Diffusion tensor field estimation based on 3D U-Net and diffusion tensor imaging model constraint / 南方医科大学学报

OBJECTIVE@#To propose a diffusion tensor field estimation network based on 3D U-Net and diffusion tensor imaging (DTI) model constraint (3D DTI-Unet) to accurately estimate DTI quantification parameters from a small number of diffusion-weighted (DW) images with a low signal-to-noise ratio.@*METHODS@#The input of 3D DTI-Unet was noisy diffusion magnetic resonance imaging (dMRI) data containing one non-DW image and 6 DW images with different diffusion coding directions. The noise-reduced non-DW image and accurate diffusion tensor field were predicted through 3D U-Net. The dMRI data were reconstructed using the DTI model and compared with the true value of dMRI data to optimize the network and ensure the consistency of the dMRI data with the physical model of the diffusion tensor field. We compared 3D DTI-Unet with two DW image denoising algorithms (MP-PCA and GL-HOSVD) to verify the effect of the proposed method.@*RESULTS@#The proposed method was better than MP-PCA and GL-HOSVD in terms of quantitative results and visual evaluation of DW images, diffusion tensor field and DTI quantification parameters.@*CONCLUSION@#The proposed method can obtain accurate DTI quantification parameters from one non-DW image and 6 DW images to reduce image acquisition time and improve the reliability of quantitative diagnosis.

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.

Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory / 生物医学工程学杂志

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

Application of Improved Wavelet Threshold in Denoising of ECG Signals / 中国医疗器械杂志

The ECG signal is susceptible to interference from the external environment during the acquisition process, affecting the analysis and processing of the ECG signal. After the traditional soft-hard threshold function is processed, there is a defect that the signal quality is not high and the continuity at the threshold is poor. An improved threshold function wavelet denoising is proposed, which has better regulation and continuity, and effectively solves the shortcomings of traditional soft and hard threshold functions. The Matlab simulation is carried out through a large amount of data, and various processing methods are compared. The results show that the improved threshold function can improve the denoising effect and is superior to the traditional soft and hard threshold denoising.

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.

Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm

OBJECTIVE: To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE).MATERIALS AND METHODS: One hundred routine-dose (RD) abdominal CT studies reconstructed using FBP were used to train the DLA. Simulated CT images were made at dose levels of 13%, 25%, and 50% of the RD (DLA-1, -2, and -3) and reconstructed using FBP. We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. LD CT images of the phantom and patients were processed using FBP, ADMIRE, and DLAs (LD-FBP, LD-ADMIRE, and LD-DLA images, respectively). To compare the image quality, we measured the noise power spectrum and modulation transfer function (MTF) of phantom images. For patient data, we measured the mean image noise and performed qualitative image analysis. We evaluated the presence of additional artifacts in the LD-DLA images.RESULTS: LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE for both phantom and patient data (all p < 0.001). LD-DLAs trained with a lower radiation dose showed less image noise. However, the MTFs of the LD-DLAs were lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose. In the qualitative image analysis, the overall image quality of LD-DLAs was best for DLA-3 (50% simulated radiation dose) and not significantly different from LD-ADMIRE. There were no additional artifacts in LD-DLA images.CONCLUSION: DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. The DLA trained with 50% simulated radiation dose showed the best overall image quality.

Heart sound denoising by dynamic noise estimation / 生物医学工程学杂志

Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

Segmentation of fundamental heart sounds–S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This pa-per proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation.

Bowel Sound Signal Identifcation of Whole Abdomen Based on Voice Endpoint Detection / 中国医疗器械杂志

Bowel sounds are one of the important physiological signals of the body,and different bowel sounds can reflect different gastrointestinal states.In this paper,long time bowel sound data is obtained with wearable full belly bowel sound recorder which is independent designed.After adaptive noise cancellation and wavelet threshold denoising,voice endpoint detection method based on short-time energy is used to identify effective bowel sounds.Experiments and results show that the sound recorder is simple and reliable.Through processing,analysis and endpoint detection algorithm,the recognition accuracy of effective bowel sounds is high,which has certain clinical practicality and research significance.

Design of Paravertebral Muscle Monitoring System Based on Surface Electromyography / 中国医疗器械杂志

In order to diagnose and evaluate the human spinal lesions through the paravertebral muscles, a paravertebral muscle monitoring system based on surface EMG signals was designed. The system used surface mount electrodes to obtain the surface myoelectric signal (sEMG) of paravertebral muscle. The signal was filtered and amplified by the conditioning circuit. The signal was collected by the microcontroller NRF52832 and was sent to the mobile APP. After the signal was preprocessed by the wavelet threshold denoising algorithm in APP, the time and frequency characteristics of the sEMG signal reflecting the functional state of the muscle were extracted. The calculated characteristic parameters was displayed in real time in the application interface. The experimental results show that the system meets the design requirements in analog signal acquisition, digital processing of signals and calculation of characteristic parameters. The system has certain application value.

A wavelet-based method for power-line interference removal in ECG signals

Abstract Introduction The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques. Methods This method consists in identifying the ECG and noise frequency range for further zeroing wavelet detail coefficients in the subbands with no ECG coefficients in the frequency content. Afterward, the enhanced ECG signal is obtained by the inverse discrete wavelet transform (IDWT). In order to choose the wavelet function, several experiments were performed with synthetic signals with worse Signal-to-Noise Ratio (SNR). Results Considering the relative error metrics and runtime, the best wavelet function for denoising was Symlet 8. Twenty synthetic ECG signals with different features and eight real ECG signals, obtained in the Physionet Challenge 2011, were used in the experiments. Results show the advantage of the proposed method against thresholding and notch filter techniques, considering classical metrics of assessment. The proposed method performed better for 75% of the synthetic signals and for 100% of the real signals considering most of the evaluation measures, when compared with a thresholding technique. In comparison with the notch filter, the proposed method is better for all signals. Conclusion The proposed method can be used for PLI removal in ECG signals with superior performance than thresholding and notch filter techniques. Also, it can be applied for high frequencies denoising even without a priori frequencies knowledge.

An echo state network algorithm based on recursive least square for electrocardiogram denoising / 生物医学工程学杂志

Electrocardiogram (ECG) is easily submerged in noise of the complex environment during remote medical treatment, and this affects the intelligent diagnosis of cardiovascular diseases. Considering this situation, this paper proposes an echo state network (ESN) denoising algorithm based on recursive least square (RLS) for ECG signals. The algorithm trains the ESN through the RLS method, and can automatically learn the deep nonlinear and differentiated characteristics in the noisy ECG data, and then the network can use these characteristic to separate out clear ECG signals automatically. In the experiment, the proposed method is compared with the wavelet transform with subband dependent threshold and the S-transform method by evaluating the signal-to-noise ratio and root mean square error. Experimental results show that the denoising accuracy is better and the low frequency component of the signal is well preserved. This method can effectively filter out complex noise and effectively preserve the effective information of ECG signals, which lays a foundation for the recognition of ECG signal feature waveform and the intelligent diagnosis of cardiovascular disease.

Savitzky-Golay Filter for Denoising Lung Sound

ABSTRACT For computerized analysis of respiratory sounds to be effective, the acquired signal must be free from all the interfering elements. Different forms of noise which can degrade the quality of lung sounds are recording artifacts, power line/Radio Frequency (RF) interferences, ambient acoustic interferences, heart sound interference etc. Such interferences adversely affect the diagnostic interpretations. Powerful denoising techniques are necessary to resolve this issue. A denoising scheme for lung sounds, based on Savitzky-Golay (S-G) filter is proposed in this paper. The order and frame length of the SG filter is determined objectively using the Signal to Noise Ratio (SNR) and computational time as objective function. Maximum SNR is observed when the frame length is nearest to the value just higher than the polynomial order. This observation holds good for different levels of simulated addictive Gaussian noise. The polynomial order of 8 and frame size of 9 are found to be promising with SNR of 10.401db at computation time of 2.1ms.

Design of virtual ECG monitor system based on LabVIEW / 医疗卫生装备

Objective To design a virtual ECG monitor system based on LabVIEW software and ECG hardware.Methods The ECG sensor from Vernier company and NI ELVIS were used as the signal acquisition platform.The modules of signal acquisition and procession,characteristic parameters extraction and report output were designed with graphical programming language on LabVIEW platform.Results The virtual ECG monitor system could implement reading,procession,analysis,report output by the computer,and individual differences in ECG analysis could be eliminated.Conclusion The system have advantages in easy operation,low cost,multi purposes and etc,and thus it is valuable for the development of miniature andintelligent multiphysiological parameters detection systems.

Image Denoising for Metal MRI Exploiting Sparsity and Low Rank Priors

PURPOSE: The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called “Slice Encoding for Metal Artifact Correction (SEMAC)” is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts. MATERIALS AND METHODS: Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, l1 minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions. RESULTS: Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction. CONCLUSION: We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.

A Low Noise Amplifier System for Nanopore-based Single Molecule Analysis / 分析化学

A novel amplifier system was proposed for low-noise recording of pico-ampere current in nanopore experiment (<100 pA). As an example, the amplifier system was applied in α-hemolysin based nanopore detection of DNA-PEG-DNA conjugate to record the signals of translocation and bumping events in buffer solution (1 mol/L KCl, 10 mmol/L Tris--HCl, 1 mmol/L EDTA and pH=8. 0). The amplified current signal was filtered by a 3 kHz Bessel filter and sampled by a 100 kHz analog-digital convertor. As a result, the presented amplifier system could lower the noise in recording the current. The current blockages (<10 pA) of single molecules with low amplitude were recovered due to the high signal-to-noise ratio.

Wavelet de-noising of strain estimates in elastography at high overlap / 国际生物医学工程杂志

Object High overlap of data window is essential to improve axial resolution in elastogaphy.However, correlated errors in displacement estimates increase dramatically with the increase of the overlap, and generate the so-called "worm" artifacts. This paper presents a wavelet shrinkage de-noising in strain estimates to reduce the worm artifacts at high overlap. Methods Each of axial strain A-lines was decomposed using discrete wavelet transformation up to 3 levels. The high frequency components of every levels of wavelet coefficients were quantified by using soft threshold function according to different adaptive thresholds. Then the discrete wavelet reconstruction were performed to produce a wavelet shrinkage denoised strain line. Results The simulation results illustrated that the presented technique could efficiently denoise worm artifacts and enhance the elastogram performance indices such as elastographic SNRe and CNRe. Elastogram obtained by wavelet denoising had the closest correspondence with ideal strain image. In addition, the results also demonstrated that wavelet shrinkage de-noising applied in strain estimates could obtain better image quality parameters than that apphed in displacement estimates. The elastic phantom experiments also showed the similar elastogram performance improvement. Conclusion Wavelet shrinkage de-noising can efficiently denoise the worm artifacts noise of elastogram and improve the performance indices of elastogram while maintaining the high axial resolution.

The Least Squares Polynomial Fitting Design Denoise Based on the Volume Pulse Wave Detection System / 中国医学物理学杂志

Purpose: Use of BCI clip-on probe for blood volume transmission pulse wave detection, through the signal conditioning amplifier circuit, A/D conversion circuit, microcontroller and interfaces to collected data to the PC. The applications of software filtering to improve hardware filtering. Methods: This article is carried out in MATLAB based on the least squares polynomial fitting of the low-pass FIR filter denoising simulation. Results: Removal of well-frequency and other interference, access to clean pulse volume waveform. Conclusion: To facilitate real-time display in the interface and for the latter part of the feature extraction and parameter calculation

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.

A Processing Method of EEG Signals Based on Second Generation Wavelet Transform and Blind Signal Separation / 航天医学与医学工程

Objective To study a processing method for EEG signals mixed with EOG and ECG signals disturbance.Methods First,the EEG was denoised by the hard threshold method,the soft threshold method,the compromise threshold method and the ? law threshold method in the second generation wavelet,and then the denoised EEG which still contained EOG and ECG was separated by fast independent component analysis( FastICA) algorithm.Results The ? law threshold method of the second generation wavelet had better denoising effect and FastICA algorithm had more ideal separate performance.Conclusion It is an effective preprocessing method for EEG in denoising with the ? law threshold method of the second generation wavelet and then in separating disturbance of independent source with FastICA algorithm.