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.
Algorithms ; Bundle-Branch Block ; pathology ; Computer Simulation ; Electrocardiography ; Humans ; Models, Cardiovascular ; Reproducibility of Results

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.
Adult ; Brain Stem ; physiology ; Evoked Potentials, Auditory, Brain Stem ; physiology ; Female ; Fourier Analysis ; Humans ; Male ; Speech ; Speech Perception ; physiology ; Young Adult

Objective We propose an autoencoder model based on a one-dimensional convolutional neural network(1DCNN)as the feature extraction network for efficient detection of epileptic EEG anomalies.Methods The local information of normal EEG signals was captured by utilizing the local feature extraction ability of 1DCNN for training of an autoencoder to learn the expression of normal EEG data in low dimensional feature space.With the difference between the input and output as the anomaly score,the threshold was determined by the optimal equilibrium point of the ROC curve,and the EEG signals exceeding the threshold were diagnosed as the seizure data.The performance of the 1DCNN-AE epilepsy detection model was evaluated using the publicly available CHB-MIT scalp EEG dataset and TUH scalp EEG dataset.Results The AUC of the 1DCNN-AE model reached 0.890 of CHB-MIT and 0.686 of TUH under the average level of patients,and the epilepsy detection rate reached 0.974 and 0.893,and these results were better than the latest epilepsy anomaly detection models LSTM-VAE and GRU-VAE.The 1DCNN model had a parameter quantity of 58.5M,which was at the same level with LSTM-VAE(47.4 M)and GRU-VAE(36.9 M)but with much smaller FLOPs(0.377 G)than LSTM-VAE(21.6 G)and GRU-VAE(16.2 G).Conclusion The autoencoder model based one-dimensional convolutional neural network can effectively detect abnormal EEG signals in epileptic seizure.

Objective We propose an autoencoder model based on a one-dimensional convolutional neural network(1DCNN)as the feature extraction network for efficient detection of epileptic EEG anomalies.Methods The local information of normal EEG signals was captured by utilizing the local feature extraction ability of 1DCNN for training of an autoencoder to learn the expression of normal EEG data in low dimensional feature space.With the difference between the input and output as the anomaly score,the threshold was determined by the optimal equilibrium point of the ROC curve,and the EEG signals exceeding the threshold were diagnosed as the seizure data.The performance of the 1DCNN-AE epilepsy detection model was evaluated using the publicly available CHB-MIT scalp EEG dataset and TUH scalp EEG dataset.Results The AUC of the 1DCNN-AE model reached 0.890 of CHB-MIT and 0.686 of TUH under the average level of patients,and the epilepsy detection rate reached 0.974 and 0.893,and these results were better than the latest epilepsy anomaly detection models LSTM-VAE and GRU-VAE.The 1DCNN model had a parameter quantity of 58.5M,which was at the same level with LSTM-VAE(47.4 M)and GRU-VAE(36.9 M)but with much smaller FLOPs(0.377 G)than LSTM-VAE(21.6 G)and GRU-VAE(16.2 G).Conclusion The autoencoder model based one-dimensional convolutional neural network can effectively detect abnormal EEG signals in epileptic seizure.

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.