In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.
To solve the defect which is recognizing but not rating the stress, or rating but not considering the influence of the previous stress state to the current state of the existing affective stress evaluation method, this paper proposes an approach of affective stress rating model on electrocardiogram (ECG). An affective stress rating algorithm based on hidden Markov model (HMM) was established with the theory of affective computing. The individual's affective stress was rated using this affective rating model combining the investigation questionnaire. Features like complexity and approximate entropy of ECG were used in the model, and a matching process suggested that it improved the accuracy of affective stress rating. The result of the experiment illustrated that the model considering the environmental factors and the influence of previous stress state to the current state was an effective method in affective stress rating, and the accuracy of rating was improved by this affective stress rating method.
Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of x, y and z axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.
ObjectivesTo evaluate the economic efficacy of nab-paclitaxel (NAB-P) combined with gemcitabine (GEM) versus GEM alone in the treatment of metastatic pancreatic cancer in China.MethodsA Markov model simulating the costs and health outcomes was developed to estimate quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER). The impact of parameter uncertainty on the model was assessed by deterministic one-way sensitivity analysis.ResultsNAB-P combined with GEM was shown superior efficacy compared to gemcitabine monotherapy, however with higher costs. The ICER between the two groups was 964 780.79￥/QALY.ConclusionsCompared with gemcitabine monotherapy, NAB-P combined with GEM is not cost-effective. The conclusion is confirmed by deterministic one-way sensitivity analysis.
Objectives To determine the health benefit of elbasvir/grazoprevir versus peginterferon combing with ribavirin (PR regimen) for Chinese chronic hepatitis C patients with genotype 1b infection. Methods Markov cohort state-transition models were constructed to conduct cost utility analysis. Sensitivity analyses were performed based on base-case analysis. Results Elbasvir/grazoprevir was dominant versus PR, resulting in higher QALYs and lower costs for both noncirrhotic patients (13.867 5 QALYs, 82 090.82 RMB vs. 12.696 2 QALYs, 122 791.55 RMB) and cirrhotic patients (12.841 6 QALYs, 225 807.70 RMB vs. 8.892 4 QALYs, 326 545.01 RMB). Elbasvir/grazoprevir was economically dominant in nearly 100% among all patients within the range of threshold from 0 to 161 805 RMB/QALY. Conclusions Elbasvir/grazoprevir was dominant in treatment of genotype 1b chronic hepatitis C infection in China.
Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.
Objective To compare the long-term cost-utility of three first-generation EGFR-TKIs targeted drugs, gefitinib, icotinib, and erlotinib as first-line treatments for advanced non-small cell lung cancer (NSCLC). Methods Real-world data were collected from 1 511 patients with advanced NSCLC treated with first-generation EGFR-TKIs as first-line treatment at West China Hospital of Sichuan University from 2009 to 2019. A three-state Markov model was established to evaluate the clinical efficacy, safety and cost-utility of three first-generation EGFR-TKIs targeted drugs. The transition probability of each state was obtained by survival analysis, the direct and indirect costs were calculated by the bottom-up method, the health utility value was obtained through literature research, the incremental cost effectiveness ratio (ICER) and quality-adjusted life years (QALYs) were calculated, and sensitivity analyses and Monte Carlo simulations were performed. Results There was no significant difference in clinical efficacy among the three first-generation EGFR-TKIs in the treatment of NSCLC. The incidence of skin rash and liver injury caused by gefitinib was significantly higher than that caused by icotinib and erlotinib (P<0.05). The average economic burden of patients treated with icotinib was the lowest (CNY 192 535.3) (P<0.01). The cost-utility ratio of icotinib (CNY 132 985.9/QALYs) was much lower than that of gefitinib (CNY 205 005.3/QALYs) and erlotinib (CNY 172 893.1/QALYs). Conclusion Compared with the three first-generation EGFR-TKIs drugs, icotinib is the most cost-effective.
Health economics analysis has become increasingly important in recent years. It is essential to master the use of relevant software to conduct research in health economics. TreeAge Pro software is widely used in the healthcare decision analysis. It can carry out decision analysis, cost-effectiveness analysis, and Monte Carlo simulation. With powerful functionlity and outstanding visualization, it can build Markov disease transition models to analyze Markov processes according to disease models and accomplish decision analysis with decision trees and influence diagrams. This paper introduces cost-effectiveness analysis based on Markov model with examples and explains the main graphs.
Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1–S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.
Objective To evaluate the cost effectiveness of human papillomavirus vaccine (HPV) for treating cervical cancer. Methods We constructed a Markov model to evaluate the cost-effectiveness of HPV versus Chinese healthy women aged 18 to 25 for treating Cervical Cancer. We calculated the clinical benefits and cost-effectiveness and judged the results based on willing to pay. Sensitivity analysis was made for parameters like cost, discounting rate and vaccine efficacy. Results HPV vaccination was a cost-effective option under the local willing to pay value with the incremental cost utility ratio 43 489 per QALY gained. It proved that vaccination was an economic and effective solution. Conclusion Given the results of Markov model, the cost effectiveness of HPV vaccination of Chinese women aged 18 to 25 is positive. Considering the data sources and model hypothesis, this report has some limitations. Further studies are warranted.