn) and wavelet packet energy in this paper. In the present study, we used the sample entropy of input signals and the energy of some parts of frequency bands as features, and then we classified the extracted features with the method combining ECOC with Real AdaBoost algorithm. In order to test the validity, we used the epilepsy database from the University of Bonn. The database has 5 groups of EEG signals, which contains the data of normal people with their eyes open or closed, the data collected inside and outside of the epileptic foci from patients during their interictal period and the data from patients during their ictal period. The results showed that the method had strong abilities of classification and recognition of the EEG signals, and especially the recognition rate had been improved significantly. The average recognition rate of the EEG signals with different features during the three periods of the five groups mentioned above can reach 96.78%, which is superior to those with algorithms recorded in many other literatures. The method has better stability, processing speed and potential of real-time application, and it plays a supporting role in the prediction and detection of epilepsy in clinical practice.
A new cool-tip radiofrequency (RF) ablation therapeutic instrument based on impedance control algorithm is introduced in this paper. The equipment is composed of hardware system and software system. The RF power output and real time data acquisition are completed by the hardware system, while the software is used mainly to finish the control of the ablation range, the core of which is impedance control algorithm, and it also used to complete the display of the real time data in the course of the experiment. The impedance algorithm has solved the problem of impedance increased rapidly during the RF ablation, which has also expanded the scope of ablation. The pig liver experiments showed that the impedance control algorithm had strong adaptability. It also obtained a result of ablation range up to 3.5~4.5 cm single needle. It has the high clinical practical value of one-time inactivation of 3~5 cm tumor.
Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.
Mass point-spring model is one of the commonly used models in virtual surgery. However, its model parameters have no clear physical meaning, and it is hard to set the parameter conveniently. We, therefore, proposed a method based on genetic algorithm to determine the mass-spring model parameters. Computer-aided tomography (CAT) data were used to determine the mass value of the particle, and stiffness and damping coefficient were obtained by genetic algorithm. We used the difference between the reference deformation and virtual deformation as the fitness function to get the approximate optimal solution of the model parameters. Experimental results showed that this method could obtain an approximate optimal solution of spring parameters with lower cost, and could accurately reproduce the effect of the actual deformation model as well.
Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA) theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.
A non-linear rectification based on immune genetic algorithm (IGA) is proposed in this paper, for the shortcoming of the non-linearity rectification. This algorithm introducing the biologic immune mechanism into the genetic algorithm can restrain the disadvantages that the poor precision, slow convergence speed and early maturity of the genetic algorithm. Computer simulations indicated that the algorithm not only keeps population diversity, but also increases the convergent speed, precision and the stability greatly. The results have shown the correctness and effectiveness of the method.
