Browsing by Author "Hsu, Chang Francis"

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A collective tracking method for preliminary sperm analysis
(2019-11-27)
Wei, Sung-Yang
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Chao, Hsuan-Hao
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Huang, Han-Ping
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Hsu, Chang Francis
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Li, Sheng-Hsiang
Background Total motile sperm count (TMSC) and curvilinear velocity (VCL) are two important parameters in preliminary semen analysis for male infertility. Traditionally, both parameters are evaluated manually by embryologists or automatically using an expensive computer-assisted sperm analysis (CASA) instrument. The latter applies a point-tracking method using an image processing technique to detect, recognize and classify each of the target objects, individually, which is complicated. However, as semen is dense, manual counting is exhausting while CASA suffers from severe overlapping and heavy computation. Methods We proposed a simple frame-differencing method that tracks motile sperms collectively and treats their overlapping with a statistical occupation probability without heavy computation. The proposed method leads to an overall image of all of the differential footprint trajectories (DFTs) of all motile sperms and thus the overall area of the DFTs in a real-time manner. Accordingly, a theoretical DFT model was also developed to formulate the overall DFT area of a group of moving beads as a function of time as well as the total number and average speed of the beads. Then, using the least square fitting method, we obtained the optimal values of the TMSC and the average VCL that yielded the best fit for the theoretical DFT area to the measured DFT area. Results The proposed method was used to evaluate the TMSC and the VCL of 20 semen samples. The maximum TMSC evaluated using the method is more than 980 sperms per video frame. The Pearson correlation coefficient (PCC) between the two series of TMSC obtained using the method and the CASA instrument is 0.946. The PCC between the two series of VCL obtained using the method and CASA is 0.771. As a consequence, the proposed method is as accurate as the CASA method in TMSC and VCL evaluations. Conclusion In comparison with the individual point-tracking techniques, the collective DFT tracking method is relatively simple in computation without complicated image processing. Therefore, incorporating the proposed method into a cell phone equipped with a microscopic lens can facilitate the design of a simple sperm analyzer for clinical or household use without advance dilution.
An Investigation Study on Mode Mixing Separation in Empirical Mode Decomposition
(2019-01-01)
Huang, Han-Ping
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Wei, Sung-Yang
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Chao, Hsuan-Hao
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Hsu, Chang Francis
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Hsu, Long
Mode mixing is a limitation of the empirical mode decomposition (EMD) method appropriate for physiological signal analysis. In 2008, boundary condition map presented by Rilling and Flandrin provided the efficiency of separating the two components of a two-tone signal as a function of their amplitude and frequency ratios. Until 2019, their findings were still applied. However, their maps only give an uncertainty-like efficiency of mode mixing separation for two-tone signals. In this paper, we propose a criterion for mode mixing separation in EMD, which provides a binary judgment on mode mixing separation instead of the above-mentioned efficiency. By comparing the slopes of the two components, we found that the phenomenon of mode mixing occurs as the extrema of the high-tone component are suppressed by the low-tone component. Under this condition, the criterion shows the relation among their amplitude ratio, frequency ratio, and relative phase between the two components. Given with the values of the three parameters, one can affirm whether the two components are mixed according to the criterion. Accordingly, we derive a black/white three-dimensional (3D) map that plots the binary result of mode mixing in black or white as a function of the three parameters. Our map agrees with Rilling's map and the results obtained from our gait analysis. Among the 23 sets of center-of-mass trajectory signals, six sets encountered the mode mixing problem and their coordinates of the three parameters were found in the black region of the map, while the other 17 sets were in the white region.
Average Entropy: Measurement of disorder for cardiac RR interval signals
(2019-09-01)
Hsu, Chang Francis
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Lin, Ping-Yen
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Chao, Hsuan-Hao
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Hsu, Long
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Chi, Sien
We introduce average entropy (AE) as a disorder measure of heart rate time series. The AE measurement reflects both the probability distribution of all discrete values in a time series and the randomness of the series. The AE analysis is able to differentiate the congestive heart failure (CHF), the healthy, and the atrial fibrillation (AF) subjects for both short and long heart rate time series. The differentiation accuracy of AE is higher than 90%. In addition, since the meaning of "disorder" is easily confused with "complexity", we explore the relation between them by analyzing heart rate time series. Specifically, we demonstrate that the complexity measured by entropy of entropy (EoE) versus the disorder measured by AE plot of heart rate time series exhibits a distinct inverted U relation. (C) 2019 Elsevier B.V. All rights reserved.
Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
(2020-02-01)
Hsu, Chang Francis
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Chao, Hsuan-Hao
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Yang, Albert C.
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Yeh, Chih-Wei
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Hsu, Long
Multiscale entropy (MSE) was used to analyze electroencephalography (EEG) signals to differentiate patients with Alzheimer's disease (AD) from healthy subjects. It was found that the MSE values of the EEG signals from the healthy subjects are higher than those of the AD ones at small time scale factors in the MSE algorithm, while lower than those of the AD patients at large time scale factors. Based on the finding, we applied the linear discriminant analysis (LDA) to optimize the differentiating performance by comparing the resulting weighted sum of the MSE values under some specific time scales of each subject. The EEG data from 15 healthy subjects, 69 patients with mild AD, and 15 patients with moderate to severe AD were recorded. As a result, the weighted sum values are significantly higher for the healthy than the patients with moderate to severe AD groups. The optimal testing accuracy under five specific scales is 100% based on the EEG signals acquired from the T4 electrode. The resulting weighted sum value for the mild AD group is in the middle of those for the healthy and the moderate to severe AD groups. Therefore, the MSE-based weighted sum value can potentially be an index of severity of Alzheimer's disease.
Entropy of Entropy: Measurement of Dynamical Complexity for Biological Systems
(2017-10-01)
Hsu, Chang Francis
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Wei, Sung-Yang
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Huang, Han-Ping
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Hsu, Long
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Chi, Sien
Healthy systems exhibit complex dynamics on the changing of information embedded in physiologic signals on multiple time scales that can be quantified by employing multiscale entropy (MSE) analysis. Here, we propose a measure of complexity, called entropy of entropy (EoE) analysis. The analysis combines the features of MSE and an alternate measure of information, called superinformation, useful for DNA sequences. In this work, we apply the hybrid analysis to the cardiac interbeat interval time series. We find that the EoE value is significantly higher for the healthy than the pathologic groups. Particularly, short time series of 70 heart beats is sufficient for EoE analysis with an accuracy of 81% and longer series of 500 beats results in an accuracy of 90%. In addition, the EoE versus Shannon entropy plot of heart rate time series exhibits an inverted U relationship with the maximal EoE value appearing in the middle of extreme order and disorder.
Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure
(2019-09-01)
Chao, Hsuan-Hao
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Yeh, Chih-Wei
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Hsu, Chang Francis
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Hsu, Long
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Chi, Sien
Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers-linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (>= 55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.
The Static Standing Postural Stability Measured by Average Entropy
(2019-12-01)
Wei, Sung-Yang
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Hsu, Chang Francis
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Lee, Yun-Ju
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Hsu, Long
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Chi, Sien
Static standing postural stability has been measured by multiscale entropy (MSE), which is used to measure complexity. In this study, we used the average entropy (AE) to measure the static standing postural stability, as AE is a good measure of disorder. The center of pressure (COP) trajectories were collected from 11 subjects under four kinds of balance conditions, from stable to unstable: bipedal with open eyes, bipedal with closed eyes, unipedal with open eyes, and unipedal with closed eyes. The AE, entropy of entropy (EoE), and MSE methods were used to analyze these COP data, and EoE was found to be a good measure of complexity. The AE of the 11 subjects sequentially increased by 100% as the balance conditions progressed from stable to unstable, but the results of EoE and MSE did not follow this trend. Therefore, AE, rather than EoE or MSE, is a good measure of static standing postural stability. Furthermore, the comparison of EoE and AE plots exhibited an inverted U curve, which is another example of a complexity versus disorder inverted U curve.