Identification of Denatured Biological Tissues Based on Time-Frequency Entropy and Refined Composite Multi-Scale Weighted Permutation Entropy during HIFU Treatment

Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.

Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy

The recognition of denatured biological tissue is an indispensable part in the process of high intensity focused ultrasound treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the recognition of denatured biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity. The disadvantage will affect the recognition effect of denatured tissues. In order to solve the above problems, the method of multi-scale rescaled range permutation entropy (MRRPE) is proposed in this paper. The simulation results show that the MRRPE not only includes the amplitude information of the signal when calculating the signal complexity, but also extracts the extreme volatility characteristics of the signal effectively. The proposed method is applied to the HIFU echo signals during HIFU treatment, and the support vector machine (SVM) is used for recognition. The results show that compared with MPE and the multi-scale weighted permutation entropy (MWPE), the recognition rate of denatured biological tissue based on the MRRPE is higher, up to 96.57%, which can better recognize the non-denatured biological tissues and the denatured biological tissues.

Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment

Biological tissue damage monitoring is an indispensable part of high-intensity focused ultrasound (HIFU) treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the monitoring of biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity, and the stability of MPE is poor due to the defects in the coarse-grained process. In order to solve the above problems, the method of improved coarse-grained multi-scale weighted permutation entropy (IMWPE) is proposed in this paper. Compared with the MPE, the IMWPE method not only includes the amplitude of signal when calculating the signal complexity, but also improves the stability of entropy value. The IMWPE method is applied to the HIFU echo signals during HIFU treatment, and the probabilistic neural network (PNN) is used for monitoring the biological tissue damage. The results show that compared with multi-scale sample entropy (MSE)-PNN and MPE-PNN methods, the proposed IMWPE-PNN method can correctly identify all the normal tissues, and can more effectively identify damaged tissues and denatured tissues. The recognition rate for the three kinds of biological tissues is higher, up to 96.7%. This means that the IMWPE-PNN method can better monitor the status of biological tissue damage during HIFU treatment.

Formation of Thermal Lesions in Tissue and Its Optimal Control during HIFU Scanning Therapy

A high intensity focused ultrasound (HIFU) scanning approach is needed to obtain multiple treatment spots for the ablation of large volume tumors, but it will bring some problems such as longer treatment times, the inhomogeneity of temperature and thermal lesions in tissues. Although some optimal control methods have been proposed, it is difficult to take into account the uniformity, efficiency and entirety of thermal lesions. In this study, based on the Helmholtz equation and Pennes’ bio-heat transfer equation, a coupled acoustic-thermal field model is proposed to investigate the relationship between temperature elevation, thermal lesions and neighboring treatment spots, and to analyze the effects of the heating time and acoustic intensity on thermal lesions by the finite element method (FEM). Consequently, optimal control schemes for the heating time and acoustic intensity based on the contribution from neighboring treatment spots to thermal lesions are put forward to reduce treatment times and improve the uniformity of temperature and thermal lesions. The simulation results show that the peak historical temperature elevation on one treatment spot is related to the number, distance and time interval of its neighboring treated spots, and the thermal diffusion from the neighboring untreated spots can slow down the drop of temperature elevation after irradiation, thus both of them affect the final shape of the thermal lesions. In addition, increasing the heating time or acoustic intensity of each treatment spot can expand the overall area of thermal lesions, but it would aggravate the elevation and nonuniformity of the temperature of the treatment region. Through optimizing the heating time, the total treatment time can be reduced from 249 s by 17.4%, and the mean and variance of the peak historical temperature elevation can decrease from 44.64 °C by 13.3% and decrease from 24.6317 by 45%, respectively. While optimizing the acoustic intensity, the total treatment time remains unchanged, and the mean of the peak historical temperature elevation is reduced by 4.3 °C. Under the condition of the same thermal lesions, the optimized schemes can reduce the treatment time, lower the peak of the temperature on treatment spots, and homogenize the temperature distributions. This work is of practical significance for the optimization of a HIFU scanning therapy regimen and the evaluation of its treatment effect.