Construction of an epileptic seizure prediction model using a semi-supervised method of generative adversarial and long short term memory network combined with Stockwell transform / 南方医科大学学报

OBJECTIVE@#To propose a semi-supervised epileptic seizure prediction model (ST-WGAN-GP-Bi-LSTM) to enhance the prediction performance by improving time-frequency analysis of electroencephalogram (EEG) signals, enhancing the stability of the unsupervised feature learning model and improving the design of back-end classifier.@*METHODS@#Stockwell transform (ST) of the epileptic EEG signals was performed to locate the time-frequency information by adaptive adjustment of the resolution and retaining the absolute phase to obtain the time-frequency inputs. When there was no overlap between the generated data distribution and the real EEG data distribution, to avoid failure of feature learning due to a constant JS divergence, Wasserstein GAN was used as a feature learning model, and the cost function based on EM distance and gradient penalty strategy was adopted to constrain the unsupervised training process to allow the generation of a high-order feature extractor. A temporal prediction model was finally constructed based on a bi-directional long short term memory network (Bi-LSTM), and the classification performance was improved by obtaining the temporal correlation between high-order time-frequency features. The CHB-MIT scalp EEG dataset was used to validate the proposed patient-specific seizure prediction method.@*RESULTS@#The AUC, sensitivity, and specificity of the proposed method reached 90.40%, 83.62%, and 86.69%, respectively. Compared with the existing semi-supervised methods, the propose method improved the original performance by 17.77%, 15.41%, and 53.66%. The performance of this method was comparable to that of a supervised prediction model based on CNN.@*CONCLUSION@#The utilization of ST, WGAN-GP, and Bi-LSTM effectively improves the prediction performance of the semi-supervised deep learning model, which can be used for optimization of unsupervised feature extraction in epileptic seizure prediction.

Noise attenuation analysis on auditory evoked potential based on maximum length sequence / 生物医学工程学杂志

The maximum length sequence (m-sequence) has been successfully used to study the linear/nonlinear components of auditory evoked potential (AEP) with rapid stimulation. However, more study is needed to evaluate the effect of the m-sequence order in terms of the noise attenuation performance. This study aimed to address this issue using response-free electroencephalogram (EEG) and EEGs with nonlinear AEPs. We examined the noise attenuation ratios to evaluate the noise variation for the calculations of superimposed averaging and cross-correlation, respectively, which constitutes the main process in the deconvolution method using the dataset of spontaneous EEGs to simulate the cases of different orders (order 5 to 12) of m-sequences. And an experiment using m-sequences of order 7 and 9 was performed in true cases with substantial linear and nonlinear AEPs. The results demonstrate that the noise attenuation ratio is well agreed with the theoretical value derived from the properties of m-sequences on the random noise condition. The comparison of waveforms for AEP components from two m-sequences showed high similarity suggesting the insensitivity of AEP to the m-sequence order. This study provides a more comprehensive solution to the selection of m-sequences which will facilitate the feasible application on the nonlinear AEP with m-sequence method.

Instantaneous energy spectrum analysis for frequency following response of speech evoked brainstem response / 生物医学工程学杂志

Speech evoked brainstem responses (s-ABRs) elicited by a speech syllable /da/ are composed of four parts: onset response (OR), transitional response, frequency following response (FFR) and offset response. FFR elicited by periodic events behaves like a quasi-periodic waveform corresponding to the stimulus sounds. The fast Fourier transform based spectra are commonly used to exam the characteristics of s-ABR in practice, which is, however, unable to trace the occurrence of the main components of s-ABR. The FFR is usually not obvious in the original individual s-ABR waveform. In this paper, we proposed a novel approach to observe the FFR by an instantaneous energy spectrum performed on the intrinsic mode functions (IMFs) after empirical mode decomposition (EMD) of the s-ABR. We demonstrated that the FFR is most pronounced on the second layer of IMFs. This finding suggests a new way which may be available to characterize and to detect the FFR better. This will benefit the clinic applications of s-ABRs.

Vector propagation algorithm based ECG simulation of bundle branch block / 生物医学工程学杂志

The simulation of excitation propagation's process in human heart is one of the main aspects of ECG forward problem. The simulation results not only are the criterion of the simulation model's precision and reliability, but also have great value in researches and diagnoses. We performed the simulation of QRST waves of complete left bundle branch block (LBBB) and right bundle branch block (RBBB) in virtue of a vector propagation algorithm (VPA), which is accurate, efficient and applicable to anisotropic computer heart models. The simulation results accord with the actual QRST wave in clinical practice.