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find Author "王品" 13 results
  • A summary of research progress on intelligent information processing methods for pregnant women's remote monitoring

    The monitoring of pregnant women is very important. It plays an important role in reducing fetal mortality, ensuring the safety of perinatal mother and fetus, preventing premature delivery and pregnancy accidents. At present, regular examination is the mainstream method for pregnant women's monitoring, but the means of examination out of hospital is scarce, and the equipment of hospital monitoring is expensive and the operation is complex. Using intelligent information technology (such as machine learning algorithm) can analyze the physiological signals of pregnant women, so as to realize the early detection and accident warning for mother and fetus, and achieve the purpose of high-quality monitoring out of hospital. However, at present, there are not enough public research reports related to the intelligent processing methods of out-of-hospital monitoring for pregnant women, so this paper takes the out-of-hospital monitoring for pregnant women as the research background, summarizes the public research reports of intelligent processing methods, analyzes the advantages and disadvantages of the existing research methods, points out the possible problems, and expounds the future development trend, which could provide reference for future related researches.

    Release date:2020-12-14 05:08 Export PDF Favorites Scan
  • Treatment of Mammary Duct Ectasia

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  • Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble

    Speech feature learning is the core and key of speech recognition method for mental illness. Deep feature learning can automatically extract speech features, but it is limited by the problem of small samples. Traditional feature extraction (original features) can avoid the impact of small samples, but it relies heavily on experience and is poorly adaptive. To solve this problem, this paper proposes a deep embedded hybrid feature sparse stack autoencoder manifold ensemble algorithm. Firstly, based on the prior knowledge, the psychotic speech features are extracted, and the original features are constructed. Secondly, the original features are embedded in the sparse stack autoencoder (deep network), and the output of the hidden layer is filtered to enhance the complementarity between the deep features and the original features. Third, the L1 regularization feature selection mechanism is designed to compress the dimensions of the mixed feature set composed of deep features and original features. Finally, a weighted local preserving projection algorithm and an ensemble learning mechanism are designed, and a manifold projection classifier ensemble model is constructed, which further improves the classification stability of feature fusion under small samples. In addition, this paper designs a medium-to-large-scale psychotic speech collection program for the first time, collects and constructs a large-scale Chinese psychotic speech database for the verification of psychotic speech recognition algorithms. The experimental results show that the main innovation of the algorithm is effective, and the classification accuracy is better than other representative algorithms, and the maximum improvement is 3.3%. In conclusion, this paper proposes a new method of psychotic speech recognition based on embedded mixed sparse stack autoencoder and manifold ensemble, which effectively improves the recognition rate of psychotic speech.

    Release date:2021-10-22 02:07 Export PDF Favorites Scan
  • Development of Terahertz Imaging Technology in the Assessment of Burn Injuries

    Terahertz waves have unique properties and advantages, which makes it gain increasing attention and applications in the biomedical field. Burns is a common clinical trauma. Since the water sensitive and non destructive characteristics of terahertz, terahertz imaging techniques can be used to detect burns. So far, terahertz imaging technology in the assessment of burn injuries has been developed from ex vivo to in vivo, and high resolution images can be obtained through the gauzes and plasters. In this paper, we mainly introduces the application of terahertz imaging technology and development in the assessment of burn injuries.

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  • Image segmentation and classification of cytological cells based on multi-features clustering and chain splitting model

    The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm (MFMS) was applied to localize regions of nuclei. Then, chain splitting model (CSM) containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness. For classification phase, 4 shape-based features and 138 textural features based on color spaces of cell nuclei were extracted. In order to achieve optimal feature set and classify different cells, chain-like agent genetic algorithm (CAGA) combined with support vector machine (SVM) was proposed. The proposed method was tested on 15 cytology images containing 461 cell nuclei. Experimental results showed that the proposed method could automatically segment and classify different types of microscopic images of pancreatic cell and had effective segmentation and classification results. The mean accuracy of segmentation is 93.46%±7.24%. The classification performance of normal and malignant cells can achieve 96.55%±0.99% for accuracy, 96.10%±3.08% for sensitivity and 96.80%±1.48% for specificity.

    Release date:2017-08-21 04:00 Export PDF Favorites Scan
  • Research on Diagnosis Algorithm of Parkinson's Disease Based on Speech Sample Multi-edit and Random Forest

    Parkinson's disease (PD) diagnosis based on speech data has been proved to be an effective way in recent years. There are still some problems on preprocessing samples, ensemble learning, and so on. The problems can further cause misleading of classifiers, unsatisfactory classification accuracy and stability. This paper proposed a new diagnosis algorithm of PD by combining multi-edit sample selection method and random forest. At the end of it, this paper presents a group of experiments carried out with the newest public datasets. Experimental results showed that this proposed algorithm realized the classification of the samples and the subjects of PD. Furthermore, it achieved average classification accuracy of 100% and obtained improvement of up to 29.44% compared to those provided by the subjects. This paper proposes a new speech diagnosis algorithm for PD based on instance selection; and the method algorithm has a higher and more stable classification accuracy, compared with the other algorithms.

    Release date:2016-12-19 11:20 Export PDF Favorites Scan
  • Combining speech sample and feature bilateral selection algorithm for classification of Parkinson’s disease

    Diagnosis of Parkinson’s disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

    Release date:2017-12-21 05:21 Export PDF Favorites Scan
  • 围手术期多学科协作诊治流程在继发性甲状旁腺功能亢进症中的临床应用

    目的总结多学科协作诊治(multidisciplinary team,MDT)模式运用于继发性甲状旁腺功能亢进症(secondary hyperparathyroidism,SHPT)的效果。方法回顾性收集 2017 年 3 月至 2019 年 5 月期间在成都市第三人民医院按照 MDT 流程完成手术的 45 例 SHPT 患者的临床资料。结果本组 45 例患者的手术均顺利,无术中死亡和围手术期死亡患者。手术时间 56~220 min、(125±40)min;术中出血量 2~30 mL、(12±7)mL;住院时间 7~12 d、(9.07±0.86)d;术前准备时间 2~5 d、(3.26±0.72)d;术后拔管时间 2~5 d、(3.20±0.69)d。与术前的 iPTH 水平相比,术后 1 h、术后 1 周和术后 1 个月的 iPTH 水平均降低(P<0.05)。患者术后的血钙和血磷水平均较术前降低(P<0.01),但手术前后的血钾水平比较差异无统计学意义(P=0.55)。术后 32 例患者的骨及关节疼痛症状均不同程度缓解,其中 3 例无法行走患者可以下地自行行走;9 例瘙痒患者的症状也有明显缓解;4 例不宁腿患者中有 3 例明显缓解;40 例乏力患者中有 15 例自述术后 1 周后乏力症状缓解,19 例自诉术后 1~2 个月一定程度缓解。术后 2 例出现重度低钙血症,1 例出现出血,1 例出现声音轻度嘶哑,6 例出现肺部感染,1 例出现脂肪液化,2 例出现 SHPT 复发。术后有 3 例患者于半年后失访;有 19 例患者于当地医院完成随访,随访时间为8 个月~2.5 年,中位数为 14.5 个月,未出现复发;其余患者于成都市第三人民医院随访 6 个月~2.5 年,中位数为 13.5个月,也未出现复发。结论MDT 的诊疗流程是保障围手术期安全的前提,该流程可以让内外科医生各司其职,紧密配合,更好地监测患者内环境,以减少术后并发症,降低手术风险,缩短术前准备时间,提高治疗效果,最大限度地保障患者安全。

    Release date:2020-09-23 05:27 Export PDF Favorites Scan
  • A partition bagging ensemble learning algorithm for Parkinson’s speech data mining

    Methods for achieving diagnosis of Parkinson’s disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.

    Release date:2019-08-12 02:37 Export PDF Favorites Scan
  • Clinic study of complete endoscopic subcutaneous mastectomy for gynecomastia by optimizing operation procedure

    Objective To investigate effect of optimizing operation procedure (OOP) on surgical outcomes of complete endoscopic subcutaneous mastectomy (CESM) in treatment of gynecomastia. Methods A total of 217 patients with gynecomastia underwent CESM from January 2014 to March 2017 in the Third People’s Hospital of Chengdu were collected according to the criteria for inclusion and exclusion, further, based on a propensity score-matching model, a total of 94 patients were evenly assigned to OOP group (April 2015 later) and non-OOP group (before April 2015). The CESM with or without OOP was performed in the OOP group or the non-OOP group, respectively. The operative time, postoperative length of stay, treatment expenses, and favorable cosmetic effect were compared in these two groups. Results The differences in the general clinical data in both groups were not statistically significant (P>0.05). The operative time (min) was shorter (139.90±37.18versus 175.20±46.99, P=0.002), the postoperative length of stay (d) was shorter too (7.13±1.46 versus 8.47±2.71, P=0.021), and the treatment expenses (yuan) were more less (11 426.80±1 861.19 versus 12 315.75±1 306.64, P=0.036) in the OOP group as compared with the non-OOP group. Meanwhile the favorable cosmetic effect of the self-evaluation score in the OOP group was significantly higher than that in the non-OOP group (7.33±1.16 versus 5.97±1.16, P<0.05). Conclusion This study demonstrates that using optimizing standard CESM could shorten operative time, reduce treatment expenses, and improve satisfaction of patients.

    Release date:2018-03-13 02:31 Export PDF Favorites Scan
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