Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network / 生物医学工程学杂志

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.

Multimodal high-grade glioma semantic segmentation network with multi-scale and multi-attention fusion mechanism / 生物医学工程学杂志

Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients. In order to improve the segmentation accuracy of HGG, this paper proposes a multi-modal glioma semantic segmentation network with multi-scale feature extraction and multi-attention fusion mechanism. The main contributions are, (1) Multi-scale residual structures were used to extract features from multi-modal gliomas magnetic resonance imaging (MRI); (2) Two types of attention modules were used for features aggregating in channel and spatial; (3) In order to improve the segmentation performance of the whole network, the branch classifier was constructed using ensemble learning strategy to adjust and correct the classification results of the backbone classifier. The experimental results showed that the Dice coefficient values of the proposed segmentation method in this article were 0.909 7, 0.877 3 and 0.839 6 for whole tumor, tumor core and enhanced tumor respectively, and the segmentation results had good boundary continuity in the three-dimensional direction. Therefore, the proposed semantic segmentation network has good segmentation performance for high-grade gliomas lesions.

A review on the application of UK Biobank in neuroimaging / 生物医学工程学杂志

UK Biobank (UKB) is a forward-looking epidemiological project with over 500, 000 people aged 40 to 69, whose image extension project plans to re-invite 100, 000 participants from UKB to perform multimodal brain magnetic resonance imaging. Large-scale multimodal neuroimaging combined with large amounts of phenotypic and genetic data provides great resources to conduct brain health-related research. This article provides an in-depth overview of UKB in the field of neuroimaging. Firstly, neuroimage collection and imaging-derived phenotypes are summarized. Secondly, typical studies of UKB in neuroimaging areas are introduced, which include cardiovascular risk factors, regulatory factors, brain age prediction, normality, successful and morbid brain aging, environmental and genetic factors, cognitive ability and gender. Lastly, the open challenges and future directions of UKB are discussed. This article has the potential to open up a new research field for the prevention and treatment of neurological diseases.

Monitoring microwave ablation using ultrasound backscatter homodyned K imaging: Comparison of estimators / 生物医学工程学杂志

The feasibility of ultrasound backscatter homodyned K model parametric imaging (termed homodyned K imaging) to monitor coagulation zone during microwave ablation was investigated. Two recent estimators for the homodyned K model parameter, RSK (the estimation method based on the signal-to-noise ratio, the skewness, and the kurtosis of the amplitude envelope of ultrasound) and XU (the estimation method based on the first moment of the intensity of ultrasound,

Advances in Ultrasound Tissue Characterization and Its Application in Thermal Ablation of Tumors / 中国医疗器械杂志

The methods of monitoring the thermal ablation of tumor are compared and analyzed in recent years. The principle method results and insufficient of ultrasound elastography and quantitative ultrasound imaging are discussed. The results show that ultrasonic tissue signature has great development space in the field of real-time monitoring of thermal ablation, but there are still some problems such as insufficient monitoring accuracy difficulty in whole-course monitoring and insufficient

Application of semantic segmentation based on convolutional neural network in medical images / 生物医学工程学杂志

With the rapid development of network structure, convolutional neural networks (CNN) consolidated its position as a leading machine learning tool in the field of image analysis. Therefore, semantic segmentation based on CNN has also become a key high-level task in medical image understanding. This paper reviews the research progress on CNN-based semantic segmentation in the field of medical image. A variety of classical semantic segmentation methods are reviewed, whose contributions and significance are highlighted. On this basis, their applications in the segmentation of some major physiological and pathological anatomical structures are further summarized and discussed. Finally, the open challenges and potential development direction of semantic segmentation based on CNN in the area of medical image are discussed.

A review on brain age prediction in brain ageing / 生物医学工程学杂志

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.

Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning / 生物医学工程学杂志

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.

A new method for classification of Alzheimer's disease combined with structural magnetic resonance imaging texture features / 生物医学工程学杂志

In this paper, a new method for the classification of Alzheimer's disease (AD) using multi-feature combination of structural magnetic resonance imaging is proposed. Firstly, hippocampal segmentation and cortical thickness and volume measurement were performed using FreeSurfer software. Then, histogram, gradient, length of gray level co-occurrence matrix and run-length matrix were used to extract the three-dimensional (3D) texture features of the hippocampus, and the parameters with significant differences between AD, MCI and NC groups were selected for correlation study with MMSE score. Finally, AD, MCI and NC are classified and identified by the extreme learning machine. The results show that texture features can provide better classification results than volume features on both left and right sides. The feature parameters with complementary texture, volume and cortical thickness had higher classification recognition rate, and the classification accuracy of the right side (100%) was higher than that of the left side (91.667%). The results showed that 3D texture analysis could reflect the pathological changes of hippocampal structures of AD and MCI patients, and combined with multi-feature analysis, it could better reflect the essential differences between AD and MCI cognitive impairment, which was more conducive to clinical differential diagnosis.

Feature extraction of motor imagery electroencephalography based on time-frequency-space domains / 生物医学工程学杂志

The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.

Research progress of techniques of 7 T MRI system in brain imaging / 生物医学工程学杂志

7 T high field magnetic resonance imaging (MRI) provides a useful tool for microscopic spatial resolution visualizing anatomy. In addition, it enables the observation and analysis of tissue metabolism and function. 7 T MRI is now developing fast both in its technology and in its potential prospective medical applications. This review introduces current applications and possible future developments of the 7 T MRI in the field of human brain imaging for clinical studies and practices.

Progress of application in non-invasive temperature monitoring of hyperthermia using B-ultrasound / 生物医学工程学杂志

Hyperthermia is a significant and promising technique for tumor treatment. Temperature is the key parameter which influences the treatment effectiveness. Therefore, real-time and precise noninvasive temperature monitoring is the pivotal issue in further development of hyperthermia. This paper introduced the noninvasive monitoring theories and techniques of hyperthermia based on ultrasonic image texture features, and reviewed the achievements both abroad and at home. In addition, some problems to be solved necessary were also pointed out.

Voxel based morphometric study of brain structure in patients with posttraumatic stress disorder / 生物医学工程学杂志

Voxel based morphometry (VBM) methods are used to detect the difference in brain structures between the posttraumatic stress disorder (PTSD) sufferers and the normal controls. Standard VBM method can detect the difference of the gray matter or white matter densities while the optimized VBM method can detect the difference of gray matter or white matter volumes in the whole brain. The experiments showed that for the patient group, gray matter density or volumes significantly increased in the right frontal lobe, middle frontal gyrus, vermis, left caudate and parietal lobe, compared with the normal controls. However, in the left frontal lobe and middle frontal gyrus, gray matter density significantly decreased. There is no significant difference in white matter between the two groups. These results are consistent with those of the fMRI, which not only provide the evidence for further study of the pathogeny in PTSD but also validate the efficiency of the VBM methods for detecting the difference in the whole brain structure.

An Itelligent System for Diagnosis of Coronary Artery Disease with BP Neural Networks / 대한의료정보학회지

OBJECTIVE: In this paper, an intelligent system using BP neural networks (BPNN) is presented for early detection coronary artery disease (CAD). METHODS: Based on the four features of ECG signals and six basic parameters of patients, BPNN was built and trained. Especially the method which combined feature extraction and classification was discussed. RESULTS: The performance of the intelligent system has been evaluated in 20 samples. The test results showed that this system was effective in detecting CAD. The correct classification rate was about 90% for normal subjects and 100% for abnormal subjects. CONCLUSION: BPNN could quite accurately detect abnormal subjects. Because it is not expensive and noninvasive, it is fit to examine health of the elderly and has good application foreground.

Research on ultrasonic detection of air bubble in race track / 医疗卫生装备

The method for ultrasonic detection of air bubble in race track is studied in this paper. A automatic detection system is established, which mainly consists of the ultrasound emitter and receiver, high-speed A/D acquisition card and PC.

Image segmentation of hepatic slice by MITK and maximum entropy / 医疗卫生装备

This paper discusses image segmentation of liver by MITK and maximum entropy in VC++ 6.0.MITK supports an extensive set of image processing and 3D visualization algorithms,which is a very convenient tool.After filtered by Wavelet transform,good segmentation results could be obtained by maximum entropy method and MITK interactive segmentation.

Multiple physiological parameters tele-monitoring system based on Internet / 医疗卫生装备

The Internet -based multiple physiological parameters tele -monitoring system is composed of an information collector for multiple physiological parameters, a personal computer and a central server. A P2P structure mixed with a C/S structure is adopted in the software design. To make tele-monitoring and tele-diagnosis available, the system is applied to real-time measuring, analyzing, monitoring and tele-transmitting of multiple physiological parameters. The system is suitable to be used in community hospital and family.

Adaptive filter method of enhancing ventricular late potential signals / 生物医学工程学杂志

This paper introduces an adaptive filter method for enhancing ventricular late potentials. The adaptive filter has only one signal electrode and does not need the reference electrode. The experiment results show that this adaptive filter method can effectively improve signal-to-noise ratio of ventricular late potentials.

Design of real-time tele-monitoring system for physiological multi-parameter based on Internet / 医疗卫生装备

A real-time tele-monitoring system for physiological multi-parameter based on Internet is introduced,and physiological signals are transmitted by P2P network technology.The experiment results show that the speed of data transfer has been improved greatly using P2P technology,and physiological signals,such as ECG,can be monitored in real-time.