A multiscale feature extraction algorithm for dysarthric speech recognition / 生物医学工程学杂志

In this paper, we propose a multi-scale mel domain feature map extraction algorithm to solve the problem that the speech recognition rate of dysarthria is difficult to improve. We used the empirical mode decomposition method to decompose speech signals and extracted Fbank features and their first-order differences for each of the three effective components to construct a new feature map, which could capture details in the frequency domain. Secondly, due to the problems of effective feature loss and high computational complexity in the training process of single channel neural network, we proposed a speech recognition network model in this paper. Finally, training and decoding were performed on the public UA-Speech dataset. The experimental results showed that the accuracy of the speech recognition model of this method reached 92.77%. Therefore, the algorithm proposed in this paper can effectively improve the speech recognition rate of dysarthria.

Research on automatic removal of ocular artifacts from single channel electroencephalogram signals based on wavelet transform and ensemble empirical mode decomposition / 生物医学工程学杂志

The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.

Research on heart rate extraction algorithm in motion state based on normalized least mean square combining ensemble empirical mode decomposition / 生物医学工程学杂志

In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.

A spike denoising method combined principal component analysis with wavelet and ensemble empirical mode decomposition / 生物医学工程学杂志

Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.

Hidden Markov models for human interactive behavior / 生物医学工程学杂志

In order to meet the requirements in the cooperation and competition experiments for an individual patient in clinical application, two human interactive behavior key-press models based on hidden Markov model (HMM) were proposed. To validate the cooperative and competitive models, a verification experimental task was designed and the data were collected. The correlation of the score and subjects' participation level has been used to analyze the reasonability verification. Behavior verification was conducted by comparing the statistical difference in response time for subjects between human-human and human-computer experiment. In order to verify the physiological validity of the models, we have utilized the coherence analysis to analyze the deep information of prefrontal brain area. Reasonability verification shows that the correlation coefficient for the training data and the testing data is 0.883 1 and 0.578 6 respectively based on cooperation model, and 0.813 1 and 0.617 8 respectively based on the competition model. The behavioral verification result shows that the cooperation and competition models have an accuracy of 71.43% respectively. The results of physiological validity show that the deep information of prefrontal brain area could been extracted based on the cooperation and competition models, and reveal the consistency of coherence between the double key-press cooperative and competitive experiments, respectively. Above all, the high consistency is obtained between the cooperatio/competition model and the double key-press experiment by the behavioral and physiological evaluation results. Consequently, the cooperation and competition models could be applied to clinical trials.

Processing of impedance cardiogram differential for non-invasive cardiac function detection / 生物医学工程学杂志

The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.

Design and Implementation of Heart Sound Detection Device Based on MEMS MIC / 中国医疗器械杂志

The paper describes how to develop a digital heart sound signal detection device based on high gain MEMS MIC that can accurately collect and store human heart sounds. According to the method of collecting heart sound signal by traditional stethoscope, the system improves the traditional stethoscope, and a composite probe equipped with a MEMS microphone sensor is designed. The MEMS microphone sensor converts the sound pressure signal into a voltage signal, and then amplifies, converts with Sigma Delta, extracts and filters the collected signal. After the heart sound signal is uploaded to the PC, the Empirical Mode Decomposition (EMD) is carried out to reconstruct the signal, and then the Independent Component Analysis (ICA) method is used for blind source separation and finally the heart rate is calculated by autocorrelation analysis. At the end of the paper, a preliminary comparative analysis of the performance of the system was carried out, and the accuracy of the heart sound signal was verified.

Novel type of unperturbed sleep monitoring scheme under pillow based on hidden Markov model / 生物医学工程学杂志

Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1-S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.

Research on the correlation of brain function based on improved phase locking value / 生物医学工程学杂志

The phase lock value(PLV) is an effective method to analyze the phase synchronization of the brain, which can effectively separate the phase components of the electroencephalogram (EEG) signal and reflect the influence of the signal intensity on the functional connectivity. However, the traditional locking algorithm only analyzes the phase component of the signal, and can't effectively analyze characteristics of EEG signal. In order to solve this problem, a new algorithm named amplitude locking value (ALV) is proposed. Firstly, the improved algorithm obtained intrinsic mode function using the empirical mode decomposition, which was used as input for Hilbert transformation (HT). Then the instantaneous amplitude was calculated and finally the ALV was calculated. On the basis of ALV, the instantaneous amplitude of EEG signal can be measured between electrodes. The data of 14 subjects under different cognitive tasks were collected and analyzed for the coherence of the brain regions during the arithmetic by the improved method. The results showed that there was a negative correlation between the coherence and cognitive activity, and the central and parietal areas were most sensitive. The quantitative analysis by the ALV method could reflect the real biological information. Correlation analysis based on the ALV provides a new method and idea for the research of synchronism, which offer a foundation for further exploring the brain mode of thinking.

Abnormal electroencephalogram features extraction of autistic children based on wavelet transform combined with empirical modal decomposition / 生物医学工程学杂志

Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5-10 years old) and 25 children with autism (20 boys and 5 girls aged 5-10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1-4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.

Analysis and research on spectrum signal of mice blood based on hilbert-huang transform / 中国医学装备

Objective:To provide a criterion for determining whether a mouse's health by analyzing the time-frequency local characteristics of nonlinear and unsteady mice blood spectrum signal.Methods: Collect the blood spectrum signal of the normal and liver damage mice by infrared spectrum method, then study the mice blood spectrum signal by Hilbert - huang transform method.Results: Initially formed criterion on judge whether there is hepatic injury in mice signal.Conclusion: Analysis method based on HHT in mice blood signal spectrum analysis can accurately determine the health of the mice and it provides a new method of analysis for noninvasive blood tests.

ESTIMACIÓN DE POTENCIALES EVOCADOS AUDITIVOS DEL TRONCO CEREBRAL MEDIANTE DESCOMPOSICIÓN MODAL EMPÍRICA

Los potenciales evocados auditivos del tronco cerebral (PEATC) son frecuentemente usados para fines diagnósticos; sin embargo, su procesamiento se hace difícil porque están inmersos en una gran cantidad de ruido proveniente no solo de fuentes externas sino también fisiológicas. Hasta ahora el método más utilizado y aceptado para obtener un registro confiable es la promediación coherente, aunque presenta algunos inconvenientes. La descomposición modal empírica (EMD) es una técnica relativamente nueva que se usa para el procesamiento de señales no estacionarias como la mayoría de señales fisiológicas. Este método separa una señal, extrayendo la energía asociada a diferentes escalas de tiempo intrínsecas, en una suma finita de modos oscilatorios. El propósito de este trabajo fue evaluar la EMD como una herramienta para mejorar el desempeño de la promediación coherente de PEATC buscando reducir la cantidad de épocas necesarias para obtener un registro confiable. Para tal fin se analizó la reconstrucción de ocho registros usando solamente los modos 2, 3 y 4 resultantes de la EMD, los estudios determinaron que una reconstrucción de 800 épocas es aceptable.

The brainstem auditory evoked potentials (BAEPs) are commonly used for diagnostic purposes; however, processing becomes difficult because they are immersed in a large amount of noise coming not only from external sources but also from other physiological sources. So far the most widely used and accepted method to obtain reliable recording is the coherent averaging, but this type of processing presents some drawbacks. The empirical mode decomposition (EMD) is a relatively new technique which is used for processing of non-stationary signal like almost physiological signals. This method separates a signal, xextracting the energy associated with various intrinsic time scales, into a finite set of oscillatory modes. The purpose of the study was to asssess the EMD as a tool for improving the performance of the averaging coherent BAEPs seeking to reduce the amount of epochs needed to obtain a reliable register. To this end, we have analyzed the reconstruction of eight registers using only modes 2, 3 and 4 resulting from the EMD, the studies found that a reconstruction of 800 epochs is acceptable.

Envelope Extraction of Heart Sounds Based on Hilbert-Huang Transform / 航天医学与医学工程

Objective Based on the analysis of time domain of heart sound with envelop to extract the envelope character of heart sounds.Methods The envelope extraction of heart sounds based on Hilbert-Huang Transform was given.Firstly,the original heart sounds signal was preprocessed by Huang Transform.Secondly,the envelope of heart sounds was got with Hilbert Transform.Results The first heart sound and the second heart sound were extracted,and all kinds of characters in time domain of heart sound were acquired more accurately.Conclusion The envelope of heart sound is extracted correctly.The foundation for further analysis of heart sounds is established.