22nd Iranian Conference on Biomedical Engineering(ICBME 2015), Iranian Research Organization for Science and Technology (IROST), Tehran, Iran, 25-27 November 2015
Asthma Diagnosis Based on Respiratory Dynamic Using Sparse Representation Based-Classifier Mohammad Reza Raoufy
Reza Darooei, Ali Mahloojifar Tarbiat Modares University
Tarbiat Modares University
Department of Electrical and Computer Engineering
Department of Physiology Faculty of Medical Sciences
Tehran, Iran
Tehran, Iran
[email protected]
[email protected]
[email protected] representation [11] and sparse expansion. Also, two types of features are selected to discriminate between cases. The interbreath interval (IBI) and volume features are similar to each other. Three other classification methods SRC, SVM and KNN are employed to compare with suggested modified SRC.
Abstract— Asthma is a chronic disease which requires being diagnosed early to start treating. A modified Sparse Representation based-Classifier (SRC) is introduced which is capable to classify all datasets such as asthma diagnosis dataset. In particular, both inter-breathing intervals (IBI) and volume of respiratory patterns and features which extracted based on those signals incorporated to diagnose asthma. The discrimination capability of the classifiers are evaluated using the classification parameters such as: sensitivity, specificity, accuracy and etc. This classification technique is used to diagnose asthma with respiratory patterns dynamics.
In this paper we describe feature extraction method and suggest a modified classification method for asthma diagnosis. The paper is organized as follows: In Section II, the database attributes and extracted features and also feature extraction method are described. Section III presents suggested classification method. Section IV is expressed results and discussion. Finally, conclusion is summarized in section V.
Keywords-asthma diagnosis; respiratory dynamic; breathing pattern; sparse representation based classifier.
I.
INTRODUCTION
II.
Asthma is a common inflammatory chronic disorder of the lungs in which airways are prone to constrict [1]. Asthma has some signs such as wheezing breathlessness, and coughing specially at nights or early mornings [2]. 7-10 percent of children and 7-9 percent of adults suffer from this disease. However, the mortality mostly happens in the countries with minimum public health authorities [1].
A. Dataset All patients recording were performed at MasihDaneshvari’s hospital of Tehran and healthy cases recording were performed at Tarbiat Modares university of Tehran. The protocols were approved by respirologist. Each of 40 volunteers (30 asthmatic and 10 healthy subjects), signed a consent form before participating in this study.
Asthma is thought to be caused by two basic elements which includes genetics and environmental factors [3]. Its diagnosis is usually based on patient’s medical history and spirometry. There is not an exact test to diagnose asthma based on respiratory dynamics and the response to medical treatments [4].
Recording lasted 70 minutes using two belts, with one around the subject’s abdomen and the other around his thorax. Then two signals were calibrated by 6 minutes spirometry. All subjects were in the supine position in a silent and clean room. After the main signals were recorded, the following parameters were extracted: 1) Volume and 2) Inter-breathing intervals (IBI). The IBI and volume of respiratory signals are illustrated in Fig. 1.
Breathing is a periodic process which has complicated patterns that depend on patient’s personal factors such as age, sexuality, etc. For example asthmatic patients have shown to have irregular breathing patterns.
B. Extracted Features Extracted features from main signals will be introduced in this section. Twenty features are divided in two categories: volume and IBI. Features are described as follow:
Fractal scaling properties of patient’s respiratory dynamics are changed whether the sick cases are altered aging and gender [5]. The patients breathing intervals are not complex [5], [6], [7], [8].
1. 2. 3. 4.
In this paper the sparse representation based-classifier is suggested for asthma diagnosis among healthy and diseased cases [9], [10]. The idea of this classifier is based on sparse
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DATASET ATTRIBUTES
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Mean of IBI/Volume. Standard deviation of IBI/Volume. Maximum of IBI/Volume. Minimum of IBI/Volume.
Figure 1. IBI and Volume in breathing signal.
5. 6. 7.
Coefficient of variation of IBI/Volume. Mean of absolute IBI/Volume derivation. Standard deviation of absolute IBI/Volume derivation. 8. Maximum of absolute IBI/Volume derivation. 9. Absolute minimum of absolute IBI/Volume derivation. 10. Coefficient of variation of absolute IBI/Volume derivation.
Figure 2. Comparison of mean of extracted values for IBI/Volume features. The first till fourth item contain IBI/Volume features and remain items related to their derivation.
Absolute value of first derivation is shown value of devotion of those factor such as Coefficient of variation (CV), Standard deviation (SD), etc. Also CV’s definition is as below:
SRC implements a sparse linear combination of the given training samples for the test samples to classify them. Suppose n training samples from c different classes are arranged as columns of a matrix A. Linear combination model of the test sample y will be as:
Mean of extracted features for each cases includes: healthy and asthma cases are shown in Fig. 2. According to those figures IBI variations are not too regular.
x Є Rn is vector of coefficients. This equation is underdetermine.
C. Feature Selection The features are selected by Sequential Forward Selection method [12]. Therefore, the selected features are described as below: § § § § § § §
Finding the sparsest solution of (2) discriminants between the classes. Follow equation must be solved to get the sparsest representation of y:
Coefficient of variation of Volume. Standard deviation of IBI. Maximum of IBI. Minimum of IBI. Mean of absolute IBI derivation. Maximum of absolute IBI derivation. Coefficient of variation of absolute IBI derivation
In this case ε is noise parameter. Solving (3) is a NP-hard problem. Reweighted l1-norm minimization [13], Iterative thresholding algorithms [14] and Smoothed l0-norm minimization [15] are some typical alternative methods that have been proposed to find the sparsest solution of (2). In this study, Smoothed l0-norm is used to approximate l0-norm [15], [16].
Those features cause more discernment and also from Fig. 2 these selections are obvious. III.
Now y must be classified in terms of x. Many decision methods can be proposed to this. In this paper, we use the subspace method to choose class label of y. According to this method, test sample is belongs to a class which reproduce it linearly by coefficients x in best way. Therefore, our optimization problem will be like this:
CLASSIFICATION METHOD
A. Sparse Representation based-Classification (SRC) Sparse representation based-classifier (SRC), a combination of machine learning and sparse representation, has been implemented successfully to human frontal face recognition in [9]. The experimental results show SRC can classify face data better than NN and NS.
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In this equation δk(.) is the characteristic function and for each class k:
Consider the weights wi>0, i=1,2,…,n as cost of training samples. Our optimization problem will be modified as:
Although, SRC is a perfect classifier for face recognition but it has bad performance to classify other kind of datasets. SRC could not separate those classes which samples of them are distributed in the same direction. The main reason of this problem is samples normalization. In the SRC, samples are normalized to have unit energy and all samples are mapped onto a hypersphere. So if distribution of data be in a same direction, samples would overlap each other because of normalization and SRC cannot discriminate between classes. In this paper, Weighted SRC is proposed to solve this bug.
Weighted SL0 recovery algorithm [17] is replaced with SL0 to recover x from this modified optimization problem. It is difficult to choose the best form for the weights. In this study, we prefer to select the square of Euclidian distance as our weights:
The decision method of WSRC is absolutely same as SRC. Therefore, the class label of test sample is found by (4) in WSRC.
B. Weighted SRC The SRC has problem with datasets which are distributed in the same direction. The weighted SRC (WSRC) has introduced in this section to resolve this problem.
Results of asthma diagnosis achieved by using four classifiers and the best set of features.
TABLE I.
Method
WSRC
SRC
SVM
KNN
Sensitivity (95% CI)
100 (88.43-100)
96.67 (82.78-99.92)
93.33 (77.93-99.18)
96.67 (82.78-99.92)
Specificity (95% CI)
100 (69.15-100)
90.00 (55.50-99.75)
100 (69.15-100)
80 (44.39-97.48)
Positive Likelihood Ratio (LR+) (95% CI)
Nan
9.67 (1.50-62.13)
Nan
4.83 (1.40-16.73)
Negative Likelihood Ratio (LR-) (95% CI)
0
0.04 (0.01-0.26)
0.07 (0.02-0.25)
0.04 (0.01-0.29)
75.00 (58.80-87.31)
75.00 (58.80-87.31)
75.00 (58.80-87.31)
Positive Prediction Value (PPV) (95% CI)
75.00 (58.8087.31) 100 (88.43-100)
96.67 (82.78-99.92)
100 (87.66-100)
93.55 (78.58-99.21)
Negative Prediction Value (NPV) (95% CI)
100 (69.15-100)
90.00 (55.50-99.75)
83.33 (51.59-97.91)
88.89 (51.75-99.72)
100
95.00
95.00
90.00
Disease Prevalence (95% CI)
Accuracy (95% CI)
Results of asthma diagnosis achieved by using different IBI features.
TABLE II.
IBI Feature
Mean
SD
Max
Min
CV
Mean
SD
Max
Min
CV
(derive)
(derive)
(derive)
(derive)
(derive)
Sensitivity
100
96.67
96.67
86.67
100
96.67
96.67
93.33
96.67
100
Specificity
0
40.00
60.00
60.00
80.00
0
80.00
80.00
80.00
90.00
Positive Likelihood Ratio (LR+) negative Likelihood Ratio (LR-)
1.00
1.61
2.42
2.17
5.00
0.97
4.83
4.67
4.63
10.00
Nan
0.08
0.06
0.22
0
Nan
0.04
0.08
0.04
0
Disease Prevalence Positive Prediction Value (PPV)
75.00
75.00
75.00
75.00
75.00
75.00
75.00
75.00
75.00
75.00
75.00
82.86
87.88
86.67
93.75
74.36
93.55
93.33
93.55
96.67
Negative Prediction Value (NPV)
0
80.00
85.71
60.00
100
0
88.89
80.00
88.89
100
66.67
76.67
83.33
73.33
93.33
63.33
90.00
86.67
90.00
96.67
Accuracy
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Results of asthma diagnosis achieved by using different Volume features.
TABLE III.
Volume Feature
Mean
Sensitivity Specificity Positive Likelihood Ratio (LR+) negative Likelihood Ratio (LR-) Disease Prevalence Positive Prediction Value (PPV) Negative Prediction Value (NPV) Accuracy
80.00 70.00 2.67
SD 93.33 70.00 3.11
Max
Min
93.33 80.00 4.67
86.67 0 0.87
CV 86.67 80.00 4.33
Mean
SD
Max
Min
CV
(derive)
(derive)
(derive)
(derive)
(derive)
90.00 70.00 3.00
86.67 70.00 2.89
90.00 80.00 4.5
93.33 80.00 4.67
76.67 70.00 2.56
0.29
0.10
0.08
Nan
0.17
0.14
0.19
0.12
0.08
0.33
85.00
75.00
75.00
75.00
75.00
75.00
75.00
75.00
75.00
75.00
88.89
90.32
93.33
72.22
92.86
90.00
89.66
93.10
93.33
88.46
53.85
77.78
80.00
0
66.67
70.00
63.64
72.73
80.00
50.00
70.00
83.33
86.67
53.33
80.00
80.00
76.67
83.33
86.67
66.67
Changing on Respiratory patterns is an important sign of asthma. Breathing pattern analyzing protocols can establish a reliable diagnosis of asthma. Also, a new modified classifier has been introduced which is capable to achieve high accuracy for many datasets. We proposed a new weight based on SRC.
IV. RESULTS AND DISCUSSION In this part, performance of proposed method is discussed and our algorithm is compared with other conventional methods like KNN, SVM [18] and SRC.in all of WSRC and SRC experiments the noise parameter value is used 0.05 (ε = 0.05). In the first experiment the error of classifiers are computed by leave-one-out method which means in the leave-oneout approach one of the cases is test sample and all remains samples are training samples.
Experimentally, the results seen for the suggested weight for SRC classification is highly more accurate than other classification algorithms. VI.
In Table І the WSRC achieves the highest accuracy and KNN Method obtains worst result rates compared to other classifiers.
[1]
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According to Table І WSRC produces highest true positive and also lowest true negative. The SRC results are similar to SVM but SRC contains more sensitivity and NPV criterion than SVM.in Table І, we can see WSRC has best accuracy. Therefore, it can discriminate healthy and diseased cases by selected features. In experiment 2, we study performance of each IBI and volumes features and compare it to each other. The discrimination capability of these Extracted features of IBI and volume signals are evaluated by WSRC classifier. Table II contains IBI features and Also, Table III shows volume features in comparison to each other. Table II indicates tenth feature CV of absolute IBI derivation yields best performance among other features. From Table II and Table III we can note that the accuracy of the IBI features are better than volume features and they can easily separate this experiment cases. V. CONCUSION Asthma is a main reason of chronic morbidity and mortality throughout the world. The patients will be treated and achieve good control on their disease if it is diagnosed as soon as possible.
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