bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering
Authors: Xia Yong , Han Junze , Wang Kuanquan 
Published: August 2015

Abstract Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods.
The application study of wavelet packet transformation in the de-noising of dynamic EEG data
Authors: Li Yifeng , Zhang Lihui , Li Baohui , Wei Xiaoyang , Yan Guiding , Geng Xichen , Jin Zhao , Xu Yan , Wang Haixia , Liu Xiaoyan , Lin Rong , Wang Quan 
Published: August 2015

Abstract This paper briefly describes the basic principle of wavelet packet analysis, and on this basis introduces the general principle of wavelet packet transformation for signal den-noising. The dynamic EEG data under +Gz acceleration is made a de-noising treatment by using wavelet packet transformation, and the de-noising effects with different thresholds are made a comparison. The study verifies the validity and application value of wavelet packet threshold method for the de-noising of dynamic EEG data under +Gz acceleration.
Removal of baseline wandering in ECG signal by improved detrending method
Authors: Shin Seung-Won , Kim Kyeong-Seop , Song Chul-Gyu , Lee Jeong-Whan , Kim Jeong-Hwan , Jeung Gyeo-Wun 
Published: August 2015

Abstract The very first step to process electrocardiogram (ECG) signal is to eliminate baseline wandering interference that is usually caused by electrode-skin impedance mismatch, motion artifacts due to a patient’s body moment or respiratory breathing. A new method is thus suggested to remove baseline wandering in ECG by improving the detrending method that was originally proposed for eliminating slow non-stationary trends from heart rate variability (HRV). In our proposed method, a global trend is estimated in terms of baseline wandering by merging the local trend based on an ECG segment that represents a part of the ECG signal. The experimental results show that the improved detrending method can efficiently resolve baseline wandering without distorting any morphological characteristic embedded in the ECG signal in no time delay manner.
A novel similarity comparison approach for dynamic ECG series
Authors: Yin Hong , Zhu Xiaoqian , Ma Shaodong , Yang Shuqiang , Chen Liqian 
Published: August 2015

Abstract The heart sound signal is a reflection of heart and vascular system motion. Long-term continuous electrocardiogram (ECG) contains important information which can be helpful to prevent heart failure. A single piece of a long-term ECG recording usually consists of more than one hundred thousand data points in length, making it difficult to derive hidden features that may be reflected through dynamic ECG monitoring, which is also very time-consuming to analyze. In this paper, a Dynamic Time Warping based on MapReduce (MRDTW) is proposed to make prognoses of possible lesions in patients. Through comparison of a real-time ECG of a patient with the reference sets of normal and problematic cardiac waveforms, the experimental results reveal that our approach not only retains high accuracy, but also greatly improves the efficiency of the similarity measure in dynamic ECG series.
EEG-based research on brain functional networks in cognition
Authors: Wang Niannian , Zhang Li , Liu Guozhong 
Published: August 2015

Abstract Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders.
EEG functional network properties related to visually induced unrecognized spatial disorientation
Authors: Li Yan , Chen Yuanyuan , Lv Xiaoning , Yang Yang , Li Jing , Hao Chenru , Li Minggao , Pu Fang 
Published: August 2015

Abstract Unrecognized spatial disorientation (SD) which is intimately linked with brain cognitive function is always a fatal issue for the safety of pilots. To explore its effects on human brain cognitive functions, electroencephalography (EEG) functional network analysis methods were adopted to examine topological changes in the connection of cognitive regions when experiencing unrecognized SD. Twelve male pilots participated in the study. They were subjected to a SD scene, namely visual rotation, which evoked unrecognized SD. For the main EEG frequency intervals, the phase lag index (PLI) and normalized mutual information (NMI) were calculated to quantify the EEG data. Then weighted connectivity networks were constructed and their properties were characterized in terms of an average clustering coefficient and global efficiency. A T-test was performed to compare PLI, NMI and network measures under unrecognized SD and non-SD conditions. It indicated a weak functional connectivity level in the theta band under unrecognized SD based on the significant decrease of mean values of PLI and NMI (p<0.05). Meanwhile, both the average clustering coefficient and global efficiency in the theta band reduced under the unrecognized SD condition. The decrease of the average clustering coefficient and global efficiency demonstrates a lack of small-world characteristics and a decline in processing efficiency of brain cognitive regions. All the experimental results show that unrecognized SD may have a negative effect on brain functional networks in the theta band.
Nonholonomic mobile system control by combining EEG-based BCI with ANFIS
Authors: Yu Weiwei , Feng Huashan , Feng Yangyang , Madani Kurosh , Sabourin Christophe 
Published: August 2015

Abstract Motor imagery EEG-based BCI has advantages in the assistance of human control of peripheral devices, such as the mobile robot or wheelchair, because the subject is not exposed to any stimulation and suffers no risk of fatigue. However, the intensive training necessary to recognize the numerous classes of data makes it hard to control these nonholonomic mobile systems accurately and effectively. This paper proposes a new approach which combines motor imagery EEG with the Adaptive Neural Fuzzy Inference System. This approach fuses the intelligence of humans based on motor imagery EEG with the precise capabilities of a mobile system based on ANFIS. This approach realizes a multi-level control, which makes the nonholonomic mobile system highly controllably without stopping or relying on sensor information. Also, because the ANFIS controller can be trained while performing the control task, control accuracy and efficiency is increased for the user. Experimental results of the nonholonomic mobile robot verify the effectiveness of this approach.
The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer’s disease
Authors: Wang Gang , Shepherd Simon J. , Beggs Clive B. , Rao Nini , Zhang Ying 
Published: August 2015

Abstract The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer’s disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients’ EEG are positive and much higher than that of the controls. This finding encourages us to introduce a kurtosis-based de-noising method. The 21-electrode EEG is first decomposed using independent component analysis (ICA), and second sort them using their kurtoses in ascending order. Finally, the subspace of EEG signal using back projection of only the last five components is reconstructed. SE will be calculated after the above de-noising preprocess. The classifications show that this method can significantly improve the accuracy of SE-based diagnosis. The kurtosis analysis of EEG may contribute to increasing the understanding of brain dysfunction in AD in a statistical way.
A study of sleep staging based on a sample entropy analysis of electroencephalogram
Authors: Li Hui , Peng Cheng , Ye Datian 
Published: August 2015

Abstract In this paper we report a detection method for different sleep stages and it is based on a single-channel electroencephalogram (EEG) system. The system is simple and can be easily setup in homes to perform sleep EEG recording, overnight sleep EEG automatic staging, and sleep quality evaluation. EEG data of 14 sleeping subjects were recorded through the entire night. All subjects were within the age group of 20-30 years and having no significant sleep disorders. To analyze the EEG data, it is segmented into equal time intervals. This is followed by calculation of Sample Entropy (SampEn) for each section, and the SampEn’s statistical characteristics, such as the median, upper quartile, lower quartile and inter-quartile range. The sleep data were divided into training group (7 cases) and test group (7 cases). Sleep stages’ quantitative ranges of training group referring to ZEO results were extracted and the quantization range used to sleep staging EEG data. Both the training group and test group results were close to ZEO results. It suggested that the statistical characteristics of Sample Entropy could be used as a criterion for sleep staging and evaluation.
Detecting diffuse axonal injury in rat brainstems by diffusion tensor imaging and AQP4 expression
Authors: Zheng Wenbin , Ma Chunlin , Kong Lingmei , Chen Xiran , Fan Wenyao 
Published: August 2015

Abstract This study investigated the correlation between AQP4 expression and DTI in rat brainstems after diffuse axonal injuries (DAI). Forty rats were imaged before injury and reimaged at 3, 6, 12, 24 and 72 h post-injury. A control group of 8 rats was imaged and sacrificed for histology but not injured. After brain injury, AQP4 expression and ADC values in the brainstems increased gradually, reaching peak values at 24 h and 12 h, respectively. FA values decreased within 72 h. There was a negative correlation between ADC values and brainstem AQP4 expression at 12 h, and a positive correlation at 24 h or 72 h ([Formula: see text]), respectively. Changes in the ADC and FA values in the brainstems indicated brain edema and severe axonal injuries. The correlations between AQP4 expression and time-dependent ADC values aid in understanding brain edema development after DAI.
An empirical comparison of different LDA methods in fMRI-based brain states decoding
Authors: Xia Maogeng , Song Sutao , Yao Li , Long Zhiying 
Published: August 2015

Abstract Decoding brain states from response patterns with multivariate pattern recognition techniques is a popular method for detecting multivoxel patterns of brain activation. These patterns are informative with respect to a subject’s perceptual or cognitive states. Linear discriminant analysis (LDA) cannot be directly applied to fMRI data analysis because of the “few samples and large features” nature of functional magnetic resonance imaging (fMRI) data. Although several improved LDA methods have been used in fMRI-based decoding, little is known regarding the relative performance of different LDA classifiers on fMRI data. In this study, we compared five LDA classifiers using both simulated data with varied noise levels and real fMRI data. The compared LDA classifiers include LDA combined with PCA (LDA-PCA), LDA with three types of regularizations (identity matrix, diagonal matrix and scaled identity matrix) and LDA with optimal-shrinkage covariance estimator using Ledoit and Wolf lemma (LDA-LW). The results indicated that LDA-LW was the most robust to noises. Moreover, LDA-LW and LDA with scaled identity matrix showed better stability and classification accuracy than the other methods. LDA-LW demonstrated the best overall performance.
Study on the diagnostic system of scoliosis by using infrared camera
Authors: Jeong Jin-hyoung , Park Eun-jeong , Cho Chang-ok , Kim Yoon-jeong , Lee Sang-sik 
Published: August 2015

Abstract In this study, the radiation generated in the diagnosis of scoliosis, to solve the problems by using an infrared camera and an optical marker system that can diagnose scoliosis developed. System developed by the infrared camera attached to the optical spinal curvature is recognized as a marker to shoot the angle between the two optical markers are measured. Measurement of angle, we used the Cobb’s Angle method used in the diagnosis of spinal scoliosis. We developed a software to be able to output to the screen using an infrared camera to diagnose spinal scoliosis. Software is composed of camera output unit was manufactured in Labview, angle measurement unit, in Cobb’s Angle measurement unit. In the future, kyphosis, Hallux Valgus, such as the diagnosis of orthopedic disorders that require the use of a diagnostic system is expected case.
Computer-aided detection of lung nodules using outer surface features
Authors: Demir Önder , Yılmaz Çamurcu Ali 
Published: August 2015

Abstract In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.
Enhancement of objects in photoacoustic tomography using selective filtering
Authors: Shin Dong-ho , Yang Yoonseok , Song Chul-Gyu 
Published: August 2015

Abstract Here we developed a real-time photoacoustic tomography (PAT) imaging acquisition device based on the linear array transducer utilized on ultrasonic devices. Also, we produced a phantom including diverse contrast media and acquired PAT imaging as the light source wavelength was changing to see if the contrast media reacted. Indocyanine green showed the highest reaction around the 800-nm band, methylene blue demonstrated the same in the 750-nm band, and gold nanoparticle showed the same in the 700-nm band. However, in the case of superparamagnetic iron oxide, we observed not reaction within the wavelength bands used herein to obtain imaging. Moreover, we applied selective filtering to the acquired PAT imaging to remove noise from around and reinforce the object’s area. Consequentially, we could see the object area in the imaging was effectively detected and the image noise was removed.
Detection of Perlger-Huet anomaly based on augmented fast marching method and speeded up robust features
Authors: Sun Minglei , Yang Shaobao , Jiang Jinling , Wang Qiwei 
Published: August 2015

Abstract Pelger-Huet anomaly (PHA) and Pseudo Pelger-Huet anomaly (PPHA) are neutrophil with abnormal morphology. They have the bilobed or unilobed nucleus and excessive clumping chromatin. Currently, detection of this kind of cell mainly depends on the manual microscopic examination by a clinician, thus, the quality of detection is limited by the efficiency and a certain subjective consciousness of the clinician. In this paper, a detection method for PHA and PPHA is proposed based on karyomorphism and chromatin distribution features. Firstly, the skeleton of the nucleus is extracted using an augmented Fast Marching Method (AFMM) and width distribution is obtained through distance transform. Then, caryoplastin in the nucleus is extracted based on Speeded Up Robust Features (SURF) and a K-nearest-neighbor (KNN) classifier is constructed to analyze the features. Experiment shows that the sensitivity and specificity of this method achieved 87.5% and 83.33%, which means that the detection accuracy of PHA is acceptable. Meanwhile, the detection method should be helpful to the automatic morphological classification of blood cells.
An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction
Authors: Liu Fei , Zhang Xi , Jia Yan 
Published: August 2015

Abstract In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top ([Formula: see text]) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top ([Formula: see text]) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods’ high accuracy and high efficiency.
MEMS-based system and image processing strategy for epiretinal prosthesis
Authors: Xia Peng , Hu Jie , Qi Jin , Gu Chaochen , Peng Yinghong 
Published: August 2015

Abstract Retinal prostheses have the potential to restore some level of visual function to the patients suffering from retinal degeneration. In this paper, an epiretinal approach with active stimulation devices is presented. The MEMS-based processing system consists of an external micro-camera, an information processor, an implanted electrical stimulator and a microelectrode array. The image processing strategy combining image clustering and enhancement techniques was proposed and evaluated by psychophysical experiments. The results indicated that the image processing strategy improved the visual performance compared with direct merging pixels to low resolution. The image processing methods assist epiretinal prosthesis for vision restoration.
Robust boundary detection of left ventricles on ultrasound images using ASM-level set method
Authors: Zhang Yaonan , Gao Yuan , Li Hong , Teng Yueyang , Kang Yan 
Published: August 2015

Abstract Level set method has been widely used in medical image analysis, but it has difficulties when being used in the segmentation of left ventricular (LV) boundaries on echocardiography images because the boundaries are not very distinguish, and the signal-to-noise ratio of echocardiography images is not very high. In this paper, we introduce the Active Shape Model (ASM) into the traditional level set method to enforce shape constraints. It improves the accuracy of boundary detection and makes the evolution more efficient. The experiments conducted on the real cardiac ultrasound image sequences show a positive and promising result.
Automatic extraction of 3D anatomical feature curves of hip bone models reconstructed from CT images
Authors: Liu Hao , Qian Hongbo , Zhao Jianning 
Published: August 2015

Abstract 3D anatomical feature curves (AFC) on bone models reconstructed from CT/MRI images are important in some fields, such as preoperative planning, intra-operative navigation, patient-specific prosthesis design, etc. Interactive extraction of feature curves on patient-specific bone models is time-consuming, has low repeatability and accuracy. This paper presents a computer graphics method to automatically extract AFC from 3D hip bone models reconstructed from CT images. A DCSS (direct curvature scale space)-based technique is firstly used to extract anatomical feature points (AFP) in every contour, using anatomical structure information as prior knowledge so that AFP are extracted and only extracted. Then, corresponding AFP are linked in different contours and AFC is generated. AFC obtained by our method were compared with those interactively extracted by three surgeons, which showed that our method is feasible (Dice coefficient: 0.94; Average symmetric surface distance: 3.97 mm). The method was also applied to identify anatomical landmarks, which showed that our method is superior to the curvature-based methods that fail to identify landmark regions or have too many redundant regions, which results in failures to subsequently label landmark regions using pre-defined spatial adjacency matrices.