Sincronización entre la videodeglución y la electromiografía de superficie en pacientes con afectación neurológica y síntomas de disfagia / Synchronization between videofluoroscopic swallowing study and surface electromyography in patients with neurological involvement presenting symptoms of dysphagia

Introducción. La disfagia se define como la dificultad para movilizar la comida desde la boca hasta el estómago. La prueba diagnóstica para esta condición es la videofluoroscopia, la cual no es totalmente inocua pues utiliza radiación ionizante. La electromiografía de superficie registra la actividad eléctrica de los músculos de manera no invasiva, por lo que puede considerarse como una alternativa para evaluar la deglución y estudiar la disfagia. Objetivo. Evaluar la relación entre los tiempos relativos de activación de los músculos implicados en la fase oral y faríngea de la deglución, con los movimientos registrados durante la videofluoroscopia. Materiales y métodos. Se analizaron las señales de la electromiografía de superficie de 10 pacientes neurológicos con síntomas de disfagia, captadas en forma simultánea con la videofluoroscopia. Se suministraron 5 ml de yogur y 10 ml de agua, y 3 g de galleta. Se estudiaron bilateralmente los grupos musculares maseteros, suprahioideos e infrahioideos. Se analizó el paso del bolo por la línea mandibular, las valleculas y el músculo cricofaríngeo, correlacionándolo con el tiempo inicial y el final de la activación de cada uno de los grupos musculares. Resultados. El tiempo promedio de la fase faríngea fue de 0,89 ± 0,12 s. En la mayoría de los casos, hubo activación muscular antes del paso por la línea mandibular y las valleculas. La terminación de la actividad muscular parece corresponder al momento en que se completa el paso del bolo alimenticio por el músculo cricofaríngeo. Conclusión. Se determinaron los tiempos de actividad muscular, la duración de la fase faríngea y la secuencia de la activación de los grupos musculares involucrados en la deglución, mediante electromiografía de superficie, validada con la videofluoroscopia.

Introduction: Dysphagia is defined as the difficulty in transporting food and liquids from the mouth to the stomach. The gold standard to diagnose this condition is the videofluoroscopic swallowing study. However, it exposes patients to ionizing radiation. Surface electromyography is a non-radioactive alternative for dysphagia evaluation that records muscle electrical activity during swallowing. Objective: To evaluate the relationship between the relative activation times of the muscles involved in the oral and pharyngeal phases of swallowing and the kinematic events detected in the videofluoroscopy. Materials and methods: Electromiographic signals from ten patients with neurological involvement who presented symptoms of dysphagia were analyzed simultaneously with videofluoroscopy. Patients were given 5 ml of yogurt, 10 ml of water, and 3 g of crackers. Masseter, suprahyoid, and infrahyoid muscle groups were studied bilaterally. The bolus transit through the mandibular line, vallecula, and the cricopharyngeus muscle was analyzed in relation to the onset and offset times of each muscle group activation. Results: The average time of the pharyngeal phase was 0.89 ± 0.12 s. Muscle activation was mostly observed prior to the bolus transit through the mandibular line and vallecula. The end of the muscle activity suggested that the passage of the bolus through the cricopharyngeus muscle was almost complete. Conclusion: The muscle activity times, duration of the pharyngeal phase, and sequence of the muscle groups involved in swallowing were determined using sEMG validated with the videofluoroscopic swallowing study.

Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation / 生物医学工程学杂志

Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.

Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method / 生物医学工程学杂志

Heart sound analysis is significant for early diagnosis of congenital heart disease. A novel method of heart sound classification was proposed in this paper, in which the traditional mel frequency cepstral coefficient (MFCC) method was improved by using the Fisher discriminant half raised-sine function (F-HRSF) and an integrated decision network was used as classifier. It does not rely on segmentation of the cardiac cycle. Firstly, the heart sound signals were framed and windowed. Then, the features of heart sounds were extracted by using improved MFCC, in which the F-HRSF was used to weight sub-band components of MFCC according to the Fisher discriminant ratio of each sub-band component and the raised half sine function. Three classification networks, convolutional neural network (CNN), long and short-term memory network (LSTM), and gated recurrent unit (GRU) were combined as integrated decision network. Finally, the two-category classification results were obtained through the majority voting algorithm. An accuracy of 92.15%, sensitivity of 91.43%, specificity of 92.83%, corrected accuracy of 92.01%, and F score of 92.13% were achieved using the novel signal processing techniques. It shows that the algorithm has great potential in early diagnosis of congenital heart disease.

Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis / 生物医学工程学杂志

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.

Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network / 生物医学工程学杂志

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.

Correspondência entre alvo e saída da prótese auditiva segundo as regras prescritivas NAL-NL1 e NAL-NL2 / Correspondência entre alvo e saída da prótese auditiva segundo as regras prescritivas NAL-NL1 e NAL-NL2

RESUMO Objetivo Analisar comparativamente os alvos prescritos pelas regras NAL (National Acoustic Laboratories) não lineares com a resposta da prótese auditiva obtida por meio das mensurações com microfone-sonda no ajuste de uso efetivo, de acordo com o grau da perda auditiva. Método Participaram do estudo 67 usuários experientes de próteses auditivas. Todos foram reavaliados quando compareceram às sessões de acompanhamento periódico. Nesse momento, realizou-se avaliação audiológica, registrando-se as horas de uso do dispositivo e realizando-se a resposta com prótese auditiva (REAR - Real Ear Aided Response). Resultados Observou-se que 80% das próteses auditivas de todos os grupos atingiram a faixa analisada, com exceção do grupo de perda moderada. Também foi realizada a análise da porcentagem de orelhas cuja resposta com prótese auditiva estivesse em ±5 dB para as frequências baixas e ±8 dB nas altas frequências e observou-se que menos de 80% dos ajustes atingiram esta faixa. Intervalos de confiança foram construídos para verificar a faixa de adaptação de preferência dos usuários experientes. Conclusão A faixa de ±10 dB demonstra ser a de preferência dos usuários. Porém, para usuários experientes, sugere-se que a faixa de adaptação encontre-se na faixa de ±3 nas frequências baixas e médias e ±7 na região de altas frequências.

ABSTRACT Purpose To compare the targets prescribed by the non-linear NAL with the real ear aided response - REAR obtained through probe microphone in the setting of effective use according to the degree of hearing loss. Methods 67 experienced hearing aid users participated in the study. All were reassessed when attending follow-up sessions. At that moment, they were asked whether they had any complaints with respect to the amplification. An audiological evaluation was performed, the hours of use of the device were recorded and the new probe microphone measurement was taken. Results The percentage of ears with REAR within ± 10dB of the prescriptive target was verified. It was observed that 80% of the hearing aids of all groups reached the analyzed range, with the exception of the moderate hearing loss group. We also performed the analysis of the percentage of ears whose hearing aid response was within ± 5 dB for the low frequencies and ± 8 dB for the high frequencies, and it was observed that less than 80% of the adjustments reached this range. Confidence intervals were constructed to verify the preference fit to target of experienced users. Conclusion The range of ±10dB proves to be the users' preference. For experienced users, it is suggested that the adaptation phase be found in the range of ±3 in the low and medium frequencies and ±7 in the high frequency region

Portable Multi Channel EEG Signal Acquisition System / 中国医疗器械杂志

This study introduces a portable multi-channel EEG signal acquisition system. The system is mainly composed of EEG electrode connector, signal conditioning circuit, EEG acquisition part, main control MCU and power supply part. The low-power EEG acquisition front-end ADS1299 and STM32 are used to form the signal acquisition and data communication part. The collected EEG signal can be transmitted to the PC for real-time display. After relevant tests, the system has small volume, low power consumption, high signal-to-noise ratio, and meets the requirements of portable wearable medical devices.

Development of Respiratory Signal Monitoring System Based on Photoplethysmography / 中国医疗器械杂志

Breathing is of great significance in the monitoring of patients with obstructive sleep apnea hypopnea syndrome, perioperative monitoring and intensive care. In this study, a respiratory monitoring and verification system based on optical capacitance product pulse wave (PPG) is designed, which can synchronously collect human PPG signals. Through algorithm processing, the characteristic parameters of PPG signal are calculated, and the respiratory signal and respiratory frequency can be extracted in real time. In order to verify the accuracy of extracting respiratory signal and respiratory rate by the algorithm, the system adds the nasal airflow respiratory signal acquisition module to synchronously collect the nasal airflow respiratory signal as the standard signal for comparison and verification. Finally, the root mean square error between the respiratory rate extracted by the algorithm from the pulse wave and the standard respiratory rate is only 1.05 times/min.

Heart rate extraction algorithm based on adaptive heart rate search model / 生物医学工程学杂志

Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.

An Adaptive Method for Detecting and Removing EEG Noise / 中国医疗器械杂志

To solve the problem of real-time detection and removal of EEG signal noise in anesthesia depth monitoring, we proposed an adaptive EEG signal noise detection and removal method. This method uses discrete wavelet transform to extract the low-frequency energy and high-frequency energy of a segment of EEG signals, and sets two sets of thresholds for the low-frequency band and high-frequency band of the EEG signal. These two sets of thresholds can be updated adaptively according to the energy situation of the most recent EEG signal. Finally, we judge the level of signal interference according to the range of low-frequency energy and high-frequency energy, and perform corresponding denoising processing. The results show that the method can more accurately detect and remove the noise interference in the EEG signal, and improve the stability of the calculated characteristic parameters.

A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine / 生物医学工程学杂志

Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%-24%, which demonstrates the efficiency of the proposed method.

Electrocardiogram signal classification algorithm of nested long short-term memory network based on focal loss function / 生物医学工程学杂志

Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.

Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning / 生物医学工程学杂志

In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.

A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals / 生物医学工程学杂志

Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.

Heartbeat-aware convolutional neural network for R-peak detection of wearable device ECG data / 南方医科大学学报

OBJECTIVE@#To develop a method for R-peak detection of ECG data from wearable devices to allow accurate estimation of the physiological parameters including heart rate and heart rate variability.@*METHODS@#A fully convolutional neural network was applied to predict the R-peak heatmap of ECG data and locate the R-peak positions. The heartbeat-aware (HA) module was introduced to enable the model to learn to predict the heartbeat number and R-peak heatmap simultaneously, thereby improving the capability of the model for extraction of the global context. The R-R interval estimated by the predicted heartbeat number was adopted to calculate the minimum horizontal distance for peak positioning. To achieve real-time R-peak detection on mobile devices, the deep separable convolution was adopted to reduce the number of parameters and the computational complexity of the model.@*RESULTS@#The proposed model was trained only with ECG data from wearable devices. At a tolerance window interval of 150 ms, the proposed method achieved R peak detection sensitivities of 100% for both wearable device ECG dataset and a public dataset (i.e. LUDB), and the true positivity rates exceeded 99.9%. As for the ECG signal of a 10 s duration, the CPU time of the proposed method for R-peak detection was about 23.2 ms.@*CONCLUSION@#The proposed method has good performance for R-peak detection of both wearable device ECG data and routine ECG data and also allows real-time R-peak detection of the ECG data.

Application of back propagation neural network in sports fatigue indicators / Aplicação da rede neural de retropropagação em indicadores de fadiga esportiva / Aplicación de la red neuronal de retropropagación en indicadores de fatiga deportiva

ABSTRACT Introduction High-intensity rehabilitation training will produce exercise fatigue. Objective A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images. Methods The principal component analysis algorithm is used to reduce the dimension of EMG signal features. The knee joint angle is estimated by the regularized over-limit learning machine algorithm and the BP neural network algorithm. Results The RMSE value of the regularized over-limit learning machine algorithm is lower than that of the BP neural network algorithm. At the same time, the ρ value of the regularized over-limit learning machine algorithm is closer to 1, indicating its higher accuracy. Conclusions The model training time of the regularized over-limit learning machine algorithm has been greatly reduced, which improves efficiency. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Introdução O treinamento de reabilitação de alta intensidade produzirá fadiga ao exercício. Objetivo Um algoritmo neural de backpropagation network (BP) é proposto para prever a fadiga esportiva com base em imagens de sinais de eletromiografia (EMG). Métodos O algoritmo de análise de componente principal é usado para reduzir a dimensão das características do sinal EMG. O ângulo da articulação do joelho é estimado usando o algoritmo de aprendizado de máquina de limite regularizado acima e o algoritmo de rede neural BP. Resultados o valor RMSE do algoritmo de aprendizado de máquina acima do limite regularizado é menor que o do algoritmo de rede neural BP. Ao mesmo tempo, o valor de ρ do algoritmo de aprendizado de máquina acima do limite regularizado está próximo de 1, indicando sua maior precisão. Conclusões O tempo de treinamento do modelo de algoritmo de aprendizado de máquina acima do limite regularizado foi bastante reduzido, o que melhora a eficiência. Nível de evidência II; Estudos terapêuticos: investigação dos resultados do tratamento.

RESUMEN Introducción El entrenamiento de rehabilitación de alta intensidad producirá fatiga por ejercicio. Objetivo Se propone un algoritmo neuronal de red de retropropagación (BP) para predecir la fatiga deportiva basándose en imágenes de señales de electromiografía (EMG). Métodos El algoritmo de análisis de componentes principales se utiliza para reducir la dimensión de las características de la señal EMG. El ángulo de la articulación de la rodilla se estima mediante el algoritmo de la máquina de aprendizaje por encima del límite regularizado y el algoritmo de red neuronal BP. Resultados el valor de RMSE del algoritmo de la máquina de aprendizaje por encima del límite regularizado es menor que el del algoritmo de red neuronal de BP. Al mismo tiempo, el valor ρ del algoritmo de la máquina de aprendizaje por encima del límite regularizado está más cerca de 1, lo que indica su mayor precisión. Conclusiones El tiempo de entrenamiento del modelo del algoritmo de la máquina de aprendizaje por encima del límite regularizado se ha reducido en gran medida, lo que mejora la eficiencia. Nivel de evidencia II; Estudios terapéuticos: investigación de los resultados del tratamiento.

A heart rate detection method for wearable electrocardiogram with the presence of motion interference / 生物医学工程学杂志

The dynamic electrocardiogram (ECG) collected by wearable devices is often corrupted by motion interference due to human activities. The frequency of the interference and the frequency of the ECG signal overlap with each other, which distorts and deforms the ECG signal, and then affects the accuracy of heart rate detection. In this paper, a heart rate detection method that using coarse graining technique was proposed. First, the ECG signal was preprocessed to remove the baseline drift and the high-frequency interference. Second, the motion-related high amplitude interference exceeding the preset threshold was suppressed by signal compression method. Third, the signal was coarse-grained by adaptive peak dilation and waveform reconstruction. Heart rate was calculated based on the frequency spectrum obtained from fast Fourier transformation. The performance of the method was compared with a wavelet transform based QRS feature extraction algorithm using ECG collected from 30 volunteers at rest and in different motion states. The results showed that the correlation coefficient between the calculated heart rate and the standard heart rate was 0.999, which was higher than the result of the wavelet transform method (

Convolutional neural network human gesture recognition algorithm based on phase portrait of surface electromyography energy kernel / 生物医学工程学杂志

Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.

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.

Design and Implementation of Software Platform for AI-ECG Algorithm Research / 中国医疗器械杂志

A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classification algorithm and to solve the problem of subjects data information management. Matlab R2019b and MySQL Sever 8.0 are used to design the software platform. The software platform is divided into three modules including data management module, data receiving module and data processing module. The software platform can be used to query and set the subjects information. It has realized the functions of data receiving, signal processing and the display, analysis and storage of ECG data. The software platform is easy to operate and meets the basic needs of scientific research. It is of great significance to the research of AI-ECG algorithm.