An image segmentation based algorithm for imaging of slow slip earthquakes

Laboratory experiments next to a variety of observations, especially in subduction zones, have explored the existence of a premonitory stable slow slip growth phase preceding large earthquakes. These phe- nomena play an important role in the earthquake cycle and thus precise imaging and monitoring of these events are of great significance. In the literature, ENIF （extended network inversion filter） has been proposed as a rigorous algorithm capable of isolating signal from different types of noise and thereby provides us with deep insight into spatio-temporal evolution of slow slip events. Despite its considerable advantages, ENIF still suffers from some limitations. ENIF applies Tikhonov method of regularization with a quadratic form of cost function. While anomalous slip regions have clear contrast with the background slip in reality, Tikhonov regularization tends to over smooth （globally smooth） the slipping portion on the estimated images. In order to avoid over smoothing phenomenon, we have incorporated into ENIF an image segmentation step which tries to preserve edges of slow-slip event. As a second limitation, due to the nonlinearity imposed by such constraint as non-negativity of slip rate, uncertainty propagation through model is not simple. As the core of ENIF, EKF （extended Kalman filter）, performs uncertainty propagation by linearization of nonlinear model using Jacobian and Hessian matrices. As an alternative for EKE we have also investigated the application of UKF （unscented Kalman filter） which uses UT （unscented transform） for uncertainty propagation. Finally, we tested our proposed algorithm using a low signal to noise ratio synthetic data set. The results show a significant improvement in the perfor- mance of ENIF when the segmentation step is incorporated into the algorithm.

A predictive model of death from cerebrovascular diseases in intensive care units

Objective This study aimed to explore the mortality prediction of patients with cerebrovascular diseases inthe intensive care unit(ICU)by examining the important signals during different periods of admission in theICU,which is considered one of the new topics in the medical field.Several approaches have been proposed forprediction in this area.Each of these methods has been able to predict mortality somewhat,but many of thesetechniques require recording a large amount of data from the patients,where recording all data is not possiblein most cases;at the same time,this study focused only on heart rate variability(HRV)and systolic and diastolicblood pressure.Methods The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring inIntensive Care II(MIMIC-II)Clinical Database.The proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients,48 men and 40 women,during their first 48 hours of ICU stay.The electrocardiogram(ECG)signals are related to lead II,and the sampling frequency is 125 Hz.The time of admission and time ofdeath are labeled in all data.In this study,the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood pressure.Topredict the patient’s future condition,the combination of features extracted from the return mapping generatedby the HRV signal,such as angle(𝛼),area(A),and various parameters generated by systolic and diastolic bloodpressure,including DBPMax−Min SBPSD have been used.Also,to select the best feature combination,the geneticalgorithm(GA)and mutual information(MI)methods were used.Paired sample t-test statistical analysis was usedto compare the results of two episodes(death and non-death episodes).The P-value for detecting the significancelevel was considered less than 0.005.Results The results indicate that the new approach presented in this paper can be compared with other methodsor leads to better results.The best combination of features based on GA to achieve maximum predictive accuracywas m(mean),L_(Mean),A,SBP_(SVMax),DBP_(Max-Min).The accuracy,specificity,and sensitivity based on the best featuresobtained from GA were 97.7%,98.9%,and 95.4%for cerebral ischemia disease with a prediction horizon of0.5–1 hour before death.The d-factor for the best feature combination based on the GA model is less than 1(d-factor=0.95).Also,the bracketed by 95 percent prediction uncertainty(95PPU)(%)was obtained at 98.6.Conclusion The combination of HRV and blood pressure signals might increase the accuracy of the predictionof the death episode and reduce the minimum hospitalization time of the patient with cerebrovascular diseasesto determine the future status.

MOTION ARTIFACT REDUCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY AUTOREGRESSIVE MOVING AVERAGE MODELING BASED KALMAN FILTERING

Functional near infrared spectrosecopy(NIRS)is a technique that is used for noninvasive measurement of the oxyhemoglobin(HbO_(2))and deoxyhemoglobin(HHb)concentrations in the brain tissue.Since the ratio of the concentration of these two agents is correlated with the neuronal activity,ONIRS can be usod for the monitoring and quantifying the cortical activity.The portability of NIRS makes it a good candidate for studies involving subject's movement.The NIRS measurements,however,are sensitive to artifacts generated by subject's head motion.This makes fNIRS signals less effective in such applications.In this paper,the autoregressive moving average(ARMA)modeling of the NIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal.Results are compared to the autoregressive model(AR)based approach,which has been done previously,and show that the ARMA models outperform AR models.We attribute it to the richer structure,containing more terms indeed,of ARMA than AR.We show that the signal to noise ratio(SNR)is about 2 dB higher for ARMA based method.