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Advances in signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis. However, because of the inevitable intraclass variations of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. Utilizing the new elastic metric, that is, time wrap ed...
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... Chui and Lytras (2019) proposed a novel multi-objective genetic algorithm-based support vector machine (MOGA-SVM) for the multinomial classification of the inflammations of appendix, pancreas and duodenum. Cho et al. applied SVM with GTWED kernel (GTWED-SVM) to evaluated on a dataset including 2,470 pulse waveforms of five distinct patterns, which achieves a lower average error rate than current pulse waveform classification methods (Jia et al., 2019). These literatures show that SVM could be used for TCM clinical diagnosis study with intelligent thought. ...
... Chui and Lytras (2019) proposed a novel multi-objective genetic algorithm-based support vector machine (MOGA-SVM) for the multinomial classification of the inflammations of appendix, pancreas and duodenum. Cho et al. applied SVM with GTWED kernel (GTWED-SVM) to evaluated on a dataset including 2,470 pulse waveforms of five distinct patterns, which achieves a lower average error rate than current pulse waveform classification methods (Jia et al., 2019). These literatures show that SVM could be used for TCM clinical diagnosis study with intelligent thought. ...
... This property can obtain effective classification results when processing a large amount of pulse signal data, as the amount of sample data increases, the stability and accuracy also increase, which is suitable for machine learning of large-scale high-dimensional data of pulse records (Ruping 2001). After extracting the pulse wave feature data and generating a two-dimensional atlas, normal people and patients are classified under the framework of SVM, which can achieve high disease recognition accuracy (Shafri and Ramle 2008;Jia et al. 2014). Because of the different physiological characteristics of each person, the pulse waveform will vary with age, gender, and activity. ...
This experiment is based on the principle of traditional Chinese medicine (TCM) pulse diagnosis, the human pulse signal collected by the sensor is organized into a dataset, and the algorithms are designed to apply feature extraction. After denoising, smoothing and eliminating baseline drift of the photoelectric sensors pulse data of several groups of subjects, we designed three algorithms to describe the difference between the two-dimensional images of the pulse data of normal people and patients with chronic diseases. Convert the calculated feature values into multi-dimensional arrays, enter the decision tree (DT) to balance the differences in human physiological conditions, then train in the support vector machine kernel method (SVM-KM) classifier. Experimental results show that the application of these feature mining algorithms to disease detection greatly improves the reliability of TCM diagnosis.
... However, the contemporary evolutions in artificial intelligence, sensor technology, and signal processing have shepherded the analysts to systematize the pulse diagnosis [8]. Wrist pulse analysis, on the premise of hemodynamics [3], has encountered perpetual ameliorations accompanying the developments in sensor accession [9]. For the classification of pulse signals as diabetics, liver disease, and healthy [10] has evolved a nonlinear perusal stratagem. ...
... For the classification of pulse signals as diabetics, liver disease, and healthy [10] has evolved a nonlinear perusal stratagem. [9], for the pulse wave classification, established SVM based approach along with the Gaussian distance kernel while [11] introduced a cubic support vector machine for lung cancer affected and healthy individuals. An exigent fraction in the aforementioned approaches is the feature extraction and feature selection. ...
Pulse diagnosis has been a requisite facet in traditional Chinese medicine as well as in western medicine, yet the prognosis of lung cancer hinged on wrist pulse analysis entails the quotidian approaches. In spite of diagnosing the lung cancer, in traditional methods, the identification stratagem is divaricated into assorted steps: analysis of signals procured, synthetic extraction and selection of features, and subsequently the classification. However, the vague and mundane feature selection and signal analysis steers to the inadequate classification accuracy due to intrinsic deficiencies. In this study, we have proposed a novel deep convolutional neural network (DCNN) based approach to discern the lung cancer against the acquired wrist pulse signals. In order to ensnare the features, vanquishing the overfitting, a 1-dimensional 15-layers DCNN model is devised hinged on 1-D convolutional, batch normalization, and pooling layers. Considering the instinctive feature extraction, from the experimental data comprised of 45,969 samples of 16 lung cancer and 20 healthy individuals, assorted units are heaped in the lodged DCNN. The experimental comparison with the state-of-art deep neural networks (DNNs) and conventional methods evinced that our lodged approach conquer the deficiencies of conventional signal processing and manual feature selection approaches. Finally, the results, with the validation precision of 97.67%, outperform the recent existing approach for lung cancer recognition.
... In this research, the parameter value C is 1000. The selection of this value was based on experiments conducted by Jia et al. (2014) 2. Parameter σ as a kernel input option in RBF kernel function In this research, kernel option parameter tested as an input parameter in the RBF kernel function on SVM method was done via empirical testing. The values of kernel option parameters tested were 9, 18 and 27. ...
Cancer is one of the most deadly diseases in the world. The International Agency for Research on Cancer (IARC) noted 14.1 million new cancer cases and 8.2 million deaths from cancer in 2012. In the last few years, DNA microarray technology has increasingly been used to analyze and diagnose cancer. Analysis of gene expression data in the form of microarray allows medical experts to ascertain whether or not a person suffers from cancer. DNA microarray data has a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme that includes dimension reduction is needed. In this research, a Principal Component Analysis (PCA) dimension reduction method that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, a Support Vector Machine (SVM) and Levenberg-Marquardt Backpropagation (LMBP) algorithm were selected. Based on the tests performed, the classification method using LMBP was more stable than SVM. The LMBP method achieved an average 96.07% accuracy, while the SVM achieved 94.98% accuracy. © 2018 Adiwijaya, Untari N. Wisesty, E. Lisnawati, A. Aditsania and Dana S. Kusumo.
... Through the continuous efforts of researchers, the great progress of sensor acquisition accuracy and machine learning analyzing pulse signal has seen steady improvements [10][11][12]. In addition to the above, the pulse diagnosis theory with its basis of hemodynamics is proved to be an effective supplementary examination method in clinical medicine [8,[12][13][14][15][16]. ...
... Sun [13] adopted an effective nonlinear analysis method to classify wrist pulse signal from individuals with different health status, i.e. healthy, suffering from liver diseases, and diabetics. In [10], the author applied support vector machine (SVM) with Gaussian time warp edit distance kernel to classify pulse waveform. The integration of medicine with different fields has helped in the development of the pulse diagnosis theory in recent years. ...
Pulse diagnosis is an efficient method in traditional Chinese medicine for detecting the health status of a person in a non-invasive and convenient way. Jin's pulse diagnosis (JPD) is a very efficient recent development that is gradually recognized and well validated by the medical community in recent years. However, no acceptable results have been achieved for lung cancer recognition in the field of biomedical signal processing using JPD. More so, there is no standard JPD pulse feature defined with respect to pulse signals. Our work is designed mainly for care giving service conveniently at home to the people having lung cancer by proposing a novel wrist pulse signal processing method, having an insight from JPD. We developed an iterative slide window (ISW) algorithm to segment the de-noised signal into single periods. We analyzed the characteristics of the segmented pulse waveform and for the first time summarized 26 features to classify the pulse waveforms of healthy individuals and lung cancer patients using a cubic support vector machine (CSVM). The result achieved by the proposed method is found to be 78.13% accurate.
... Experimental results on normal strength pulse (NS-pulse) versus feeble pulse (F-pulse) and overall samples classification were both obtained over 90%. Jia et al. [73] recognize five distinct pulse patterns from 2470 pulse waveforms. They provided a novel elastic metric for SVM to perform pulse waveforms classification. ...
As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs
examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of
disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on
patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic
data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.
... When acquiring the pulse pressure wave signal via sensors, it can become superposed by artifacts resulting from measurement noise, respiration, and motion [68,69]. Accordingly, before further analyzing the signal, it has to be preprocessed to eliminate these interference factors. ...
... After noise reduction and baseline wander removal, certain signal processing approaches require a segmentation of the pulse signal into individual periods. While up to now most studies have carried out a manual segmentation, some authors have suggested algorithms for this purpose [26,69,74,75]. As an additional step, Xia et al. [74] and Thakker and Vyas [76] recommend the removal of outliers and suggest appropriate methods for this purpose such as a dynamic time wrapping approach. ...
... A further use of support vector machines is described in [103] for distinguishing pulses from healthy and diseased subjects. In [69], a pulse waveform classifier is proposed using a support vector machine with a Gaussian time wrap edit distance kernel function. A support vector machine approach together with a Hilbert-Huang transform-based method for spectrum feature extraction is employed in [26] for distinguishing healthy persons from subjects with cholecystitis and nephritis. ...
Introduction: Pulse palpation is an important diagnostic tool in Traditional Chinese Medicine (TCM) and related Oriental medicine systems. However, mastering pulse diagnosis requires long-term experience and remains subjective up to a certain degree even in an advanced stage of practice. Accordingly, considerable research efforts have been spent to objectively measure the radial pulse and in further consequence automate Oriental pulse diagnosis by means of technological aids. This article gives the first comprehensive review about the current state of the art of this field covering topics such as developed pulse acquisition systems, suggested data preprocessing and feature extraction methods, and proposed classification approaches. Furthermore, persisting problems are pointed out and recommendations for future research directions are given.
Methods: A literature search was conducted using scientific databases such as PubMed and ScienceDirect. ResearchGate, Academia, and GoogleScholar were used as additional literature sources. Key search terms were Chinese pulse diagnosis, Oriental pulse diagnosis,
wrist pulse, standardization, objectification, automation, technological aids, sensors, data processing, classification, devices, acquisition systems and combinations and synonyms of these terms. Additionally, the reference lists of found articles were scanned to identify further relevant literature.
Results: A total of 111 references were included into the review. Four main research themes emerged from the literature: (1) pulse waveform acquisition, (2) pulse waveform preprocessing, (3) pulse waveform feature extraction, (4) classifiers and classification objectives.
Conclusion: Automating TCM pulse diagnosis by means of technological aids is a very active research field. Nevertheless, much remains to be done in terms of both technological developments and standardization of procedures.
Background and objective: In traditional Chinese medicine and Ayurvedic medicine, wrist pulse wave fluctuations are an important indicator for distinguishing different health states. Owing to the development of modern sensing technology, computational methods have been used in the analysis of pulse wave signals. The description and quantification of the peaks in the pulse wave is significant for the identification of health status.
Methods: In this study, we decomposed the pressure pulse waveform of the radial artery into several components by sparse decomposition with an improved Gabor function. To better represent the position, shape, and relationship of the peaks, we designed an improved Gabor function structure based on the characteristics of the pulse waveform to generate a time-frequency dictionary. Compared with conventional representation methods, the shape of the Gabor function is more variable. In addition, owing to the limitation of windowing, the Gabor function can reduce the influence on other positions when it represents a specific position. Feature vectors consisting of decomposed components can be used for computerized pulse signal analysis and disease diagnosis.
Results: In the binary classification of healthy and diseased pulse signals, the proposed method achieved the best results for health/diabetes, health/cardiac disease, health/hypertension, and health/nephropathy with accuracies of 93.54%, 73.42%, 88.42%, and 82.28%, respectively. The multi-classification performance of the different types of features was evaluated by six classifiers, and the proposed method obtained the highest classification performance with support vector machine-radial basis function for both balanced and imbalanced data.
Conclusions: The results indicated that the proposed method enabled to obtain a smaller representation error and exhibited superior performance in distinguishing between the signals collected from patients and healthy individuals. Moreover, for the multi-classification of the pulse signals, the proposed method performed better than the state-of-the-art methods.
Predictive modeling of clinical data is fraught with challenges arising from the manner in which events are recorded. Patients typically fall ill at irregular intervals and experience dissimilar intervention trajectories. This results in irregularly sampled and uneven length data which poses a problem for standard multivariate tools. The alternative of feature extraction into equal-length vectors via methods like Bag-of-Words (BoW) potentially discards useful information. We propose an approach based on a kernel framework in which data is maintained in its native form: discrete sequences of symbols. Kernel functions derived from the edit distance between pairs of sequences may then be utilized in conjunction with support vector machines to classify the data. Our method is evaluated in the context of the prediction task of determining patients likely to develop type 2 diabetes following an earlier episode of elevated blood pressure of 130/80 mmHg. Kernels combined via multi kernel learning achieved an F1-score of 0.96, outperforming classification with SVM 0.63, logistic regression 0.63, Long Short Term Memory 0.61 and Multi-Layer Perceptron 0.54 applied to a BoW representation of the data. We achieved an F1-score of 0.97 on MKL on external dataset. The proposed approach is consequently able to overcome limitations associated with feature-based classification in the context of clinical data.
