Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: −0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
The mechanical behavior modeling of human soft biological tissues is a key issue for a large number of medical applications, such as surgery simulation, surgery planning, diagnosis, etc. To develop a biomechanical model of human soft tissues under large deformation for surgery simulation, the adaptive quasi-linear viscoelastic (AQLV) model was proposed and applied in human forearm soft tissues by indentation tests. An incremental ramp-and-hold test was carried out to calibrate the model parameters. To verify the predictive ability of the AQLV model, the incremental ramp-and-hold test, a single large amplitude ramp-and-hold test and a sinusoidal cyclic test at large strain amplitude were adopted in this study. Results showed that the AQLV model could predict the test results under the three kinds of load conditions. It is concluded that the AQLV model is feasible to describe the nonlinear viscoelastic properties of in vivo soft tissues under large deformation. It is promising that this model can be selected as one of the soft tissues models in the software design for surgery simulation or diagnosis.
This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Objective To investigate the predictive value of mechanical power (MP) in the weaning outcome of adaptive mechanical ventilation plus intelligent trigger (AMV+IntelliCycle, simply called AMV) mode for acute respiratory distress syndrome (ARDS) patients. Methods From November 2019 to March 2021, patients with mild to moderate ARDS who were treated with invasive mechanical ventilation in the intensive care unit of the First Affiliated Hospital of Jinzhou Medical University were divided into successful weaning group and failed weaning group according to the outcome of weaning. All patients were treated with AMV mode during the trial. The MP, oral closure pressure (P0.1), respiratory rate (RR) and tidal volume (VT) of the two groups were compared 30 min and 2 h after spontaneous breathing trial (SBT). The correlation between 30 min and 2 h MP and shallow rapid respiratory index (RSBI) was analyzed by Pearson correlation. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of 30 min MP in ARDS patients with AMV mode weaning failure. Results Sixty-eight patients were included in the study, 49 of them were successfully removed and 19 of them failed. There was no statistical significance in age, gender, body mass index, oxygenation index, acute physiology and chronic health evaluation Ⅱ score, reasons for mechanical ventilation (respiratory failure, sepsis, intracranial lesions, and others) between the two groups (all P>0.05). The MP, P0.1 and RR at SBT 30 min and 2 h of the successful weaning group was lower than those of the failed weaning group (all P<0.05), but the VT of the successful weaning group was higher than the failed weaning group (all P<0.05). There was a significant relation between the MP at SBT 30 min and 2 h and RSBI (r value was 0.640 and 0.702 respectively, both P<0.05). The area under ROC curve of MP was 0.674, 95% confidence interval was 0.531 - 0.817, P value was 0.027, sensitivity was 71.73%, specificity was 91.49%, positive predictive value was 0.789, negative predictive value was 0.878, optimal cutoff value was 16.500. The results showed that 30 min MP had a good predictive value for the failure of weaning in AMV mode in ARDS patients. Conclusion MP can be used as an accurate index to predict the outcome of weaning in ARDS patients with AMV mode.
In order to get the adaptive bandwidth of mean shift to make the tumor segmentation of brain magnetic resonance imaging (MRI) to be more accurate, we in this paper present an advanced mean shift method. Firstly, we made use of the space characteristics of brain image to eliminate the impact on segmentation of skull; and then, based on the characteristics of spatial agglomeration of different tissues of brain (includes tumor), we applied edge points to get the optimal initial mean value and the respectively adaptive bandwidth, in order to improve the accuracy of tumor segmentation. The results of experiment showed that, contrast to the fixed bandwidth mean shift method, the method in this paper could segment the tumor more accurately.
We conducted this study to explore the influence of the ocular residual aberrations changes on contrast sensitivity (CS) function in eyes undergoing orthokeratology using adaptive optics technique. Nineteen subjects' nineteen eyes were included in this study. The subjects were between 12 and 20 years (14.27±2.23 years) of age. An adaptive optics (AO) system was adopted to measure and compensate the residual aberrations through a 4-mm artificial pupil, and at the same time the contrast sensitivities were measured at five spatial frequencies (2,4,8,16, and 32 cycles per degree).The CS measurements with and without AO correction were completed. The sequence of the measurements with and without AO correction was randomly arranged without informing the observers. A two-interval forced-choice procedure was used for the CS measurements. The paired t-test was used to compare the contrast sensitivity with and without AO correction at each spatial frequency. The results revealed that the AO system decreased the mean total root mean square (RMS) from 0.356 μm to 0.160 μm(t=10.517, P＜0.001), and the mean total higher-order RMS from 0.246 μm to 0.095 μm(t=10.113, P＜0.001). The difference in log contrast sensitivity with and without AO correction was significant only at 8 cpd (t=-2.51, P=0.02). Thereby we concluded that correcting the ocular residual aberrations using adaptive optics technique could improve the contrast sensitivity function at intermediate spatial frequency in patients undergoing orthokeratology.
An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance (MR) image bias field. An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm. The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum. The Legendre polynomial was used to fit bias field, the polynomial parameters were optimized globally, and finally the bias field was estimated and corrected. Compared to those with the improved entropy minimum algorithm, the entropy of corrected image was smaller and the estimated bias field was more accurate in this study. Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm. This algorithm can be applied to the correction of MR image bias field.
Master protocol with adaptive design is a new complex innovative trial design that combines an adaptive treatment strategy and master protocol. It is more flexible and adjustable. In the complex clinical trial environment, the dynamics emphasized in this design are consistent with the idea of traditional Chinese medicine (TCM) syndrome differentiation and treatment. In this study, we summarized its concept, characteristics and advantages, and we also discussed its application in TCM clinical research. We hope this paper can provide more thinking and suggestions for TCM clinical trials.
Image-guided radiation therapy using magnetic resonance imaging (MRI) is a new technology that has been widely studied and developed in recent years. The technology combines the advantages of MRI imaging, and can offer online real-time tracking of tumor and adjacent organs at risk, as well as real-time optimization of radiotherapy plan. In order to provide a comprehensive understanding of this technology, and to grasp the international development and trends in this field, this paper reviews and summarizes related researches, so as to make the researchers and clinical personnel in this field to understand recent status of this technology, and carry out corresponding researches. This paper summarizes the advantages of MRI and the research progress of MRI linear accelerator (MR-Linac), online guidance, adaptive optimization, and dosimetry-related research. Possible development direction of these technologies in the future is also discussed. It is expected that this review can provide a certain reference value for clinician and related researchers to understand the research progress in the field.
In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.