数字化WACOM图形板数据集诊断帕金森病的研究(IJITCS-V9-N12-6)

数字化WACOM图形板数据集诊断帕金森病的研究(IJITCS-V9-N12-6)
数字化WACOM图形板数据集诊断帕金森病的研究(IJITCS-V9-N12-6)

I.J. Information Technology and Computer Science, 2017, 12, 45-51

Published Online December 2017 in MECS (https://www.360docs.net/doc/f08229884.html,/)

DOI: 10.5815/ijitcs.2017.12.06

A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset

Kemal Akyol

Department of Computer Engineering, Kastamonu University, 37100, Kastamonu, Turkey

E-mail: kakyol@https://www.360docs.net/doc/f08229884.html,.tr

Received: 04 September 2017; Accepted: 22 September 2017; Published: 08 December 2017

Abstract—Parkinson Disease is a neurological disorder, which is one of the most painful, dangerous and non-curable diseases, which occurs at older ages. The Static Spiral Test, Dynamic Spiral Test and Stability Test on Certain Point records were used in the application which was developed for the diagnosis of this disease. These datasets were divided into 80-20% training and testing data respectively within the framework of 10-fold cross validation technique. Training data as the input data were sent to the Random Forest, Logistic Regression and Artificial Neural Networks classifier algorithms. After this step, performances of these classifier algorithms were evaluated on testing data. Also, new data analysis was carried out. According to the results obtained, Artificial Neural Networks is more successful than Random Forest and Logistic Regression algorithms in analysis of new data.

Index Terms—Parkinson disease, digitized Wacom graphics tablet, Static Spiral Test, Dynamic Spiral Test, Stability Test on Certain Point.

I.I NTRODUCTION

Park inson’s disease (PD) is a neurological disorder, which is one of the most painful, dangerous and non-curable diseases, which occurs at older ages. So, it has negative impacts on patients’ daily life [1]. A common disease of the central nervous system among the elderly [2], Parkinson’s disease (PD) is currently incurable [3]. The correct diagnosis of this disease is very difficult [4], but proper treatment of this disease can ease the symptoms and improve the quality of life of patients significantly [3]. Its complex symptoms bring about challenges for the clinical diagnosis [2]. Thus, its efficient monitoring and management are very important [3]. Digital analysis of writing and drawing turned into a valuable research and clinical tool for the patients with essential tremor, Parkinson’s disease, dystonia, and related disorders [5]. Digitized spiral drawing correlates with motor scores and might be more delicate in early changes than subjective ratings [6]. Spiral drawing is used in assessing of tremor severity for the clinical evaluation of medical treatments [7]. Sakar et al. [8] collected a wide range of voice samples for building predictive telediagnosis and telemonitoring models. They investigated more PD-discriminative information, using well-known machine learning tools and published the dataset for researchers. In this study, the SST, DST and STCP datasets were obtained from this publicly raw data source. These datasets were divided into 80-20% training and test data respectively within the framework of 10-fold cross validation technique. The diagnosis of this disease was explored by utilizing these datasets. Next, by sending these datasets as input data to the classifier algorithms, successes of these algorithms were analyzed. The rest of this paper was organized as follows: In Section 2, related studies were examined. In Section 3 and 4, information about the machine learning algorithms and statistical analysis metrics which were used in the designed application were given respectively. In Section 5, the information about the data was given in detail. In Section 6, design process and results were presented. Finally, the conclusion was given in Section 7.

II.R ELATED W ORKS

There are numerous studies on this disease. Some of the studies are as follows: In [2], the authors proposed a new method based on a polar coordinate system with varying origin in order to quantitatively evaluate the performance in spiral drawing tasks for patients with PD. Fifteen subjects, three of whom normal subjects and twelve of whom are PD patients, were included in the designed spiral drawing experiment. The hand movements of patients were recorded by utilizing the digitized tablet respectively. The variation of origin, radius, degree and other characteristics of hand movements were evaluated by introducing a set of parameters for feature extraction. The result showed that the proposed polar coordinate system embraced good performance in the quantitative evaluation of spiral drawing. In [3], the authors proposed a machine learning model that detects bradykinesia and dyskinesia for self-monitoring the motor function in Parkinson's disease. Bradykinesia, which is the slowness of movement, is typically associated with under-medication. Dyskinesia which is involuntary movements can be the result of over-medication. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias and dyskinesias. This work combined spirography data and clinical assessments from a

longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. Over the course of three years, over 30.000 spiral-drawing measurements were involved on 65 advanced PD patients, and 86% classification accuracy and over 0.90 AUC were achieved. In [4], The authors developed a new dataset based on handwritten exams to aid diagnosis of Parkinson's Disease. The features were extracted from this dataset over two different drawings: spirals and meanders. This approach seemed to be suitable for aiding in the automatic diagnosis of this disease by means of computer vision and machine learning techniques. In [5], the authors introduced a novel adaptation of the Wacom-based method, using an Apple iPad-based system for computerized spiral analysis of hand-drawn Archimedean spirals. These spirals provide insight into movement dynamics beyond subjective visual assessment, using a Wacom graphics tablet. In [6], the authors drew spirals on a digitizing tablet, generating x, y, z data-coordinates and time data, and derived indices corresponded to overall spiral execution, shape and second order smoothness, first order zero-crossing, tightness, mean speed and variability of spiral width. All indices were significantly different between PD cases and controls, except for zero-crossing. Their model using all indices had high discriminative validity (sensitivity = 0.86, specificity = 0.81). In [7], the authors proposed new methods which provide new insight into tremor amplitude and frequency variations for computerized spiral analysis. Successful results were obtained from the digitized tablet with their implementation. In [9], the authors used a test battery consisting of self-assessments and motor tests which include tapping and spiral drawing tasks, on 9482 test occasions by 62 patients with advanced PD in a telemedicine setting. On each test occasion, three Archimedes spirals were traced. Bland-Altman analysis was used to evaluate agreement between the spiral score and the standardized manual rating. In [10], the authors investigated the spiral drawing performance as an indicator of fine motor function. Seven people with multiple sclerosis, nine younger controls, and eight older controls drew spirals on a graphics tablet at a comfortable speed and size. Analysis results demonstrated the utility of the analysis of spiral movements as an objective technique for assessing motor control degradation. In [11], the authors investigated whether a smartphone-based system can be used to quantify dexterity in PD. More specifically, they developed data-driven methods in order to quantify and characterize dexterity in PD. Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests, using a smartphone. The raw tapping and spiral data were processed and analyzed with time series analysis techniques in order to extract 37 spatiotemporal features. For each of the five scales, machine learning models were built and tested by utilizing the principal components of the features as predictors and mean ratings of the three specialists as target variables. In [12], the authors demonstrated that handwriting markers could be used for PD symptoms profiling successfully. They defined a novel metric as the normalized velocity variability. This metric is very potent in identifying pathological movement patterns. And they explored the use of a pen-and-tablet device to study differences in hand movement and muscle coordination between healthy subjects and PD patients. They applied a comprehensive machine learning approach to build a model that could classify unknown subjects based on their line-drawing performances. The results showed that this study was a good candidate for recognition of healthy and PD individuals, and promised in the context of telemedicine applications.

III.M ACHINE L EARNING A LGORITHMS

A machine learning is the process which obtained meaningful information from the data stack. There are many studies which carried out on this subject in the literature. Bhuvaneswari and Sabarathinam detected the defects in the manufactured ceramic tiles to ensure high density quality, using Artificial Neural Network [13]. Khazaee proposed a multi-class support vector machine (SVM)-based classifier for heart beat classification [14]. Nanda et al. used the Multi Layer Perceptron, Functional-link Artificial Neural Network and Legendre Polynomial Equation models for prediction of Rainfall in India. Quite accurate results were obtained with these models for complex time series model [15]. Ghiasi-Freez et al. optimized the Neural Network models for the prediction of nuclear magnetic resonance parameters in carbonate reservoir. For this, they used Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Intelligent optimization algorithms [16]. The authors carried out voice analysis for telediagnosis of Parkinson disease, using Artificial Neural Networks and Support Vector Machines [17]. Finally, Al-Tahrawi performed the Arabic text categorization by using Logistic Regression method [18].

In this study, the classification algorithms which mentioned below were used for the machine learning. Besides, 10-fold cross validation technique was applied to improve the performance of the study. Each model was built and tested with training and testing data respectively.

A. Random Forest (RF)

With the RF algorithm which introduced by Breiman [19], the tree structure is constituted by determining the number of trees and the samples will be used in each node. The classification of new dataset is performed by selecting the trees, which have the most votes in these trees [20].

B. Logistic Regression (LR)

LR is a regression method that helps to classify and assign the binary outcome variable. For example, a relationship between the age and the presence or absence of any disease is investigated by utilizing this method [21]. It is the most popular model in statistical modeling

of binary response Y and quantitative explanatory variables [22].

C. Artificial Neural Networks (ANN)

A neural network is a machine. It is designed in order to perform the human brain functions [23]. An ANN is a combination of interconnected simple processing elements, units or nodes. The ability of the network depends on a number of weights that are obtained through learning a set of training examples [24].

IV. S TATISTICAL A NALYSIS

The performances of the machine learning methods are assessed by utilizing the Accuracy (Acc) and Sensitivity (Sen) metrics. These metrics are used to show the effectiveness of the proposed methodology. The definitions of these metrics are as follows:

()()

Acc =TP + TN /TP + FP + TN + FN (1)

Sen TP /(TP FN)=+ (2)

The Acc metric, which is given in Equation 1, can be expressed as the ratio of the number of accurately diagnosed instances to the number of total instances and it is the most widely used measurement of model success. The Sen metric, which is given in Equation 2, can be expressed as the ratio of the number of the patients who were classified as correctly to total patients having PD [25]. These metrics can be calculated based on four values:

a) True Positive (TP), the number of the patients who

were classified as correctly out of the patients having PD,

b) False Negative (FN), the number of the patients

who were classified as incorrectly out of the patients having PD,

c) True Negative (TN), the number of the patients

who were classified as correctly out of the patients not having PD,

d) False Positive (FP), the number of the patients

who were classified as incorrectly out of the patients not having PD.

Fig.1. A general flowchart of the proposed approach.

V. D ATA

The publicly available dataset (1) which was constructed, using Wacom Cintiq 12WX graphics table

1

https://https://www.360docs.net/doc/f08229884.html,/ml/datasets/Parkinson+Disease+Spiral+Drawi

ngs+Using+Digitized+Graphics+Tablet

consists of two different groups of handwriting samples (62 people with Parkinson (PWP) and 15 healthy individuals) from the Department of Neurology in Cerrahpasa Faculty of Medicine, Istanbul University. There are three different kinds of tests developed for the data collection via graphics tablet. They are as follows: Static Spiral Test (SST): Three wound Archimedean spirals appears on the graphics tablet using the software

and patients were asked to retrace the same spiral as much as they can, using the digital pen in this test. Dynamic Spiral Test (DST): The purpose of this test is to determine the change in patient's drawing performance and pause times since it is more difficult to retrace the Archimedean spiral in this case. It was observed that most of the patients continued drawing but nearly all of them lost the pattern as a result of this test. Stability Test on Certain Point (STCP): There is a certain red point in the middle of the screen and the subjects are asked to hold the digital pen on the point without touching the screen in a certain time in this test. The purpose of this test is to determine the patient's hand stability or hand tremor level [8, 20].

VI.D ESIGN P ROCESS AND R ESULTS

Figure 1 describes the flowchart of the proposed approach for diagnosis of the PD. This study was carried ou t on the Python 2.7 platform imported ―scikit-learn‖ machine learning library. As shown in the flowchart, the datasets were obtained by reading data directory which includes control (the patients non-having PD) and parkinson (the patients having PD) directories for the SST, DST and STCP. The information obtained from these directories was presented in Table 1 and 2. These datasets were divided into 80-20% training and testing data respectively within the framework of 10-fold cross-validation technique. Training data as the input data were sent to the RF, LR and ANN classifier algorithms. Each classifier predicted whether the subject had PD or not. After this step, performances of these classifier algorithms were evaluated on testing data.

Table 1. Information of parkinson and control directories in data

directory.

a) Evaluation metrics for the SST

b) Evaluation metrics for the DST

c) Evaluation metrics for the STCP

Fig.2. Classification results for the SST, DST and STCP datasets. The Acc and Sen metrics for each learning were given in Figure 2. According to this figure, experimental results are as follows:

The best performance was achieved with the RF algorithm for each three datasets based on the Acc metrics. That is to say, the RF classifier is more successful than others for the SST and DST in terms of this metric.

On the other hand, the RF and LR classifiers are more successful than ANN classifier for the STCP.

The RF and ANN classifiers but LR are the best for all datasets based on the Sen metric.

In addition, new data analysis was carried out by reading the data in the test_data directory in line with this information. Only the information of patients having PD is available in test dataset, and the number of them is 87455. Therefore, Acc metrics are the same as the Sen metrics. The obtained results which were given in the 3-column and 4-row confusion matrix were presented in Table 3. According to this table, experimental results on testing data are as follows:

The Acc metrics are 100.0%, 100.0% and 100.0% on the SST, DST and STCP datasets respectively by performing with the ANN. That is, the ANN correctly classified all of the patients having PD for each dataset. The Acc metrics are 52.90%, 51.94% and 99.74% on the SST, DST and STCP datasets respectively by performing with the RF algorithm. It can be said that the RF algorithm is quite successful for STCP. Namely, the RF algorithm was classified correctly 46268 out of 87455 PD for SST. It classified correctly 27004 out of 51992 patients for DST and classified 62163 out of 62327 patients for STCP.

The Acc metrics are 50.37%, 57.98% and 86.87% on the SST, DST and STCP datasets respectively by performing with the LR algorithm. The LR algorithm was classified correctly 44052 out of 87455 PD for SST. It classified correctly 30143 out of 51992 patients for DST and classified 54142 out of 62327 patients for STCP. It can be seen that RF and LR algorithms are quite successful for STCP. However, they are not successful for the other datasets, SST and DST.

It is interesting to note that the ANN algorithm is more successful in the classification of Table 2 data (test_data directory), while the RF algorithm in the classification of Table 1 data (data directory) is ahead.

Table 2. PD information in ―test_data‖ directory

Table 3. Classification results for test data.

VII.C ONCLUSION

PD is a neurological disorder, which is one of the most painful, dangerous and non-curable diseases, which occurs at older ages. So, it has negative impacts on patients’ daily life. Static Spiral Test, Dynamic Spiral Test and Stability Test on Certain Point records were used in the application which was developed for the diagnosis of this disease. These records, which were obtained from the raw-dataset, were divided into 80-20% training and testing data respectively within the framework of 10-fold cross validation technique. Training data as the input data were sent to the RF, LR and ANN classifier algorithms. After this step, performances of these classifier algorithms were evaluated on testing data. Moreover, new data analysis was carried out. According to the results obtained, it is interesting to note that the ANN algorithm has proved more successful in the classification of Table 2 data, while the RF algorithm in the classification of Table 1 data is ahead. Namely, ANN algorithm is more successful than RF and LR algorithms for the analysis of new data.

A CKNOWLEDGMENT

The author would like to thank the UCI Machine Learning Repository for providing the Parkinson Disease Dataset.

R EFERENCES

[1]G. Yadav, Y. Kumar and G. Sahoo, ―Predication of

Parkinson's disease using data mining methods: a

comparative analysis of tree, statistical, and support vector

machine classifiers,‖ Indian J Med Sci, vol. 65, pp. 231-

242, 2011.

[2]M. San Luciano, C. Wang, R.A. Ortega, Q. Yu, S.

Boschung, J. Soto-Valencia, et al., ―Digitized Spiral

Drawing: A Possible Biomarker for Early Parkinson’s

disease,‖ PLoS One, vol. 11, e0162799, 2016.

[3]J. Westin, S. Ghiamati, M. Memedi, D. Nyholm, A.

Johansson, M. Dougherty, T. Groth, ―A new computer

method for assessing drawing impairment in Parkinson's

disease,‖ J Neurosci Methods, vol. 130, pp. 143-148, 2010.

[4]M.G. Longstaff, and R.A. Heath, ―Spiral drawing

performance as an indicator of fine motor function in

people with multiple sclerosis,‖Human Movement

Science, vol. 25, pp. 474-491, 2006.

[5]J.A. Sisti, B. Christophe, A.R. Seville, A.L. Garton, V.P.

Gupta, A.J. Bandin, Q. Yu, S.L. Pullman, ―Computerized

spiral analysis using the iPad,‖ Journal of Neuroscience

Methods, vol. 275, pp. 50-54, 2017.

[6]M. Wang, B. Wang, J. Zoua, M. Nakamura, ―A new

quantitative evaluation method of spiral drawing for

patients with Parkinson’s disease based on a polar

coordinate system with varying origin,‖ Physica A:

Statistical Mechanics and its Applications, vol 391, pp.

4377-4388, 2012.

[7] A.P. Legrand, I. Rivals, A. Richard, E. Apartis, E. Roze,

M. Vidailhet, S. Meunier, E. Hainque, ―New insight in

spiral drawing analysis methods –Application to action

tremor quantification,‖ Clinical Neurophysiology, vol.

128, pp. 1823-1834, 2017. [8] B.E. Sakar, M.E. Isenkul, C.O. Sakar, A. Sertbas, F.

Gurgen, S. Delil, H. Ap aydin, O. Kursun, ―Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings,‖ Journal of Biomedical and Health Informatics, vol. 17, pp. 828–834, 2013.

[9]S. Aghanavesi, D. Nyholm, M. Senek, F. Bergquist, M.

Memedi, ―A sma rtphone-based system to quantify dexterity in Parkinson's disease patients,‖ Informatics in Medicine Unlocked, vol. 9, pp. 11-17, 2017.

[10] C.R. Pereira, D.R. Pereira, F.A. Silva, J.P. Masieiro, S.A.

Weber, C. Hook, J.P. Papa, ―A new computer vision-based approach to aid the diagnosis of Parkinson's disease,‖ Comput Methods Programs Biomed, vol. 136, pp. 79-88, 2016.

[11] A. Sadikov, V. Groznik, M. Mo?ina, J. ?abkar, D.

Nyholm, M. Memedi, D. Georgiev, ―Feasibility of spirography features for objective assessment of motor function in Parkinson's disease,‖ Artificial Intelligence in Medicine, in press, 2017.

[12] C. Kotsavasiloglou, N. Kostikis, D. Hristu-Varsakelis, M.

Arnaoutoglou, ―Machine learning-based classification of simple drawing movements in Parkinson's disease,‖ Biomedical Signal Processing and Control, vol. 31, pp.

174-180, 2017.

[13]S. Bhuvaneswari, J. Sabarathinam, ―Def ect Analysis

Using Artificial Neural Network,‖ I.J. Intelligent Systems and Applications, vol. 05, pp. 33-38, 2013.

[14] A. Khazaee, ―Heart Beat Classification Using Particle

Swarm Optimization,‖I.J. Intelligent Systems and Applications, vol. 06, pp. 25-33, 2013.

[15]S.K. Nanda, D.P. Tripathy, S.K. Nayak, S. Mohapatra,

―Prediction of Rainfall in India using Artificial Neural Network Models,‖I.J. Intelligent Systems and Applications, vol. 12, pp. 1-22, 2013.

[16]J. Ghiasi-Freez, A. Hatampour, P. Parvasi, ―Application

of Optimized Neural Network Models for Prediction of Nuclear Magnetic Resonance Parameters in Carbonate Reservoir Rocks,‖I.J. Intelligent Systems and Applications, vol. 06, pp. 21-32, 2015.

[17]Saloni, R. K. Sharma, A. K. Gupta, ―Voice Analysis for

Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines,‖I.J.

Intelligent Systems and Applications, vol. 06, pp. 41-47, 2015.

[18]M.M. Al-Tahrawi, ―Arabic Text Categorization Using

Logistic Regression,‖I.J. Intelligent Systems and Applications, vol. 06, pp. 71-78. 2015.

[19]L. Breiman, ―Random forests‖, Mach Learn, vol. 45, pp.

5-32, 2001.

[20]O. Akar, and O. Gungor, ―Classification of multispectral

images using Random Forest algorithm,‖ Journal of Geodesy and Geoinformation, vol. 1, pp. 139-146, 2012.

[21]S. Lemeshow, and D. Hosmer, ―Applied logistic

regression‖, 2nd ed. New York, USA: Wiley, 2000. [22] A. Agresti, ―An introduction to categorical data analysis‖,

2nd ed. New Jersey, USA: Wiley, 2007.

[23]S. Haykin, ―Neural Networks and Learning Machines‖,

3rd ed., Prentice Hall, New York.

[24]K. Gurney, ―An introduction to neural networks‖, London

and New York, UCL Press, 1997.

[25] A. Baratloo, M. Hosseini, A. Negida, and G.E. Ashal,

―Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity,‖Emerg (Tehran), vol. 3, pp.

48-49, 2015.

[26]M.E. Isenkul, B.E. Sakar, O. Kursun, ―Improved spiral

test using digitized graphics tablet for monitoring

Parkinson's disease,‖ The 2nd International Conference on e-Health and Telemedicine (ICEHTM-2014), pp. 171-175, 2014.

Authors’ Profiles

Kemal AKYOL

He received his B.Sc. in Computer Science

Department from Gazi University,

Ankara/Turkey in 2002. He received his

M.Sc. degree from Natural and Applied

Sciences, Karabuk University,

Karabuk/Turkey and Ph.D. degree from same

department. His research interests include data mining, decision support systems and expert systems. How to cite this paper: Kemal Akyol, "A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.12, pp.45-51, 2017. DOI: 10.5815/ijitcs.2017.12.06

帕金森病的诊断与鉴别诊断

帕金森病的诊断与鉴别诊断 帕金森病(PD)由英国学者James Parkinson(1817)首先描述,又称为震颤麻痹(Shaking palsy)。临床以震颤、肌强直、运动减少和姿势障碍为特征,是中老年人的常见病。 1、帕金森病的病因尚未清楚 1.3 发病机制: 目前认为自由基生成、氧化反应增强、谷胱甘肽含量降低以及线粒体功能异常、内源性和外源性毒物等因素,导致黑质DA神经元细胞变性,其中线粒体功能异常起主导作用。应用分子生物学技术对有关的酶和基因片段的研究亦支持这种看法。研究发现黑质组织线粒体复合物Ⅰ基因缺陷、遗传异常,会导致易患性,在毒物等因素的作用下,黑质复合物Ⅰ活性艿接跋欤 贾律窬 赴 湫浴⑺劳觥R虼? 大多数学者认为认为遗传和环境因素可能在PD发病中起主要作用。 鉴别诊断: 肌炎型炎性假瘤:典型表现为眼外肌肌腹和肌腱同时增粗,上直肌和内直肌最易受累,眶壁骨膜与眼外肌之间的低密度脂肪间隙为炎性组织取代而消失。 动静脉瘘(主要为颈动脉海绵窦瘘):常有多条眼外肌增粗,眼上静脉增粗,增强后增粗的眼上静脉增强尤为明显,一般容易鉴别,如在CT 上鉴别困难,可行DSA 确诊。 转移瘤:眼外肌有时可发生转移瘤,表现为眼外肌呈结节状增粗并可突入眶内脂肪内,如果表现不典型,鉴别困难,可行活检鉴别。 淋巴瘤:眼外肌肌腹和肌腱均受累,一般上直肌或提上睑肌较易受累,此肿瘤与炎性假瘤在影像上较难鉴别,活检有助于鉴别。 【影像学】 CT 和MRI 均能较好地显示增粗的眼外肌,但在MRI 上很容易获得理想的冠状面和斜矢状面,显示上直肌和下直肌优于CT ,而且根据MRI 信号可区分病变是炎性期还是纤维化期,对于选择治疗方法帮助更大。 2 、帕金森病的特异性病理指标-路易氏体 主要为黑质致密区含黑色素的神经元变性、丢失及细胞破碎、色素颗粒游离;细胞内出现玻璃体样同心样包涵体(Lewy小体)(见图1);包涵体也见于兰斑、迷走神经背核、下丘脑、中缝核、交感节等。星形胶质细胞增生、胶质纤维增生。黑质神经元细胞内铁含量增高,也主要在黑质致密部,即神经元变性、坏死的部位。帕金森的特异性病理指标-路易氏体,见图1 3 、帕金森病的临床诊断 3.1 发病年龄:中老年隐袭性发病, ≥50 岁占总患病人数的90 %。 3.2 首发症状以多动为主要表现者易于早期诊断。首发症状依次为:震颤

帕金森病常用评分量表

(一)统一帕金森病评分量表(UPDRS)又名帕金森氏病综合评分量表unified Parkinson‘s disease rating scale I.精神、行为和情绪 1.智能损害 0=正常 1=轻度记忆力下降,无其他智能障碍。 2=中度记忆力下降,伴有定向障碍。中等程度的处理复杂问题的能力下降。轻度自理能力下降,有时需别人提示。 3=严重记忆力下降,伴时间和地点定向障碍,处理问题的能力严重受损。 4=严重记忆力损害,仅保留对自身的判断能力。不能自行判断和处理问题。个人生活需他人照料,不能单独生活。 2.思维障碍(痴呆和药物中毒) 0=无思维障碍。 1=有生动的梦境。 2=有不严重的幻觉,但洞察力保留。 3=幻觉或妄想,缺乏洞察力,可能影响日常生活。 4=持续性的幻觉、妄想或明显精神障碍,不能自理。 3.抑郁 0=无抑郁。 1 =经常悲伤或内疚,但持续时间短。 2=持续性抑郁,可持续一周或更长时间。 3=持续性的抑郁和自主神经症状(失眠、厌食、体重下降、缺乏兴趣) 4=持续性的抑郁和自主神经症状,有自杀意图或倾向。 4.主动性 0=正常。 1=与正常比缺乏主见,显得被动。 2=缺乏主动性,对某些活动缺乏兴趣。 3=缺乏主动性,对日常活动缺乏兴趣。 4=完全没有兴趣,退缩。 II.日常生活能力(“关”和“开”期) 5.语言 0=正常; 1=轻度受影响,但理解无困难。 2=中度受影响,有时需要重复表达。

3=严重受影响,经常需要重复表达。 4=大多数时候听不懂。 6.流涎 0=正常。 1=轻度,口水多,可能有夜间流涎。 2=中度,口水多,少量流涎。 3=明显,口水很多,中量流涎。 4=严重流涎,需不断用纸或手帕揩拭。 7.吞咽 0=正常。 1=很少呛咳。 2=有时呛咳。 3=需要进软食。 4=需留置胃管或胃造瘘喂食。 8.书写和笔迹 0=正常。 1=轻度缓慢或字迹变小。 2=中度缓慢或字迹变小,但各字均可辨认。 3=严重影响,字迹中并非所有字都可辨认。 4=大多数字不能辨认。 9.刀切食物和使用餐具 0 =正常。 1=有点缓慢和笨拙,但不需帮助。 2=虽然缓慢而笨拙,但能切大多数食物,需一些帮助。3=需别人切食物、挟莱,但能缓慢进食。 4=需要喂食。 10.穿衣 0=正常。 1=有些缓慢,但不需要帮助。 2=偶尔需要帮助其系钮扣或将手臂放入衣袖。 3=需要相当多的帮助,仅能单独完成少数动作。 4 =完全需要帮助。 11.卫生 0 =正常。 1 =有些慢,但不需帮助。 2 =淋浴或坐浴需人帮助,或在帮助下缓慢完成。

帕金森氏病综合评分量表UPDRS教学教材

帕金森氏病综合评分量表(UPDRS) UPDRS是目前国际上普遍采用的量表,下列项目(1-17)每一项的计分值用0,1,2,3,4,5五个等级。分值越高,PD症状越严重。 1,智力损害 0=无 1=轻度智力损害,持续遗忘,能部分回忆过去的事件,但无其他困难。2=中等记忆损害,有定向障碍,解决复杂问题有中等程度的困难,在家中生活功能有轻度但肯定的损害,偶然需要提示。3=严重记忆损害伴时间及地点定向障碍,解决问题有严重困难。 4=严重记忆损害,仅保留人物定向,不能做出判断或解决问题,生活需要帮助,不能一人独处。 2,思维障碍(由于痴呆或药物中毒) 0=无 1=有生动的梦境 2=良性幻觉,但仍有自知力 3=偶有或常有的幻觉或妄想,无自知力,可能影响日常活动 4=持续的幻觉,妄想或明显的精神病,不能自我照顾。 3,抑郁 0=无 1=悲观和内疚时间比正常多,持续时间不超过数日或数周 2=持续抑郁,一周或更长 3=持续抑郁伴自主神经症状 4=持续抑郁伴自主神经症状和自杀念头或意愿。 4,主动性 0=正常 1=缺乏自信,比较被动 2=对选择性(非常规)活动无兴趣或动力3=对每天的(常规)活动无兴趣或动力 4=退缩,完全无主动性 5,言语 0=正常 1=轻度受影响,仍能听懂 2=中度受影响,有时重复后才能听懂 3=严重受影响,经常重复后才听懂 4=经常听不懂 6,唾液分泌 0=正常 1=口腔内分泌物略多,可有夜间流涎 2=中等程度的唾液分泌增多,可能有轻微流涎 3=明显唾液增多伴流涎4=明显流涎,需持续用纸巾或手帕擦拭 7,吞咽 0=正常 1=极少呛咳 2=偶然呛咳 3=需进软食 4=需胃管或胃造瘘进食 8,书写 0=正常 1=轻度缓慢或字体变小 2=中度缓慢或字体变小,所有字迹均清楚 3=严重受影响,部分字迹不清楚 4=大多数字迹不清楚 9,刀切食物和使用餐具 0=正常 1=稍慢笨拙,不需要帮助 2=慢和笨拙,能切大多食物,需某种程度帮助 3=需他人切食物,还能自己缓慢进食 4=需要喂食 10,穿衣 0=正常1=略慢不需要帮助 2=偶然需要帮助扣扣及将手臂伸进衣袖里 3=需要相当多的帮助,但还能独立做某些事情 4=完全需要帮助 11,个人卫生 0=正常 1=稍慢,不需要帮助 2=淋浴或盆浴需要帮助,做个人卫生很慢 3=洗脸,刷牙,梳头洗澡均需要帮助 4=留置导尿或其他机械帮助 12,翻身和整理床单 0=正常 1=稍慢笨拙,不需要帮助 2=能独立翻身或整理床单,但很困难 3=能开始翻身或整理床单,不能独自完成 4=完全需要帮助 13,跌跤 0=无 1=偶有 2=有时有,少于每日一次 3=平均每日一次 4=多于每日一次 14,行走中冻结 0=无 1=少见,可有启动困难 2=有时有冻结 3=经常有,偶因冻结跌交4=经常因冻结跌交15,行走 0=正常 1=轻度困难,上臂不摆或有拖步倾向 2=中度困难,稍微或需要帮助 3=严重行走困难,需要帮助 4=有帮助也不能行走 16,震颤 0=无 1=轻,不常有 2=中,令人烦恼 3=严重,许多活动受影响 4=更严重,多数活动受影响 17,与帕金森病有关的感觉主诉 0=无 1=偶然有麻木,针刺感或轻微疼痛 2=经常有麻木,针刺感或轻微疼痛,并不难受 3=经常有疼痛 4=极度疼痛感 18-31项目每一项记分值用0, 0.5,1,1.5,2,2.5,3,3.5,4;个等级的四个等级中有0.5的高低之差.得分越

帕金森氏病综合评分量表(UPDRS)

统一帕金森病评定量表 Unified Parkinson's Disease Rating Scale,UPDRS 3.0版 Ⅰ. 精神,行为和情绪 1. 智力损害 0=无 1=轻微智力损害,持续健忘,能部分回忆过去的事件,无其他困难 2=中等记忆损害,有定向障碍,解决复杂问题有中等程度的困难,在家中生活功能有轻度但肯定的损害,有时需要鼓励 3=严重记忆损害伴时间及(经常有)地点定向障碍,解决问题有严重困难 4=严重记忆损害,仅保留人物定向,不能作出判断或解决问题,生活需要更多的他人帮助 2. 思维障碍(由于痴呆或药物中毒) 0=无 1=生动的梦境 2=“良性”幻觉,自知力良好 3=偶然或经常的幻觉或妄想, 无自知力, 可能影响日常活动 4=持续的幻觉、妄想或富于色彩的精神病, 不能自我照料 3. 抑郁 0=无 1=悲观和内疚时间比正常多,持续时间不超过1周 2=持续抑郁(1周或以上) 3=持续抑郁伴自主神经症状(失眠、食欲减退、体重下降、兴趣降低) 4=持续抑郁伴自主神经症状和自杀念头或意愿

4. 动力或始动力 0=正常 1=比通常缺少决断力(assertive),较被动 2=对选择性(非常规)活动无兴趣或动力 3=对每天的(常规)活动无兴趣或动力 4=退缩,完全无动力 Ⅱ. 日常生活活动(确定“开或关”) 5. 言语 (接受) 0=正常 1=轻微受影响,无听懂困难 2=中度受影响,有时要求重复才听懂 3=严重受影响,经常要求重复才听懂 4=经常不能理解 6. 唾液分泌 0=正常 1=口腔内唾液分泌轻微但肯定增多,可能有夜间流涎2=中等程度的唾液分泌过多,可能有轻微流涎 3=明显过多的唾液伴流涎 4=明显流涎,需持续用纸巾或手帕擦拭 7. 吞咽 0=正常 1=极少呛咳

帕金森病的运动症状波动和异动症

帕金森病的运动症状波动和异动症 引言—在接受左旋多巴治疗5年的患者中有多达50%的患者出现运动症状波动(motor fluctuations, MF)和异动症[]。这些症状在起病早的(例如,起病年龄小于50岁)帕金森病(Parkinson disease, PD)患者中尤其常见;并且这些症状仅见于服用左旋多巴的患者,服用其他抗帕金森病药物不会产生症状波动和异动症。(参见) 在左旋多巴治疗的早期阶段患者通常对药物反应良好。然而,随着疾病进展,左旋多巴的效果在每剂用药后约4小时开始减退,导致患者预知需要下次剂量用药。该现象可能由一个观察结果来解释,即在病程早期多巴胺神经末梢还能够储存和释放多巴胺,随着疾病进一步进展和多巴胺神经末梢变性增加,基底神经节中的多巴胺浓度更加依赖于血浆左旋多巴水平。由于左旋多巴的半衰期为90分钟,同时肠道对左旋多巴的吸收常常无法预测,造成左旋多巴血浆水平可能出现不规律的波动。 MF为“开”期与“关”期间的变动。“开”期患者对药物反应良好;“关”期患者则又出现基础帕金森综合征的症状。 异动症表现为异常的不自主运动,一般表现为舞蹈样动作或肌张力障碍性症状,更严重时可能表现为投掷样或肌阵挛性动作。异动症通常在患者“开”期时出现,可能偶尔以痛性肌张力障碍的形式出现在患者的“关”期,尤其在晨醒时,此时由于整夜没有服药,服药间隔时间过长,造成戒断反应,出现了足肌张力障碍性内旋(通常在帕金森病症状较重的一侧)。 本专题将讨论晚期PD患者出现MF的药物治疗。PD的一般治疗方法将单独讨论。(参见) 手术是晚期PD患者的另一种治疗选择,因为对有晚期典型的PD伴MF的特定患者,当药物治疗无法进一步改善症状时,对丘脑底核或苍白球的双侧深部脑刺激似乎能改善患者的运动功能。晚期PD患者的手术治疗将在别处讨论。(参见) 疗效减退现象—晚期PD患者在服用一剂左旋多巴后不到4小时就开始感觉到疗效减退或剂末效应。 改变左旋多巴给药—如果患者用药剂量相对较小且没有副作用,最初可通过增加左旋多巴的剂量来治疗患者的疗效减退[]。然而,加大药物剂量常会加重副作用,却不会有效地增加药物剂量的持续作用时间。通常来说,缩短用药间隔同时每次服用较低剂量的药物是一种更有效的方法。然而常常很难精确地逐渐调整药物剂量;并且一些患者开始出现“全或无”反应,因此每次服用的较低剂量导致患者没有明显的临床反应。这种现象的出现是因为在疾病晚期,药物反应所需的阈值高于疾病早期。 当采用片剂难以调整给药剂量和给药间隔时,则偶尔会给予患者液体息宁()。然而,这种方法通常不实用,因为息宁不溶于水,而且目前没有商品化的液体息宁制剂可供使用。现已有液体息宁每日供应的制备说明,但这种方法最好留给专业人士使用[]。 如果可获得左旋多巴-凝胶输注液,可通过经皮胃空肠造瘘置管泵给予该药物来取代口服左旋多巴-卡比多巴,以缩短“关”期。一项为期12周的双盲随机对照试验为该做法提供了支持,该试验纳入了71例晚期PD患者[]。结果发现,与间断给予口服左旋多巴-卡比多巴速释剂相比,持续输注左旋多巴-卡比多巴凝胶引起了运动症状“关”期平均时间vs 小时)以及无令人困扰异动症的“开”期平均时间vs 小时)改善显著更多。该方法的缺点包括需要手术经皮置管以及该置管的相关不良事件。 左旋多巴持续释放(sustained-release, SR)剂型(如息宁控释片)可能有助于疗效减退现象的早期阶段,并且可能使左旋多巴的效力持续时间在一整天中额外增加多达90分钟[]。但是,以上结论的证据并不一致[],美国神经病学会(American Academy of Neurology, AAN)在2006年发布的实践参数总结到:与速释剂型相比,SR剂型并没有减少“关”期时间[]。此外,息宁控释片的吸收不如息宁速释片好;因此,可能需要增加大约30%的个体剂量以达到相同的临

统一帕金森病评定量表(规范版)

统一帕金森病评定量表(UPDRS ) 姓名: 性别: 年龄: 住院号: 联系方式: 评定时间: I .精神,行为和情绪 1. 智力损害 0 =无 1=轻微智力损害,持续健忘,能部分回忆过去的事件,无其他困难 2=中等记忆损害,有定向障碍,解决复杂问题有中等程度的困难,在家中生活功能有轻度但肯定的损害,有时需要鼓励 3=严重记忆损害伴时间及(经常有)地点定向障碍,解决问题有严重困难4=严重记忆损害,仅保留人物定向,不能作出判断或解决问题,生活需要更多的他人帮助 2. 思维障碍(痴呆或药物中毒) 0=无 1=生动的梦境 2=“良性”幻觉,自知力良好 3=偶然或经常的幻觉或妄想, 无自知力, 可能影响日常活动4=持续的幻觉、妄想或富于色彩的精神病, 不能自我照料 3. 抑郁 0=无 1=悲观和内疚时间比正常多,持续时间不超过1 周2=持续抑郁(1 周或以上)3=持续抑郁伴自主神经症状(失眠、食欲减退、体重下降、兴趣降低)4=持续抑郁伴自主神经症状和自杀念头或意愿 4. 动力或始动力 0=正常 1=比通常缺少决断力(assertive),较被动 2=对选择性(非常规)活动无兴趣或动力 3=对每天的(常规)活动无兴趣或动力 4=退缩,完全无动力 n .日常生活活动(“关”和“开”期) 5. 言语(接受) 0=正常 1=轻微受影响,无听懂困难 2=中度受影响,有时要求重复才听懂 3=严重受影响,经常要求重复才听懂 4=经常不能理解 6. 唾液分泌 0=正常 1=口腔内唾液分泌轻微但肯定增多,可能有夜间流涎2=中等程度的唾液分泌过多,可能有轻微流涎 3=明显过多的唾液伴流涎4=明显流涎,需持续用纸巾或手帕擦拭 7. 吞咽 0=正常 1=极少呛咳 2=偶然呛咳 3=需进软食 4=需要鼻饲或胃造瘘进食 8. 书写

统一帕金森病评分量表

统一帕金森病评分量表(UPDRS 姓名:病案号:评价时间:□□口□年□□月□□日 0 =正常

1 =与正常比缺乏主见,显得被动。 2 =缺乏主动性,对某些活动缺乏兴趣。 3 =缺乏主动性,对日常活动缺乏兴趣。 4 =完全没有兴趣,退缩。 II ?日常生活能力(“关”和“开”期)5. 语言 0 =正常; 1 =轻度受影响,但理解无困难。 2 =中度受影响,有时需要重复表达。 3 =严重受影响,经常需要重复表达。 4 =大多数时候听不懂。 6. 流涎 0 =正常。 1 =轻度,口水多,可能有夜间流涎。 2 =中度,口水多,少量流涎。 3 =明显,口水很多,中量流涎。 4=严重流涎,需不断用纸或手帕揩拭。 7. 吞咽 0 =正常。 1 =很少呛咳。 2 =有时呛咳。 3 =需要进软食。 4=需留置胃管或胃造痿喂食。 8.书写和笔迹

1 =轻度缓慢或字迹变小。 2 =中度缓慢或字迹变小,但各字均可辨认。 3=严重影响,字迹中并非所有字都可辨认。 4 =大多数字不能辨认。 9. 刀切食物和使用餐具 0 =正常。 1=有点缓慢和笨拙,但不需帮助。 2=虽然缓慢而笨拙,但能切大多数食物,需一些帮助3=需别人切食物、挟莱,但能缓慢进食。 4 =需要喂食。 10. 穿衣 0 =正常。 1=有些缓慢,但不需要帮助。 2 =偶尔需要帮助其系钮扣或将手臂放入衣袖。 3 =需要相当多的帮助,仅能单独完成少数动作。 4 =完全需要帮助。 11. 卫生 0 =正常。 1 =有些慢,但不需帮助。 2=淋浴或坐浴需人帮助,或在帮助下缓慢完成。 3=洗面、刷牙、梳头或去洗手间均需人帮助。 4=需用导尿管及其他便器。 12.床上翻身和盖被褥

统一帕金森病评定量表UPDRS

统一帕金森病评定量表UPDRS

统一帕金森病评定量表UPDRS 一、精神、行为和情绪 1.智力损害 0=无 1=轻微智力损害,持续健忘,能部分回忆过去的事件,无其他困难 2=中等记忆损害,有定向障碍,解决复杂问题有中等程度的困难,在家中生活功能有轻度但肯定的损害,有时需要鼓励 3=严重记忆损害伴有时间及(经常有)地点定向障碍,解决问题有严重困难 4=严重记忆损害,仅保留人物定向,不能作出判断或解决问题,生活需要更多的他人帮助 2.思维障碍(由于痴呆或药物中毒) 0=无 1=生动的梦境 2=“良性”幻觉,自知力良好 3=偶然或经常的幻觉或妄想,无自知力,可能影响日常活动 4=持续的幻觉、妄想或富于色彩的精神病,不能自我照料

3.抑郁 0=无 1=悲观和内疚时间比正常多,持续时间不超过1周 2=持续抑郁(1周或以上) 3=持续抑郁伴有自主神经症状(失眠、食欲减退、体重下降、兴趣降低) 4=持续抑郁伴有自主神经症状和自杀念头或意 愿 4.动力或始动力 0=正常 1=比通常缺少决断力(assertixe),较被动 2=对选择性(非常规)活动无兴趣或动力 3=对每天的(常规)活动无兴趣或动力 4=退缩,完全无动力 二、日常生活活动(确定“开”或“关”)) 5.言语(接受) 0=正常 1=轻微受影响,无听懂困难 2=中度受影响,有时要求重复才听懂 3=严重受影响,经常要求重复才听懂 4=经常不能理解

6.唾液分泌 0=正常 1=口腔内唾液分泌轻微但肯定增多,可能有夜间流涎 2=中等程度的唾液分泌过多,可能有轻微流涎3=明显过多的唾液伴流涎 4=明显流涎,需持续用纸巾或手帕檫拭 7.吞咽 0=正常 1=极少呛咳 2=偶然呛咳 3=需要软食 4=需要鼻饲或胃造瘘进食 8.书写 0=正常 1=轻微缓慢或字变小 2=中度缓慢或字变小,所有字迹均清楚 3=严重受影响,不是所有字迹均清楚 4=大多数字迹不清楚 9切割食物和使用餐具 0=正常 1=稍慢和笨拙,但不需要帮助

(完整word版)统一帕金森病评定量表UPDRS

统一帕金森病评定量表UPDRS 一、精神、行为和情绪 1.智力损害 0=无 1=轻微智力损害,持续健忘,能部分回忆过去的事件,无其他困难 2=中等记忆损害,有定向障碍,解决复杂问题有中等程度的困难,在家中生活功能有轻度但肯定的损害,有时需要鼓励 3=严重记忆损害伴有时间及(经常有)地点定向障碍,解决问题有严重困难 4=严重记忆损害,仅保留人物定向,不能作出判断或解决问题,生活需要更多的他人帮助2.思维障碍(由于痴呆或药物中毒) 0=无 1=生动的梦境 2=“良性”幻觉,自知力良好 3=偶然或经常的幻觉或妄想,无自知力,可能影响日常活动 4=持续的幻觉、妄想或富于色彩的精神病,不能自我照料 3.抑郁 0=无 1=悲观和内疚时间比正常多,持续时间不超过1周 2=持续抑郁(1周或以上) 3=持续抑郁伴有自主神经症状(失眠、食欲减退、体重下降、兴趣降低) 4=持续抑郁伴有自主神经症状和自杀念头或意愿 4.动力或始动力

0=正常 1=比通常缺少决断力(assertixe),较被动 2=对选择性(非常规)活动无兴趣或动力 3=对每天的(常规)活动无兴趣或动力 4=退缩,完全无动力 二、日常生活活动(确定“开”或“关”)) 5.言语(接受) 0=正常 1=轻微受影响,无听懂困难 2=中度受影响,有时要求重复才听懂 3=严重受影响,经常要求重复才听懂 4=经常不能理解 6.唾液分泌 0=正常 1=口腔内唾液分泌轻微但肯定增多,可能有夜间流涎 2=中等程度的唾液分泌过多,可能有轻微流涎 3=明显过多的唾液伴流涎 4=明显流涎,需持续用纸巾或手帕檫拭 7.吞咽 0=正常 1=极少呛咳 2=偶然呛咳

帕金森病的症状和体征

帕金森病的症状和体征 【关键词】帕金森病 帕金森病(PD)的症状和体征是神经系统疾病中最多的,但教科书大多只强调三大症状,即震颤、强直和运动迟缓。事实上,PD其他的一些临床症状和体征,认识它和掌握它,对诊断更有直接的帮助。 在黑质多巴胺能通路上的任何病损,均可造成PD的结果,破坏可以有多种形式,常见的有缺氧、缺血、炎症性、化学性和药理性,其最后结果是使多巴爱减少,多巴胺受体阻滞而产生症状,另外急性或慢性纹状体黑质内的神经元破坏,如脑积水、脑瘤等,还有一些非遗传性退行性疾病也可引起PD。因此,其病因可分为:(1)原发性(idiopathic);(2)继发性(symptomatic);(3)PD叠加综合征;(4)遗传神经退行性病。 PD是一类清楚的临床及病理表现的疾病单元,占临床上所有的运动障碍的75%,病理特征是黑质黑色素细胞的消失和Lewy body 的发现。临床学家考虑主要症状中震颤、强直和运动减少3点中具有2点即可诊断PD,另外,对L-Dopa有良好反应则更提示为PD,姿势平衡障碍更多见于Parkinsonism中,即使如此诊断有时也很困难。如对L-Dopa有良好的反应,也不一定就是PD,对此反应不好的也可能是PD,没有震颤的也可能是PD。任何临床的诊断必需是灵活且能足以满足一些一般情况以下的例外状态。因此,临床要认识PD的诸多症状和体征,这对诊断PD是绝对必要的。 1 起病

PD起病平均年龄50~60岁,进展慢,整个病程10~20年。有5%的病人起病在40岁以前,称青年型。在PD第一症状产生前已有黑质神经元丧失至少已达80%。起病隐袭,前驱症状常是非特异性的,如疲劳、身体不适、人格改变,这些症状可以发生在第一运动症状发生之前的数年。一般病人第一症状常发生在精细运动中,如感到躯体运动无力、轻度共济失调(病人常诉my golf game is off)或书写困难,有时有疼痛主述,如肩臂肌肉紧张(起病初一般不对称),因此早期诊断困难。间隙性震颤(有时限于几个手指),精细检查时的齿轮样强直,或许是最早的线索,许多病人一直要到第一运动症状出现时才能诊断。 2 主要表现 2.1 震颤 静止性震颤最早由Parkinson本人描述,系本病最易认识的体征,75%的病人以此症状为首发,常一侧肢体远端先开始,有时可仅一个手指持续数年后才出现其他症状,一般为节律性、交替性、特征性的为搓丸样震颤(pi-rolling tremor),频率一般3~8次/s,EMG 上可有明显表现,数年后震颤可扩展至肢体近端,然后影响至同侧下肢,最后可影响对侧肢体,至后期面、口震颤和颏部的伴随震颤也常见到。下肢受累时,当病人坐或仰卧时可出现,负重时可消失,走路时上肢震颤可增加,紧张、焦虑时也可增加,睡眠时可消失。中度的动作性震颤在疾病发展期也可见到,但早期出现则要考虑排除其他疾病。

帕金森病的诊断及治疗方法

帕金森病的诊断及治疗方法 济南脑科医院帕金森病治疗中心关良帕金森病是多见于中年以上的中枢神经系统变性疾病,是一种常见病、慢性病,又称“震颤麻痹”,男性多于女性。帕金森病本身不是一种致命的疾病,一般不影响寿命。随着治疗方法和水平的不断创新和提高,越来越多的病人能终身维持高水平的运动机能和生活质量。当然,如果患者没有得到及时和合理的治疗,很容易导致身体机能下降,甚至生活不能自理,最后出现各种并发症,如肺炎、泌尿系统感染等。病情发展到晚期将全身僵直,卧床不起,导致器官功能衰竭和生命危险。它给患者的工作、生活和家庭带来极大的痛苦。随着我国人口呈现老年化趋势,患帕金森症的比例也在增加,据统计,目前我省患者已达五万人左右。由于环境污染和某些尚未认识的原因,此病也有向中年人甚至年轻人发展的趋势。 病因 环境因素直接或间接地参与了帕金森病发病的过程,这是因为致病因素通过与职业、生活密切相关的方式和途径作用于人体,从而导致了帕金森病的发生。目前比较公认的直接影响发病的环境因素有:⑴有机化学物质。⑵水源污染:据来自一些工业化国家的报道,帕金森病的发生和流行有一定区域性,而这些区域的水中显然含有某些与致病有关的水溶性物质。⑶农药与工业污染:国外的一些实验表明,帕金森病的发病与农药的使用之间关系密切。此外,环境因素通过破坏机体内部的自由基清除系统,使体内蓄积大量的氧自由基,也间接造成帕金森病的发生。 现在病因研究认为主要是杀虫剂、除草剂对人体的伤害,遗传不起重要作用。主要病变在黑质和纹状体。正常人脑的纹状体含有多种神经递质,其中一多巴胺及其代谢产物含量最多,多巴胺由黑质细胞产生,它为纹状体的抑制性神经递质,乙酰胆碱为一种兴奋性神经递质,在正常人身上两者是处于动态的平衡状态,而帕金森病者由于黑质细胞遭受大量坏变,多巴胺递减,并且乙酰胆碱的作用相对亢进,于是便出现了帕金森病种种症状,纹状体的多巴胺含量愈少,症状就愈重。症状 ⑴静止性震颤,手指的节律性震颤形成所谓“挫丸样动作。”⑵强直,是锥体外系性肌张力增高,为“铅管样强直”。由于肌强直可有头部前倾,关节屈曲等表现。⑶运动迟缓,可有写字过小症;起立及翻身不能,行走时以极小的步伐向前冲去,越走越快,不能即使停步或转弯,称慌张步态。面部表情极少,呈“面具脸”。流涎,严重时可有吞咽困难。⑷其它可有顽固性便秘、大量出汗、皮脂溢出等植物性神经症状表现。

帕金森病常用评分量表

(一)统一帕金森病评分量表(UPDRS )又名帕金森氏病综合评分量表un ified Park inson ‘ s disease rat ing scale

3?=?严重受影响,经常需要重复表达。 4?=?大多数时候听不懂。 6. ?流涎 0?=?正常。 1?=?轻度,口水多,可能有夜间流涎。 2?=?中度,口水多,少量流涎。 3?=?明显,口水很多,中量流涎。 4?=?严重流涎,需不断用纸或手帕揩拭。 7. ?吞咽 0?=?正常。 1?=?很少呛咳。 2?=?有时呛咳。 3?=?需要进软食。 4?=?需留置胃管或胃造痿喂食。 8. ?书写和笔迹 0?=?正常。 1?=?轻度缓慢或字迹变小。 2?=?中度缓慢或字迹变小,但各字均可辨认。 3?=?严重影响,字迹中并非所有字都可辨认。 4?=?大多数字不能辨认。 9. ?刀切食物和使用餐具 0 =?正常。 1?=?有点缓慢和笨拙,但不需帮助。 2?=?虽然缓慢而笨拙,但能切大多数食物,需一些帮助3?=?需别人切食物、挟莱,但能缓慢进食。 4?=?需要喂食。 10. 穿衣 0?=?正常。 1?=?有些缓慢,但不需要帮助。 2?=?偶尔需要帮助其系钮扣或将手臂放入衣袖。 3?=?需要相当多的帮助,仅能单独完成少数动作。 4 =?完全需要帮助。 11. 卫生 0 =?正常。

1 =?有些慢,但不需帮助。 2 =?淋浴或坐浴需人帮助,或在帮助下缓慢完成。

3 =?洗面、刷牙、梳头或去洗手间均需人帮助。 4 =?需用导尿管及其他便器。 12. 床上翻身和盖被褥 0 =?正常。 1 =?有些缓慢和笨拙,但不需要帮助。 2 =?能独自翻身或盖好被褥,但有很大困难。 3 =?有翻身和盖被褥的动作,但不能独立完成。 4 =?完全不能。 13. 跌倒(与僵住无关) 0 = ?无。 1 =?偶尔跌倒。 2 =?有时跌倒,少于1?次/天。 3 = ?平均每天跌倒1?次。 4 =?平均每天跌倒1?次以上。 14. 行走时被僵住 0 = ?无。 1 =?偶尔岀现步行中僵住,仅在起步时呈犹豫状态 (起步难或十分缓慢) 2 =?偶尔行走中出现僵住,每天少于1?次。 3 =?常有僵住,偶尔因僵住而跌倒。 4 =?经常因僵住而跌倒。 15. ?步行 0 =?正常。 1 =?轻度困难,无手臂摆动或拖步。 2 =?中度困难,很少需要帮助或不需要支撑物。 3 =?严重行走困难,需支撑物。 4 =?即使有支撑物也不能步行。 16. 震颤(身体任何部位的震颤) 0 = ?无。 1 =?轻度,不经常出现。 2 =?中度,给病人造成麻烦。 3 =?重度,干扰很多活动。 4 =?极显着,大多数活动受干扰。 17. 与帕金森综合征有关的感觉诉主诉 0 = ?无。 1 =?偶尔有麻、刺或轻度疼痛。

2019年帕金森病考试试题

帕金森病考试试题 一、A1型题(本大题33小题.每题1.0分,共33.0分。每一道考试题下面有A、 B、C、D、E五个备选答案。请从中选择一个最佳答案,并在答题卡上将相应题号的相应字母所属的方框涂黑。) 第1题 帕金森病临床表现中下列哪项不对 A 运动减少 B 静止性震颤 C 写字过大症 D 肌强直 E 慌张步态 【正确答案】:C 【本题分数】:1.0分 第2题 帕金森病以下的哪项表述是不正确的 A 多在中老年期发病 B 主要表现静止性震颤、运动迟缓、肌强直 C 常规辅助检查无特殊发现 D 早期发现,早期治疗可治愈 E 抗胆碱能药物适用于震颤明显的较年轻的患者 【正确答案】:D 【本题分数】:1.0分 第3题 帕金森病和特发性良性震颤的主要区别是 A 有家族遗传史 B 起病隐袭、缓慢进展

C 精神紧张时震颤加重 D 肌强直 E 静止性震颤 【正确答案】:D 【本题分数】:1.0分 第4题 关于帕金森病步态描述正确的是 A 联带运动减少 B 鸭步 C 醉酒步态 D 走路快 E 身体后倾易跌倒 【正确答案】:A 【本题分数】:1.0分 第5题 铅管样强直是下列哪种疾病的表现 A 有机磷中毒 B 周围神经炎 C 强直性脊柱炎 D 脑膜炎 E 帕金森病 【正确答案】:E 【本题分数】:1.0分 第6题 关于帕金森病运动减少下列哪项说法不正确A 始动困难

B 随意运动缓慢 C 精细动作尚可 D 联带运动减少 E 以上都不是 【正确答案】:C 【本题分数】:1.0分 第7题 震颤麻痹源于什么部位变性 A 纹状体 B 黑质细胞 C 红核 D 小脑 E 脑干 【正确答案】:B 【本题分数】:1.0分 第8题 关于帕金森病的三个主要体征,哪项是正确的 A 震颤,肌张力增高,慌张步态 B 震颤,面具脸,肌张力增高 C 运动减少,搓丸样动作,肌张力增高 D 震颤,肌张力增高,运动减少 E 震颤,面具脸,运动减少 【正确答案】:D 【本题分数】:1.0分 第9题 黑质纹状体系统内使左旋多巴转化为多巴胺的酶是

帕金森病的诊断及鉴别诊断

帕金森病的诊断及鉴别诊断 07中医五1班0701360 方林帕金森病是一种常见于中老年的神经系统变性疾病,多在60岁以后发病。其在临床上主要表现为患者动作缓慢,手脚或身体的其它部分的震颤,身体失去了柔软性,变得僵硬。帕金森病的病变部位在中脑。该处有一群神经细胞,叫做黑质神经元,它们合成一种叫做“多巴胺”的神经递质,其神经纤维投射到大脑的其它一些区域,如纹状体,对大脑的运动功能进行调控。当这些黑质神经元变性死亡至80%以上时,大脑内的神经递质多巴胺便减少到不能维持调节神经系统的正常功能,便出现帕金森病的症状。 帕金森病的病因有很多,流行病学调查结果发现,帕金森病的患病率存在地区差异,所以人们怀疑环境中可能存在一些有毒的物质,损伤了大脑的神经元。医学家们在长期的实践中发现帕金森病似乎有家族聚集的倾向,有帕金森病患者的家族其亲属的发病率较正常人群高一些。尽管帕金森病的发生与老化和环境毒素有关,但是并非所有老年人或暴露与于同一环境的人,甚至同样吸食大量MPTP的人都会出现帕金森病。虽然帕金森病患者也有家族集聚现象,但至今也没有在散发的帕金森病患者中找到明确的致病基因,说明帕金森病的病因是多因素的。 帕金森病的起病是缓慢的,最初的症状往往不被人所注意。但出现以下症状时,临床上就基本可以诊断为帕金森病了。1、静止性震颤:震颤往往是发病最早期的表现,通常从某一侧上肢远端开始,以拇指、食指及中指为主,表现为手指象在搓丸子或数钞票一样的运动。然后逐渐扩展到同侧下肢和对侧肢体,晚期可波及下颌、唇、舌和头部。在发病早期,患者并不太在意震颤,往往是手指或肢体处于某一特殊体位的时候出现,当变换一下姿势时消失。以后发展为仅于肢体静止时出现,例如在看电视时或者和别人谈话时,肢体突然出现不自主的颤抖,变换位置或运动时颤抖减轻或停止,所以称为静止性震颤,这是帕金森病震颤的最主要的特征。震颤在病人情绪激动或精神紧张时加剧,睡眠中可完全消失。震颤的另一个特点是其节律性,震动的频率是每秒钟4-7次。这个特征也可以帮助我们区别其它的疾病,如因舞蹈病、小脑疾患、还有甲状腺机能亢进等引起的疾病。2、肌肉僵直:帕金森病患者的肢体和躯体通常都失去了柔软性,变得很僵硬。病变的早期多自一侧肢体开始。初期感到某一肢运动不灵活,有僵硬感,并逐渐加重,出现运动迟缓、甚至做一些日常生活的动作都有困难。如果拿起患者的胳膊或腿,帮助他活动关节,你会明显感到他的肢体僵硬,活动其关节很困难,象在来回折一根铅管一样。如果患肢同时有震颤,则有断续的停顿感,就象两个咬合的齿轮转动时的感觉。3、运动迟缓:在早期,由于上臂肌肉和手指肌的强直,病人的上肢往往不能作精细的动作,如解系鞋带、扣纽扣等动作变得比以前缓慢许多,或者根本不能顺利完成。写字也逐渐变得困难,笔迹弯曲,越写越小,这在医学上称为“小写症”。面部肌肉运动减少,病人很少眨眼睛,双眼转动也减少,表情呆板,好象戴了一副面具似的,医学上称为“面具脸”。行走时起步困难,一旦开步,身体前倾,重心前移,步伐小而越走越快,不能及时停步,即“慌张步态”。行进中,患侧上肢的协同摆动减少以至消失;转身困难,要用连续数个小碎步才能转身。因口、舌、鄂及咽部肌肉的运动障碍,病人不能自然咽下唾液,导致大量流涎。言语减少,语音也低沉、单调。严重时可导致进食饮水呛咳。病情晚期,病人坐下后不能自行站立,卧床后不能自行翻身,日常生活

统一帕金森病评定量表【最新版】

统一帕金森病评定量表 姓名:性别:年龄:住院号:联系方式: 评定时间: Ⅰ.精神,行为和情绪 1.智力损害 0=无 1=轻微智力损害,持续健忘,能部分回忆过去的事件,无其他困难 2=中等记忆损害,有定向障碍,解决复杂问题有中等程度的困难,在家中生活功能有轻度但肯定的 损害,有时需要鼓励 3=严重记忆损害伴时间及(经常有)地点定向障碍,解决问题有严

重困难 4=严重记忆损害,仅保留人物定向,不能作出判断或解决问题,生活需要更多的他人帮助 2.思维障碍(痴呆或药物中毒) 0=无 1=生动的梦境 2=“良性”幻觉,自知力良好 3=偶然或经常的幻觉或妄想, 无自知力, 可能影响日常活动 4=持续的幻觉、妄想或富于色彩的精神病, 不能自我照料 3.抑郁 0=无 1=悲观和内疚时间比正常多,持续时间不超过1周

2=持续抑郁(1周或以上) 3=持续抑郁伴自主神经症状(失眠、食欲减退、体重下降、兴趣降低) 4=持续抑郁伴自主神经症状和自杀念头或意愿 4.动力或始动力 0=正常 1=比通常缺少决断力(assertive),较被动 2=对选择性(非常规)活动无兴趣或动力 3=对每天的(常规)活动无兴趣或动力 4=退缩,完全无动力 Ⅱ.日常生活活动(“关”和“开”期)

5.言语(接受) 0=正常 1=轻微受影响,无听懂困难 2=中度受影响,有时要求重复才听懂 3=严重受影响,经常要求重复才听懂 4=经常不能理解 6.唾液分泌 0=正常 1=口腔内唾液分泌轻微但肯定增多,可能有夜间流涎2=中等程度的唾液分泌过多,可能有轻微流涎 3=明显过多的唾液伴流涎

4=明显流涎,需持续用纸巾或手帕擦拭7.吞咽 0=正常 1=极少呛咳 2=偶然呛咳 3=需进软食 4=需要鼻饲或胃造瘘进食 8.书写 0=正常 1=轻微缓慢或字变小 2=中度缓慢或字变小,所有字迹均清楚

帕金森病的诊断及鉴别

帕金森病的诊断、鉴不诊断与治疗 帕金森病的诊断与鉴不诊断 中国人民解放军总医院神经内科孙斌 以多动为要紧表现者易于早期诊断。有关神经介质、神经肽类、神经内分泌等,均不能作为临床确诊依据。脑 CT 、MRI 检查无专门改变。 1.帕金森病血生化检查是否处于正常水平? A.异常水平 B.正常水平 帕金森病(PD)由英国学者James Parkinson(1817)首先描述,又称为震颤麻痹(Shaking palsy)。临床以震颤、肌强直、运动减少和姿势障碍为特征,是中老年人的常见病。 1、帕金森病的病因尚未清晰

1.1 流行病学: 该病随年龄增加而发病率增高, 50岁以上的发病率约为500/10万, >60岁为1 000/10万。约2/3的PD发生在50-60岁,30岁以下发病约1%。我国既往的流行病学调查发病率偏低,但新近的调查发病率结果与国外相近。患病率:白种人每10万人为60-180,黑人为85.7,日本人为34.3-55.0。早期患者占总患病人数的40%左右。20世纪末,美国约有100万PD患者;目前可能我国老年PD 患者可能超过130万。 1.2 病因: 尚未清晰,目前受人关注的有三大因素。 (1)进展性老化因素:PD发病与年龄有关,40岁以下仅占10%,40-50岁为20%,50岁以上为70%,中老年常见。正常人随着年龄增长黑质中DA神经元不断有变性、丢失,当DA神经元丢失80%以上、纹状体DA含量减少超过80%时,才出现PD症状。随着老化而神经细胞内MAO含量却居高不下,可能为促发因素。而80岁以上患病率仅约1%,故年龄绝非PD发病的唯一因素。 (2)遗传因素:约10%-15% PD有阳性家族史,年轻发病者多有近亲同病发病史。有人认为系常染色体显性遗传,外显率低,或多基因遗传;新近的研究认为与线粒体DNA突变有关。 (3)环境因素:环境因素(如工业污染)引起人们的极大关注。有研究通过对5 000名PD患者调查,发觉发病率与使用杀虫剂或化学制品有关。 80年代初在洛杉机由吸毒者发觉1-甲基-4苯基-1,2,3,6-四氢吡啶(1-Methyl-4-phenyl-1,2,3,6-Tetrahydropyridine,MPTP),经MAO-B催化形成N-甲基-4苯基-吡啶离子(MPP + ),它对黑质DA神经元具有高亲和力、选择性毒害作用。推测其毒害机制为,对线粒体呼吸链中复合体Ⅰ的毒性而导致: (1)ATP合成抑制; (2)NADH(还原辅酶Ⅰ)及乳酸堆积; (3)细胞内钙离子浓度急剧变化; (4)谷胱甘肽合成减少,氧自由基生成过多,最终神经元死亡。 1.3 发病机制: 目前认为自由基生成、氧化反应增强、谷胱甘肽含量降低以及线粒体功能异常、内源性和外源性毒物等因素,导致黑质 DA神经元细胞变性,其中线粒体功能异常起主导作用。应用分子生物学技术对有关的酶和基因片段的研究亦支持这种看法。研究发觉黑质组织线粒体复合物Ⅰ基因缺陷、遗传异常,会导致易患性,在毒物等因素的作用下,黑质复合物Ⅰ活性受到阻碍,而导致神经元细胞变性、死亡。因此, 大多数学者认为认为遗传和环境因素可能在 PD发病中起要紧作用。

帕金森病的诊断与治疗(完整版)

帕金森病的诊断与治疗(完整版) 帕金森病(PD)是一种多见于中老年人的神经系统变性疾病。调查结果显示,在65岁以上人群中PD患病率约1.7%,我国目前至少有200万PD患者,随着我国步入老龄社会,患病人数正在持续增长。 临床主要表现为运动迟缓、静止性震颤、肌强直以及姿势步态的异常。除以上运动症状以外还包括许多非运动症状,有些非运动症状出现在运动症状之前,如嗅觉减退、疼痛、睡眠障碍、精神情绪异常、便秘、认知功能障碍等。运动症状和非运动症状均严重影响患者及其照料者的生活质量,给家庭和社会带来沉重的负担。 一、PD的诊断 2015年国际运动障碍协会提出了新的PD诊断标准,并且提出了临床前期、前驱期和临床期的概念,更方便研究和临床诊断。 临床前期是指神经变性已经出现,但是没有明显的临床症状和体征;前驱期是指已经出现某些非运动症状或轻微的症状体征,但仍不足以做出PD的诊断;临床期则是基于典型的运动症状做出PD的诊断。

新的诊断标准仍是首先要符合帕金森综合征的诊断,即运动迟缓为核心,静止性震颤或肌强直至少一项。 新的诊断标准将支持项简化为4条,分别为:单个肢体静止性震颤(既往或本次检查);多巴胺能药物治疗具有明确且显著的有效应答;出现左旋多巴诱导的异动症(疾病早期不易出现);存在嗅觉丧失或心脏间碘苄胍(MIBG)闪烁显像法显示存在心脏去交感神经支配。 二、PD的治疗 帕金森病的治疗方面,2016年出版了最新的中国帕金森病及运动障碍学会关于PD治疗的专家共识。共识提到,PD的治疗原则既要考虑运动症状也要考虑非运动症状;治疗的方法包括药物、手术、体育锻炼以及其他康复、心理咨询及照料护理等。药物是首要的和关键的,外科手术是药物治疗的有益补充。不论哪种方法目前治疗仅能改善症状而不能治愈该病,因此治疗不能只看眼前而不考虑将来。 在疾病的早期,运动锻炼和疾病修饰性药物是首先要考虑的;药物应从小剂量开始逐渐增加剂量以减少运动并发症的发生。中国PD患者发生异动症的比率明显低于西方国家,与我们遵从的用药原则有关。用药既要

统一帕金森病评定量表 UPDRS

统一帕金森病评定量表(UPDRS)
末次服药时间检查时间
□开期□关期服用药物
Ⅰ精神、行为和情绪
M1 智力损害
□0无 □1轻微智力损害,持续健忘,能部分回忆过去的事件,无其他困难 □2中等记忆损害,有定向障碍,解决复杂问题有中等程度的困难, □3严重记忆损害伴时间及(经常有)地点定向障碍,解决问题有严重困难 □4严重记忆损害,仅保留人物定向,不能作出判断或解决问题,生活需要他人帮助
M2 思维障碍(由于痴呆或药物中毒)
□0无 □1生动的梦境 □2“良性”幻觉,自知力良好 □3偶然或经常的幻觉或妄想,无自知力,可能影响日常活动 □4 持续的幻觉、妄想或富于色彩的精神病,不能自我照料
M3 抑郁
□0无 □1悲观和内疚时间比正常多,持续时间不超过 1 周 □2持续抑郁(1 周或以上) □3持续抑郁伴自主神经症状(失眠、食欲减退、体重下降、兴趣降低)
□4持续抑郁伴自主神经症状和自杀念头或意愿
M4 动力或始动力
□0正常 □1比通常缺少决断力,较被动 □2对选择性(非常规)活动无兴趣或动力 □3对每天的(常规)活动无兴趣或动力 □4退缩,完全无动力
Ⅱ.日常生活活动
M5 言语(接受)
□0正常 □1轻微受影响,无听懂困难 □2中度受影响,有时要求重复才听懂 □3严重受影响,经常要求重复才听懂 □4经常不能理解
M6 唾液分泌
精心整理
精心整理

精心整理
□0正常 □1口腔内唾液分泌轻微但肯定增多,可能有夜间流涎 □2中等程度的唾液分泌过多,可能有轻微流涎 □3明显过多的唾液伴流涎 □4明显流涎,需持续用纸巾或手帕擦拭
M7 吞咽
□0正常 □1极少呛咳 □2偶然呛咳 □3需进软食 □4需要鼻饲或胃造瘘进食
M8 书写
□0正常 □1轻微缓慢或字变小 □2中度缓慢或字变小,所有字迹均清楚 □3严重受影响,不是所有字迹均清楚 □4大多数字迹不清楚
M9 切割食物和使用餐具
□0正常 □1稍慢和笨拙,但不需要帮助 □2尽管慢和笨拙,但能切割多数食物,需要某种程度的帮助 □3需要他人帮助切割食物,但能自己缓慢进食 □4需要喂食
M10 着装
□0正常 □1略慢,不需帮助 □2偶尔需要帮助扣扣子及将手臂放进袖里 □3需要相当多的帮助,但还能独立做某些事情 □4完全需要帮助
M11 个人卫生
□0正常 □1稍慢,但不需要帮助 □2需要帮助淋浴或盆浴,或做个人卫生很慢 □3洗脸、刷牙、梳头及洗澡均需帮助 □4需导尿或其他机械帮助
M12 翻身和整理床单
□0正常 □1稍慢且笨拙,但无需帮助
精心整理

相关文档
最新文档