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제안 방법
- In this study, the bio-signal measurement system that obtains information about the contraction pressure of the pelvic floor muscle was fabricated, in order to put forward a method that presents the degree of stress urinary incontinence quantitatively. The diagnostic parameters were established by analyzing data relating to the contraction pressure of pelvic floor muscle obtained from outpatients of the Department of Urology at Inje University, Pusan Paik Hospital.
- In this study, the bio-signal measurement system that obtains information about the contraction pressure of the pelvic floor muscle was fabricated, in order to put forward a method that presents the degree of stress urinary incontinence quantitatively. The diagnostic parameters were established by analyzing data relating to the contraction pressure of pelvic floor muscle obtained from outpatients of the Department of Urology at Inje University, Pusan Paik Hospital. The significance of the diagnostic parameters was evaluated and the patients with similar characteristics were grouped into each classification group, by using a statistical analysis program (SPSS 12.
- 0). The diagnostic algorithm was proposed by analyzing the characteristics of the diagnostic parameters of each classified group. In order to evaluate the efficacy of the proposed algorithm, the factor analysis, multiple regression, and discriminant analysis were performed.
- The diagnostic algorithm was proposed by analyzing the characteristics of the diagnostic parameters of each classified group. In order to evaluate the efficacy of the proposed algorithm, the factor analysis, multiple regression, and discriminant analysis were performed.
- The data concerning the contraction pressure of the pelvic floor muscle which was transmitted to the PC though the data transmission equipment, was defined as diagnostic parameters by using a data analysis program. This data analysis program consists of the measurement mode that conducts real-time monitoring of data about the contraction pressure of pelvic floor muscle and the analysis mode that analyzes signals and establishes diagnostic parameters. The measurement mode is used to measure contraction pressure of pelvic floor muscle.
- As parameters, this study suggested that the pelvic floor muscle should be contracted for 5, 10 and 20 seconds respectively, in order to measure the maximum contraction of pelvic floor muscle, pressure reduction rate, duration of maximum pressure, and space area. A rest time of 10 seconds was given between contractions.
- A rest time of 10 seconds was given between contractions. The data system was designed to obtain data with identical patterns from all patients, through the suggested measurement protocol.
- In this study, in order to evaluate the condition and the potential strength of pelvic floor muscle, the maximum contraction pressure and the duration of maximum pressure that were proposed in the primarily study were used. Through the comparison and analysis of the graphs, the additional diagnostic parameters such as pressure differences, pressure reduction rate and space area were suggested.
- In this study, the analysis was planned using 6 subjects who were diagnosed as normal through urinanalysis, bladder ultrasound examination and history taking, and 19 other patients who were diagnosed as suffering from stress urinary incontinence, with a negative history of surgery in relation to urinary incontinence. The mean age of the subjects was 50±15 years, so the range included various age groups.
- The mean age of the subjects was 50±15 years, so the range included various age groups. After analyzing the data obtained from the subjects, the following five diagnostic parameters were suggested: maximum contraction pressure, pressure difference, pressure reduction rate, duration of maximum pressure, and space area.
- The t-test was performed to ascertain whether the suggested diagnostic parameters were appropriate for classification of both the normal group and the patient group. In order to classify the data showing similar characteristics, the cluster analysis was conducted. In order to build clusters, the hierarchical clustering method was used, which starts with one independent cluster and then builds additional clusters with similar characteristics.
- In this study, the cluster analysis was executed in order to classify the normal group and the patient group and further divide the patient group based on the analysis of the values of the five diagnostic parameters. The hierarchical clustering method was used to build clusters.
- In order to evaluate the efficacy of the algorithm explored in this study, the factor analysis, multiple regression analysis, and discriminant analysis were conducted. For the factor analysis, two common factors which could explain all diagnostic parameters were set.
- 075% of the five diagnostic parameters. In order to analyze the two common factors which resulted from the factor analysis, a factor matrix which indicated the location of each diagnostic parameter was analyzed. Since factor 1 lies at right angles to factor 2, each diagnostic parameter can be shown in the graph where the two axes are factors 1 and 2.
- Because factor scores can be used as independent variables for discriminant analysis, the factor scores for each subject could be calculated. With this in mind, the multiple regression analysis was conducted by setting up five diagnostic parameters as independent variables, and the scores of the two factors as dependent variables. Through the multiple regression analysis, the regression equation for the estimation of the factors was arrived at.
- With these diagnostic parameters, two common factors, ‘pelvic floor muscle energy’ and ‘maximum contraction maintaining power’ were calculated by using linear regression (1) and (2).
- For this study, the Phase II clinical trial was performed at the clinical trial center of Inje University, Pusan Paik Hospital, in order to evaluate the accuracy of the suggested diagnostic algorithm. The subjects were selected by the same method as that used for obtaining the initial data.
- A total of 15 subjects participated in this clinical trial: 4 normal subjects, 8 subjects with stress urinary incontinence Grade 1, and 3 subjects with stress urinary incontinence Grade 2. By analyzing fifteen sets of data obtained through this clinical trial, five diagnostic parameters were derived: maximum contraction pressure, pressure difference, pressure reduction rate, duration of maximum pressure, and space area. With these diagnostic parameters, two common factors, ‘pelvic floor muscle energy’ and ‘maximum contraction maintaining power’ were calculated by using linear regression (1) and (2).
- In this study, the diagnostic algorithm which can assess the degree of stress urinary incontinence quantitatively was suggested by measuring the contraction pressure of pelvic floor muscle. The contraction pressure of pelvic floor muscle was measured by using a bio-signal measurement system, and five diagnostic parameters were derived through data analysis.
- In this study, the diagnostic algorithm which can assess the degree of stress urinary incontinence quantitatively was suggested by measuring the contraction pressure of pelvic floor muscle. The contraction pressure of pelvic floor muscle was measured by using a bio-signal measurement system, and five diagnostic parameters were derived through data analysis. The significance between the normal group and the patient group in all diagnostic parameters was ascertained by t-test.
대상 데이터
- ). For the data transmission equipment a DAQ-Pad USB-6015 (National Instruments Co. Ltd) was used. This can transmit up to 10 samples per second.
- The contraction pressure data from all subjects were obtained by using the suggested protocol in order to compare identical patterns of data between subjects. A total of 15 subjects participated in this clinical trial: 4 normal subjects, 8 subjects with stress urinary incontinence Grade 1, and 3 subjects with stress urinary incontinence Grade 2. By analyzing fifteen sets of data obtained through this clinical trial, five diagnostic parameters were derived: maximum contraction pressure, pressure difference, pressure reduction rate, duration of maximum pressure, and space area.
데이터처리
- The diagnostic parameters were established by analyzing data relating to the contraction pressure of pelvic floor muscle obtained from outpatients of the Department of Urology at Inje University, Pusan Paik Hospital. The significance of the diagnostic parameters was evaluated and the patients with similar characteristics were grouped into each classification group, by using a statistical analysis program (SPSS 12.0). The diagnostic algorithm was proposed by analyzing the characteristics of the diagnostic parameters of each classified group.
- The t-test was performed to ascertain whether the suggested diagnostic parameters were appropriate for classification of both the normal group and the patient group. In order to classify the data showing similar characteristics, the cluster analysis was conducted.
- The diagnostic algorithm was derived based on the observed characteristics of the diagnostic parameters which were exhibited in each classified cluster. In order to evaluate the efficacy of the algorithm, the factor analysis, multiple-regression, and discriminant analysis on the results were performed. Through the factor analysis, two common factors that could explain the five diagnostic parameters were generated, and obtained a score for each factor.
- Through the factor analysis, two common factors that could explain the five diagnostic parameters were generated, and obtained a score for each factor. The multiple regression analysis using the two obtained factor scores as dependent variables was conducted. Through the two multiple regression equations which resulted from the multiple regression analysis, the common factor scores of each subject could be estimated.
- Through the two multiple regression equations which resulted from the multiple regression analysis, the common factor scores of each subject could be estimated. And then the linear discriminant function to assess the degree of urinary incontinence in each subject was derived, based on the two common factor scores established through discriminant the multiple regression analysis. The five groups were classified by using the linear discriminant function.
- Before conducting t-test analysis, we evaluated whether equal variance was assumed by verifying significance probability under Levene’s equal variance test.
- The factor scores for each patient’s diagnostic parameters could be estimated by using these two equations formed from the results of the multiple regression analysis.
- The contraction pressure of pelvic floor muscle was measured by using a bio-signal measurement system, and five diagnostic parameters were derived through data analysis. The significance between the normal group and the patient group in all diagnostic parameters was ascertained by t-test. The diagnostic algorithm was defined which would make a diagnosis quantitatively by suggesting a condition for each diagnostic parameter so as to ensure that the data could be classified according to clusters which did not overlap the data of other group.
이론/모형
- The hierarchical clustering method was used to build clusters. For calculation of distance between clusters, we used the Ward method. At the first stage of the cluster table, the coefficient indicating the sum of squared Euclidean distance was 0.
- Thus, the subjects were selected either diagnosed as normal, or diagnosed with stress urinary incontinence Grade 1 or Grade 2. In order to obtain data, the bio-signal measurement system described in this study was used. The contraction pressure data from all subjects were obtained by using the suggested protocol in order to compare identical patterns of data between subjects.
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