Soojeong Lee

Oscillometric Blood Pressure Estimation Based on Maximum Amplitude Algorithm Employing Gaussian Mixture Regression

This paper introduces a novel approach to estimate the systolic and diastolic blood pressure ratios (SBPR and DBPR) based on the maximum amplitude algorithm (MAA) using a Gaussian mixture regression (GMR). The relevant features, which clearly discriminate the SBPR and DBPR according to the targeted groups, are selected in a feature vector. The selected feature vector is then represented by the Gaussian mixture model. The SBPR and DBPR are subsequently obtained with the help of the GMR and then mapped back to SBP and DBP values that are more accurate than those obtained with the conventional MAA method.

A new a priori SNR estimator based on multiple linear regression technique for speech enhancement

Article history: Available online 18 April 2014 We propose a new approach to estimate the ap riori signal-to-noise ratio (SNR) based on a multiple linear regression (MLR) technique. In contrast to estimation of the ap riori SNR employing the decision- directed (DD) method, which uses the estimated speech spectrum in previous frame, we propose to find the ap riori SNR based on the MLR technique by incorporating regression parameters such as the ratio between the local energy of the noisy speech and its derived minimum along with the a posteriori SNR. In the experimental step, regression coefficients obtained using the MLR are assigned according to various noise types, for which we employ a real-time noise classification scheme based on a Gaussian mixture model (GMM). Evaluations using both objective speech quality measures and subjective listening tests under various ambient noise environments show that the performance of the proposed algorithm is better than that of the conventional methods.

Improved Gaussian Mixture Regression Based on Pseudo Feature Generation Using Bootstrap in Blood Pressure Estimation

Although the systolic and diastolic blood pressure ratios (SBPRs and DBPRs) based on the conventional maximum amplitude algorithm (MAA) are assumed to be fixed; this assumption is not valid. In this paper, we present an improved Gaussian mixture regression (IGMR) approach that can accurately measure blood pressure. The SBPR and DBPR are estimated by using the IGMR technique. Specifically, the number of features samples in the clustered feature space is increased using the nonparametric bootstrap technique to create the pseudo feature. The pseudo feature vector is much more matched than the original feature for the Gaussian mixture model (GMM) to fit individual BP characteristics in the training stage. By using the classified targeting clusters, we eventually estimate the SBPR and DBPR based on the IGMR technique at the test stage. The mean error (ME) and standard deviation of the error (SDE), and mean absolute error (MAE) of the SBP and DBP estimates obtained with the SBPR and DBPR using the proposed technique approaches are superior to the ME, SDE, and MAE of the estimates obtained using the conventional methods. The difference in the SDE between the proposed technique and the conventional MAA technique for the SBP and DBP turned out to be 3.67 and 3.08 mmHg in the simulation.

Estimated confidence interval from single blood pressure measurement based on algorithmic fusion

BACKGROUND Current oscillometric blood pressure measurement devices generally provide only single-point estimates for systolic and diastolic blood pressures and rarely provide confidence ranges for these estimates. A novel methodology to obtain confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates from a single oscillometric blood pressure measurement is presented. METHODS The proposed methodology utilizes the multiple regression technique to fuse optimally a set of SBP and DBP estimates obtained through different algorithms. However, the set of SBP and DBP estimates is a small number to determine the CI of each individual subject. To address this issue, the weighted bootstrap approach based on the multiple regression technique was used to generate a pseudo sample set for the SBP and the DBP. In this paper, the multiple regression technique can estimate the best-fitting surface of an efficient function that relates the input sample set as an independent vector to the auscultatory nurse measurement as a dependent vector to estimate regression coefficients. Consequently, the coefficients are assigned to an eight-sample set obtained from the fusion of different algorithms as optimally weighted parameters. CIs are also estimated using the conventional methods on the set of fused SBP and DBP estimates for comparison purposes. RESULTS The proposed method was applied to an experimental dataset of 85 patients. The results indicated that the proposed approach provides better blood pressure estimates than the existing algorithms and, in addition, is able to provide CIs for a single measurement. CONCLUSIONS The CIs derived from the proposed scheme are much smaller than those calculated by conventional methods except for the pseudo maximum amplitude-envelope algorithm for both the SBP and the DBP, probably because of the decrease in the standard deviation through the increase in the pseudo measurements using the weighted bootstrap method for each subject. The proposed methodology is likely the only one currently available that can provide CIs for single-sample blood pressure measurements.

Oscillometric Blood Pressure Estimation Based on Deep Learning

Oscillometric measurement is widely used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we propose a deep belief network (DBN)-deep neural network (DNN) to learn about the complex nonlinear relationship between the artificial feature vectors obtained from the oscillometric wave and the reference nurse blood pressures using the DBN-DNN-based-regression model. Our DBN-DNN is a powerful generative network for feature extraction and can address to stick in local minima through a special pretraining phase. Therefore, this model provides an alternative way for replacing a popular shallow model. Based on this, we apply the DBN-DNN-based regression model to estimate the SBP and DBP. However, there are a small amount of data samples, which is not enough to train the DBN-DNN without the overfitting problem. For this reason, we use the parametric bootstrap-based artificial features, which are used as training samples to efficiently learn the complex nonlinear functions between the feature vectors obtained and the reference nurse blood pressures. As far as we know, this is one of the first studies using the DBN-DNN-based regression model for BP estimation when a small training sample is available. Our DBN-DNN-based regression model provides a lower standard deviation of error, mean error, and mean absolute error for the SBP and DBP as compared with the conventional methods.

Robust closed-form time-of-arrival source localization based on α-trimmed mean and Hodges-Lehmann estimator under NLOS environments

In this paper, we propose an NLOS source localization method that utilizes the robust statistics, namely, the α-trimmed mean and Hodges-Lehmann estimator. The root mean squared error average of the proposed methods is similar to that of the other estimators such as M-estimator and Taylor-series maximum likelihood estimator using the median, but the proposed robust estimators have advantages that they have the closed-form solution. The simulation results show that the root mean squared error performance of the proposed methods is similar or outperforms that of the iteration-based M-estimator. The Taylor-series maximum likelihood estimator based on the sample median is most superior among the investigated localization methods, but it has the disadvantages that the computational complexity is high and that the solution may converge to the local maxima. Also, it is shown that the performances of the closed-form proposed estimators outperform the JMAP-ML and LS estimator in the above of certain NLOS noise level. HighlightsThe robust closed-form TOA source localization methods are proposed.The proposed estimators have not been used in the TOA source localization context.The proposed methods have an advantage that they are the closed-form.The mixed version of the JMAP-ML estimator and proposed estimators is also proposed.

Deep learning ensemble with asymptotic techniques for oscillometric blood pressure estimation

BACKGROUND AND OBJECTIVE This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. METHOD This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements. However, there are some inherent problems with these methods. First, it is not easy to determine the best DBN-DNN estimator, and worthy information might be omitted when selecting one DBN-DNN estimator and discarding the others. Additionally, our input feature vectors, obtained from only five measurements per subject, represent a very small sample size; this is a critical weakness when using the DBN-DNN technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To address these problems, an ensemble with an asymptotic approach (based on combining the bootstrap with the DBN-DNN technique) is utilized to generate the pseudo features needed to estimate the SBP and DBP. In the first stage, the bootstrap-aggregation technique is used to create ensemble parameters. Afterward, the AdaBoost approach is employed for the second-stage SBP and DBP estimation. We then use the bootstrap and Monte-Carlo techniques in order to determine the CIs based on the target BP estimated using the DBN-DNN ensemble regression estimator with the asymptotic technique in the third stage. RESULTS The proposed method can mitigate the estimation uncertainty such as large the standard deviation of error (SDE) on comparing the proposed DBN-DNN ensemble regression estimator with the DBN-DNN single regression estimator, we identify that the SDEs of the SBP and DBP are reduced by 0.58 and 0.57  mmHg, respectively. These indicate that the proposed method actually enhances the performance by 9.18% and 10.88% compared with the DBN-DNN single estimator. CONCLUSION The proposed methodology improves the accuracy of BP estimation and reduces the uncertainty for BP estimation.

On using multivariate polynomial regression model with spectral difference for statistical model-based speech enhancement

In this paper, we propose a statistical model-based speech enhancement technique using a multivariate polynomial regression (MPR) based on spectral difference scheme.In the analyzing step, three principal parameters, the weighting parameter in the decision-directed (DD) method, the long-term smoothing parameter for the noise estimation.The control parameter of the minimum gain value is estimated as optimal operating points technique by using to the spectral difference under various noise conditions.The MPR technique offers an effective scheme to represent complex nonlinear input-output relationship between the optimal operating points and spectral differences so that operating points can be determined according to various noise conditions in the off-line step. In this paper, we propose a statistical model-based speech enhancement technique using a multivariate polynomial regression (MPR) based on spectral difference scheme. In the analyzing step, three principal parameters, the weighting parameter in the decision-directed (DD) method, the long-term smoothing parameter for the noise estimation, and the control parameter of the minimum gain value are estimated as optimal operating points technique by using to the spectral difference under various noise conditions. These optimal operating points, which are specific according to different spectral differences, are estimated based on the composite measure, which is a relevant criterion in terms of speech quality. Thus, we apply the MPR technique by incorporating the spectral differences as independent variables in order to estimate the optimal operating points. The MPR technique offers an effective scheme to represent complex nonlinear input-output relationship between the optimal operating points and spectral differences so that operating points can be determined according to various noise conditions in the off-line step. In the on-line speech enhancement step, different parameters are chosen on a frame-by-frame basis through the regression according to the spectral difference. The performance of the proposed method is evaluated using objective and subjective speech quality measures in various noise environments. Our experimental results show that the proposed algorithm yields better performances than conventional algorithms.

Combining Bootstrap Aggregation with Support Vector Regression for Small Blood Pressure Measurement

Blood pressure measurement based on oscillometry is one of the most popular techniques to check a health condition of individual subjects. This paper proposes a support vector using fusion estimator with a bootstrap technique for oscillometric blood pressure (BP) estimation. However, some inherent problems exist with this approach. First, it is not simple to identify the best support vector regression (SVR) estimator, and worthy information might be omitted when selecting one SVR estimator and discarding others. Additionally, our input feature data, acquired from only five BP measurements per subject, represent a very small sample size. This constitutes a critical limitation when utilizing the SVR technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To overcome these challenges, a fusion with an asymptotic approach (based on combining the bootstrap with the SVR technique) is utilized to generate the pseudo features needed to predict the BP values. This ensemble estimator using the SVR technique can learn to effectively mimic the non-linear relations between the input data acquired from the oscillometry and the nurse’s BPs.

Deep Belief Networks Ensemble for Blood Pressure Estimation

In this paper, we propose a deep belief network (DBN)–deep neural network (DNN) with mimic features based on the bootstrap inspired technique to learn the complex nonlinear relationship between the mimic feature vectors obtained from the oscillometry signals and the target blood pressures. Unfortunately, we have two problems in utilizing the DBN–DNN technique to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP). First, our set of input feature vectors is very small, which is a fatal drawback to training based on the DBN–DNN technique. Second, the special pre-training phase can also trigger an unstable estimation, because there are still a lot of random initialized assigns, such as the training data set, weights, and biases. For these reasons, we employ the bootstrap-inspired technique as a fusion ensemble estimator based on the DBN–DNN-based regression model, which is used to create the mimic features to estimate the SBP and DBP. Our DBN–DNN-based ensemble regression estimator provides a lower standard deviation of error, mean error, and mean absolute error for the SBP and DBP as compared with those of the conventional methods.