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Research Article | Open Access
A New Method of Diagnosing Constitutional Types Based on Vocal and Facial Features for Personalized Medicine
The aim of the present study is to develop an accurate constitution diagnostic method based solely on the individual’s physical characteristics, irrespective of psychologic traits, characteristics of clinical medicine, and genetic factors. In this paper, we suggest a novel method for diagnosing constitutional types using only speech and face characteristics. Based on 514 subjects, the area under the receiver operating characteristics curve (AUC) values of classification models in age and gender groups ranged from 0.64 to 0.89. We identified significant features showing statistical differences among three constitutional types by performing statistical analysis. Also, we selected a compact and discriminative feature subset for constitution diagnosis in each age and gender group. Our method may support the direction of improved diagnosis prediction and will serve to develop a personal and automatic constitution diagnosis software for improvement of the effectiveness of prescribed medications and development of personalized medicine.
Due to the development of medicine and advances of biotechnology and information technology, the midpoint of medical treatment has shifted away from common treatments of a certain disease to personalized medicine [1–8]. Consistent with this paradigm, there has been an explosion of interest in alternative oriental medicines and in a fusion of oriental and western medicines [9–16]. One of the core research areas of personalized medicine in western and oriental medicine is to understand the psychological characteristics, morphological traits, genetic characteristics, and constitution of individuals. The human constitution has been researched in western and oriental medicine for a long time. For example, Hippocrates suggested that the human constitution could be attributed to four kinds of substances (blood, phlegm, choler, and black bile) . Wang classified humans into seven constitutional groups using physiological and physical status [4, 17]. Similarly, Lee classified humans into four Sasang constitutional types as TE (Teaeumin), TY (Taeyangin), SE (Soeumin), and SY (Soyangin) based on physiological, psychological, and physical characteristics [2, 11–13, 15].
Personal constitution diagnosis is important for several reasons. Firstly, people have vulnerability to particular diseases according to their individual psychological characteristics, genetic characteristics, and morphological traits. Therefore, risk factors for particular diseases can be identified according to an individual’s constitution in the early stages of disease progression . Secondly, drug response to prescribed medicine varies with personal constitution . As such, the efficiency of prescribed medicine can be improved if we know the patient’s constitution.
Many studies on Sasang constitution have been conducted. For the association of Sasang constitutions and diseases and the difference of constitutional types, many researchers introduced constitution analysis methods [1, 4, 5, 8, 12, 13, 15, 18–23]. Song et al.  classified TE and SE among the constitutional types using skin elasticity of the hand and proved that elasticity of the TE type was higher than that of the SE type. Their constitution diagnosis measured hand skin elasticity based on a questionnaire survey of thickness and elasticity of the skin. The limitations of the study were that experiments were performed in only TE and SE types and not in the TY and SY types, and elasticity measurements were performed only on the back of the hand. Choi et al.  studied the distribution of insulin resistance using multivariate logistic regression analysis and features such as age, cholesterol level, smoker/nonsmoker, diastolic blood pressure, and insulin in subjects of each constitutional type. They demonstrated that prevalence of insulin resistance differs according to Sasang constitution type and suggested that personal constitution type can act as an independent risk factor for insulin resistance. An association study between genome-wide SNP (simple nucleotide polymorphisms) profiles and Sasang constitution types for a more accurate Sasang constitution diagnosis was conducted via experiments using 353,202 SNPs from 60 DNAs by Yin et al . They observed that 5,692 SNPs in TE versus SE association analysis were significantly different, 7,542 SNPs in SE versus SY were significantly different, and 4,083 SNPs in SY versus TE were significantly different. The detailed contents of Sasang constitutional medicine are described in references [2, 12], and the research on face or speech signals are described in references [24–28].
Until recently, previous studies that used face, SNPs, skin, body shape, and speech signals have focused primarily on difference analysis among constitutional types; the study of diagnosis prediction is rare. In this study, we focus on Sasang constitution diagnosis using morphological characteristics that are easily accessible to researchers and doctors.
The motivations of this study are as follows: first, how can we obtain essential and useful features that show relationships between morphological characteristics and corresponding constitutional types? Second, how does one use these features to build an efficient and accurate diagnosis model?
We make the following contributions to the field of constitution diagnosis.(i)Propose a readily available and novel method for an accurate and detailed constitution diagnosis using the combination of facial characteristics and speech signals in age- and gender-specific groups. Our method may support the direction of improved diagnosis prediction and will serve to develop a personal and automatic constitution diagnosis tool for improvement of the effectiveness of prescribed medications and development of personalized medicine.(ii)Suggest discriminative and meaningful features for constitution diagnosis via statistical analysis, and identify a compact and useful feature subset in accordance with age and gender. Analysis of results will serve to create a better discriminative feature set in this field.
2. Materials and Methods
2.1. Data Preparation
Speech and facial feature extraction from 514 subjects in several hospitals and the Korea Institute of Oriental Medicine in the Republic of Korea was carried out. Constitutional types of all subjects were determined by specialists and drug responses . Speech record configurations were no resonance, noise intensity from 40 to 50 dB, room temperature of 20°C ± 5°C and humidity of 40% ± 5%, Sennheiser e-835s microphone, Blaster Live 24-bit external sound card, and GoldWave recording program. Distance of the mouth of subjects and the microphone was 4–6 cm, and features were extracted using five vowels (A, E, I, O, U) and one sentence. The extracted features consisted of pitch, average ratio of pitch period, Jita (absolute Jitter), MFCC (Mel frequency cepstral coefficients) [29, 30], and so forth.
We took photographs from the side and front of the subject’s face using a digital camera with a ruler (Nikon D700 with an 85 mm lens). Based on an identified feature point from a side- and front-face image, we obtained features such as distance, distance ratios, angle, and area from forehead, nose, mouth, face shape, and eye . Doctors designated the feature points (Figure 1). Height and weight of subject were measured by a digital scale (LG-150; G Tech International Co., Ltd, Republic of Korea). A total of 82 features were used in this study (29 features from speech signals, 51 features from face, and weight and height features). All feature measurements were done based on self-made tool using MATLAB on Window XP. The specific content of the extracted features was described in Table 1.
Since the face and speech signals are influenced by age and gender [21, 31], experimental data were divided into five categories based on age and gender: Female-20 (women aged 20–29 years), Female-30 (women aged 30–49 years), Female-50 (women aged 50 years and over), Male-30 (men aged 30–49 years), and Male-50 (men aged 50 years and over). The Male-20 (men aged 20–29 years) category was excluded due to the lack of a minimum number of subjects. The numbers of each constitutional type in 5 groups are listed in Table 4 for statistical analysis.
2.2. Experiment Configurations
The goals of our experiment were to measure the ability to distinguish constitutional types, and to identify a more discriminative and compact feature set through feature selection. We conducted classification experiments of TE, SE, and SY constitution types with our five data sets according to the difference of age and gender, with and without feature selection. To investigate the differences of detailed performances of each feature types, speech feature set, face feature set, and hybrid feature set (combining face and speech) were used in this experiment.
We applied normalization (scale 0~1 value) to all data sets. The Wrapper approach using machine learning of LIBLINEAR  and the best-first search (forward) was used in feature subset selection. All experiments were performed using LIBLINEAR (L2-loss SVM dual type) in Weka software , and a 10-fold cross validation for a statistical evaluation of learning algorithm was performed. For optimal parameter selection (tuning), the value of the C parameter was obtained in the range of , and other parameters were fixed as defaults. The area under receiver operating characteristic curve (AUC) was used as a major evaluation criterion. AUC is widely used to quantify the quality of a prediction or classification model in medical science, bioinformatics, medicine statistics, and biology [34–36]. We also evaluated performance using the sensitivity, specificity, and F-measure for detailed evaluation. Statistical analyses were conducted by SPSS version 19 for Windows (SPSS Inc., Chicago, IL, USA).
3. Results and Discussion
3.1. Comparison of Experimental Results
For brief summarization of performance evaluation, the AUC values for the 5 groups with and without feature selection method are showed in Table 2.
In experiments using full features without feature selection, the results indicated that the hybrid feature set (Hybrid-FF) performed better than the individual face and speech feature sets (Face-FF and Speech-FF), except for performance by the face feature set in the Female-30 group. The AUC values of age and gender classification models using hybrid feature set without feature selection ranged from 0.59 to 0.69%.
After application of feature subset selection, the remaining number of features was small, whereas the AUC values of constitution classification were greater than that of the full feature set in all the experiments. For example, the AUC values of classification models using hybrid feature set ranged from 0.64 to 0.89%. The values of classification models using feature selection showed improvements of 0.22 in the Female-20 group and 0.09 in Male-50.
When comparing the face (Face-FS), speech (Speech-FS), and hybrid feature sets (Hybrid-FS), the performance of Hybrid-FS method was higher than that of Face-FS and Speech-FS in the Female-20, Female-50, and Male-50 groups. However, performance of Face-FS method was higher than that of Hybrid-FS and Speech-FS in the Female-30 and Male-30 groups. Thus, it is preferable to use the Face-FS method in these two groups. The theoretical performance of the Hybrid-FS method using feature selection is better than or equal to that of Face-FS and Speech-FS, because the hybrid feature set includes all the speech and face features. However, realistically, many of the feature selection methods may not ensure better performance because a greater number of features add difficulty in building a classifier and lead to the curse of dimensionality and an NP problem [36–38]. Detailed performance evaluation of experiments using feature selection was showed in Table 3.
(: number of samples, Std.: standard deviation, Sch.: Scheffe’s test, *P< 0.05, **P< 0.01, and ***P< 0.001).|
3.2. Statistical Analysis of Meaningful Features
For the statistical analysis of features obtained from feature selection in Hybrid-FS method, we carried out a one-way ANOVA test, and for the post-hoc analysis, we performed the Scheffe’s multiple comparison test. The ANOVA test indicated that there was a significant difference among constitutional types. All features showing P values of <0.05 are shown in Table 4.
In this statistical analysis, we did not found features that covered a broad range of applicability in predicting constitutional types in the age- and gender-specific classification. However, we identified several features with an obvious propensity for classifications of constitutional type.
In facial feature analysis using results from the Scheffe's multiple comparison test, there was a statistically significant difference in weight among constitutional types. Weights between TE and the other types were significantly different in the Female-20 group (F = 19.85, P = 0.0000). Weights between TE and the other types in the Female-50 group were significantly different (F = 25.64, P = 0.0000). Weights among TE, SE, and SY were significantly different in the Male-30 group (F = 26.298, P = 0.0000). There was a significant difference in FD43_143 between TE and the other types in the Female-30 group (F = 23.558, P = 0.0000) and between TE and SE in the Female-50 group (F = 3.384, P = 0.0374). FArea03 between TE and the other types was significantly different in the Male-50 group (F = 14.231, P = 0.0000).
For speech features, there was a significant difference in SITS between TE and SE in the Male-50 group (F = 5.088, P = 0.0088). The uF2 value between SY and the other types was significantly different in the Female-20 group (F = 4.975, P = 0.0100), and aRF2_F1 between TE and SY was significantly different in the Female-20 group (F = 3.772, P = 0.0286).
3.3. Limitations and Future Work
Constitution diagnosis is very difficult in this area, because constitution type decisions are dependent on qualitative judgments of doctors and inspectors . We think that it is possible to develop an accurate diagnosis method or standardization for constitution diagnosis after collecting more diagnosis information by doctors or inspectors.
Our experimental and statistical analysis showed an important and useful feature for better diagnosis based on differences in age and gender. Since diagnosis performances and selected features differ according to age and gender, it makes constitution diagnosis difficult in the real world. Until now, features showing an obvious propensity for constitution diagnosis are not yet sufficient, and data sets to achieve diagnoses that are more accurate are insufficient. Accordingly, more research for constitution diagnosis is needed.
In future work, we will investigate the relationship between constitution and improvement of the effectiveness of medications and explore the role of constitutional types in certain disease. We think that this is very important research in clinical medicine, because the efficiency of prescribed medicine can be improved if we know the patient’s constitution. For instance, Jeong et al. [9, 10] investigated changes in cytokine production in the acute stage of SY constitution CI (cerebral infarction) patients after oral administration of Yangkyuk-Sanhwa-Tang water, and revealed that Yangkyuk-Sanhwa-Tang had a good effect on anti-inflammatory cytokines and a good CI treatment effect. The results of these studies may help to improve the effectiveness of prescribed medications in Sasang constitutional types.
This study describes a novel prediction method for constitution diagnosis as an essential prerequisite for personalized medicine or alternative medicine. We demonstrated the possibility and usefulness of constitution diagnosis using the combination of face and speech feature sets in age- and gender-specific groups, identified a compact and discriminative feature subset, and included supporting statistical analysis of significant features. Our results could be used for developing an automatic constitution diagnostic tool for improving the effectiveness of prescribed medications and could be used in the fields of speech and face recognition.
This work was supported in part by National Research Foundation of Korea (NRF) Grant funded by the Korea government (MEST) (20110027738).
- R. P. Ebstein, J. Benjamin, and R. H. Belmaker, “Personality and polymorphisms of genes involved in aminergic neurotransmission,” European Journal of Pharmacology, vol. 410, no. 2-3, pp. 205–214, 2000.
- H. Chae, I. K. Lyoo, S. J. Lee et al., “An alternative way to individualized medicine: psychological and physical traits of Sasang typology,” Journal of Alternative and Complementary Medicine, vol. 9, no. 4, pp. 519–528, 2003.
- V. Kaasinen, E. Nurmi, J. Bergman et al., “Personality traits and brain dopaminergic function in Parkinson's disease,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 23, pp. 13272–13277, 2001.
- B. Y. Kim, S. Cha, H. J. Jin, and S. Jeong, “Genetic approach to elucidation of Sasang constitutional medicine,” Evidence-based Complementary and Alternative Medicine, vol. 6, no. 1, pp. 51–57, 2009.
- J. C. Loehlin, Genes and Environment in Personality Development, Sage, Thousand Oaks, Calif, USA, 1992.
- P. Nowotny, J. M. Kwon, and A. M. Goate, “SNP analysis to dissect human traits,” Current Opinion in Neurobiology, vol. 11, no. 5, pp. 637–641, 2001.
- J. Paris, “Predispositions, personality traits, and posttraumatic stress disorder,” Harvard Review of Psychiatry, vol. 8, no. 4, pp. 175–183, 2000.
- C. S. Yin, H. J. Park, J. H. Chung, H. J. Lee, and B. C. Lee, “Genome-wide association study of the four-constitution medicine,” Journal of Alternative and Complementary Medicine, vol. 15, no. 12, pp. 1327–1333, 2009.
- H. J. Jeong, S. H. Hong, H. J. Park et al., “Yangkyuk-Sanhwa-Tang induces changes in serum cytokines and improves outcome in focal stroke patients,” Vascular Pharmacology, vol. 39, no. 1-2, pp. 63–68, 2002.
- H. J. Jeong, H. J. Lee, S. H. Hong, H. M. Kim, and J. Y. Um, “Inhibitory effect of Yangkyuk-Sanhwa-Tang on inflammatory cytokine production in peripheral blood mononuclear cells from the cerebral infarction patients,” International Journal of Neuroscience, vol. 117, no. 4, pp. 525–537, 2007.
- H. J. Kim, S. Y. Hwang, J. H. Kim et al., “Association between genetic polymorphism of multidrug resistance 1 gene and sasang constitutions,” Evidence-Based Complementary and Alternative Medicine, vol. 6, no. 1, pp. 73–80, 2009.
- J. Lee, Y. Jung, J. Yoo, E. Lee, and B. Koh, “Perspective of the human body in sasang constitutional medicine,” Evidence-Based Complementary and Alternative Medicine, vol. 6, no. 1, pp. 31–41, 2009.
- E. B. Shim, S. Lee, J. Y. Kim, and Y. E. Earm, “Physiome and sasang constitutional medicine,” Journal of Physiological Sciences, vol. 58, no. 7, pp. 433–440, 2008.
- H. Y. Shin, H. J. Jeong, J. H. Lee et al., “Regulatory effect of cytokine production in patients with cerebral infarction by Yulda-Hanso-Tang,” Immunopharmacology and Immunotoxicology, vol. 22, no. 2, pp. 183–193, 2000.
- J. Y. Um, J. C. Joo, K. Y. Kim, K. M. Lee, and H. M. Kim, “Angiotensin converting enzyme gene polymorphism and traditional Sasang classification in Koreans with cerebral infarction,” Hereditas, vol. 138, no. 3, pp. 166–171, 2003.
- J. H. Yoo, E. J. Lee, C. K. Kwak et al., “Clinical trial of herbal formula on weight loss in obese Korean children,” American Journal of Chinese Medicine, vol. 33, no. 5, pp. 713–722, 2005.
- Q. Wang, Theories of Physical Constitutions of Traditional Chinese Medicine, Chinese Medical Science and Technology Publishing Company, Beijing, China, 1995.
- K. Choi, J. Lee, J. Yoo, E. Lee, B. Koh, and J. Lee, “Sasang constitutional types can act as a risk factor for insulin resistance,” Diabetes Research and Clinical Practice, vol. 91, no. 3, pp. e57–e60, 2011.
- H. S. Jang, M. Y. Im, B. C. Shin, and M. S. Lee, “Menstrual cycle and Yin-Yang in healthy nursing college students,” Complementary Therapies in Clinical Practice, vol. 11, no. 4, pp. 266–269, 2005.
- I. Koo, J. Y. Kim, M. G. Kim, and K. H. Kim, “Feature selection from a facial image for distinction of sasang constitution,” Evidence-based Complementary and Alternative Medicine, vol. 6, no. 1, pp. 65–71, 2009.
- D. D. Pham, J. H. Do, B. Ku, H. J. Lee, H. Kim, and J. Y. Kim, “Body mass index and facial cues in Sasang typology for young and elderly persons,” Evidence-based Complementary and Alternative Medicine, vol. 2011, Article ID 749209, 9 pages, 2011.
- H. W. Song, S. Lee, Y. K. Park, and S. Y. Woo, “Quantitative sasang constitution diagnosis method for distinguishing between tae-eumin and soeumin types based on elasticity measurements of the skin of the human hand,” Evidence-Based Complementary and Alternative Medicine, vol. 6, no. 1, pp. 93–98, 2009.
- H. H. Won, S. Lee, E. Jang et al., “A genome-wide scan for the sasang constitution in a korean family suggests significant linkage at chromosomes 8q11.22–23 and 11q22.1–3,” Journal of Alternative and Complementary Medicine, vol. 15, no. 7, pp. 765–769, 2009.
- M. Soltane, N. Doghmane, and N. Guersi, “Face and speech based multi-modal biometric authentication,” International Journal of Advanced Science and Technology, vol. 21, pp. 41–56, 2010.
- G. A. Atkinson and M. L. Smith, “Using photometric stereo for face recognition,” International Journal of Bio-Science and Bio-Technology, vol. 3, pp. 35–44, 2011.
- A. M. Lech and R. Johnston, “The relative utility of verbal descriptions and facial composites in facial identifications,” International Journal of Bio-Science and Bio-Technology, vol. 3, pp. 1–16, 2011.
- Y. Fayçal, Y. Farid, B. Mesaoud, and G. Mhania, “Pitch shifting of arabic speech signal by source filter modelling for prosodic transformations,” International Journal of Software Engineering and Its Applications, vol. 2, pp. 59–70, 2008.
- R. Ebrahimpour, M. Nazari, M. Azizi, and A. Amiri, “Single training sample face recognition using fusion of classifiers,” International Journal of Hybrid Information Technology, vol. 4, pp. 25–32, 2011.
- A. Gelzinis, A. Verikas, and M. Bacauskiene, “Automated speech analysis applied to laryngeal disease categorization,” Computer Methods and Programs in Biomedicine, vol. 91, no. 1, pp. 36–47, 2008.
- J. Kang and H. Lee, “Automatic voice classification system based on traditional Korean medicine,” Proceedings of World Academy of Science, Engineering and Technology, vol. 56, pp. 35–38, 2009.
- J. González, “Formant frequencies and body size of speaker: a weak relationship in adult humans,” Journal of Phonetics, vol. 32, no. 2, pp. 277–287, 2004.
- R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, “LIBLINEAR: a library for large linear classification,” Journal of Machine Learning Research, vol. 9, pp. 1871–1874, 2008.
- H. Ian, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2005.
- E. Çomak, K. Polat, S. Güneş, and A. Arslan, “A new medical decision making system: least square support vector machine (LSSVM) with Fuzzy Weighting Pre-processing,” Expert Systems with Applications, vol. 32, no. 2, pp. 409–414, 2007.
- J. Huang and C. X. Ling, “Using AUC and accuracy in evaluating learning algorithms,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 3, pp. 299–310, 2005.
- B. J. Lee, M. S. Shin, Y. J. Oh, H. S. Oh, and K. H. Ryu, “Identification of protein functions using a machine-learning approach based on sequence-derived properties,” Proteome Science, vol. 7, article 27, 2009.
- I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003.
- J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2nd edition, 2006.
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