DNA quantification and characterization are of critical importance in disease diagnosis and clinical analysis, while label-free technology greatly simplifies the sensing protocol as it eliminates the extra step for attaching the indicator to DNA strands. In this work, we present a novel label-free DNA detection methodology based on electrical frequency-dependent impedance. The impedance of DNA strands conjunct with streptavidin-coated magnetic beads was measured at 8 different frequencies using an electrical impedance sensor integrated on a chip. Different concentrations of 300 bp double-stranded DNA samples were used to validate our sensor. The minimum DNA amount that could be successfully detected was 0.77 ng (3.9 amol). Detecting DNA fragments using our sensor could be further reduced from currently 20 minutes to under 15 minutes.
Accurate detection of diabetic macular edema (DME) is an important task in optical coherence tomography (OCT) images of the eye. A relatively simple and practical approach is proposed in this paper. A pre-trained convolutional neural network (CNN) is fine tuned for a classification of DME versus normal cases. The fine-tuned Inception-Resnet-v2 CNN model can effectively identify pathologies in comparison to classical learning. Experiments were carried out on the publicly available data set of the Singapore Eye Research Institute (SERI). The developed model was also compared to other fine tuned models, such as Resnet-50 and Inception-v3. The proposed method achieved 100% classification accuracy with the Inception-Resnet-v2 model using a leave-one-out cross-validation strategy. For robustness, the model trained on the SERI dataset was tested on another dataset provided by the Chinese University HongKong (CUHK), also with 100% accuracy. The proposed method is a potentially impactful tool for accurately detecting DME vs. normal cases.
The development of continuous glucose monitoring and insulin control algorithms have enabled the recent development of closed-loop artificial pancreas technology. However, despite these advancements, glucose sensor accuracy and reliability under physiologic conditions and over long periods of monitoring continue to be limiting factors in achieving a truly closed-loop artificial pancreas. To develop improved sensor technology, glucose sensor dynamics and performance need to be modeled accurately under physiologic conditions. A three dimensional hydrogen-based glucose sensor model was developed to predict steady-state sensor performance. The finite element model, which included a three-electrode system and relevant electrochemical reactions for electrochemical current calculation, was developed on COMSOL Multiphysics software. The results were validated using an experimental setup measuring various hydrogen peroxide concentrations ranging from 5 mM to 35 mM. The model predicted a linear relationship between current ranging from $5 . 1 \mu A$ to $35 . 8 \mu A$ for the aforementioned glucose concentrations. Experimental data demonstrated a linear relationship between hydrogen peroxide concentration within the same range, and current measurements ranging from $9 . 4 \mu A$ to $60 . 6 \mu A$. The model and experimental data differed consistently by percentages between 40-46 % for all concentrationstested. This consistent scaling error can be attributed to the difference in electrode geometric area and electrochemical active area. Future iterations of the model must take into consideration the effective electrode area.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is associated with atypical connectivity within and between brain regions. In this study, we attempted to classify functional Magnetic Resonance Images (fMRI) of Typically Developing (TD) and ASD participants using conditional random forest and random forest. Restingstate fMRI images of TD and ASD participants (N=320 for training and N=80 for validation) were obtained from the Autism Imaging Data Exchange; ABIDE-I, ABIDE-II. Images were preprocessed using a standard pipeline. A Functional Connectivity (FC) matrix was calculated using 237 cortical, subcortical, and cerebellar Regions of Interest (ROIs). The dimensionality of the FC matrix was reduced using conditional random forests and at each dimension classification accuracy was tested using random forests. Results suggest that in the current dataset, the random forest is able to classify the TD and ASD with a peak accuracy of 65% using 143 features. Remarkably, the Cingulo-Opercular Task Control (COTC) region contributed the highest number of features linked to more accurate classification, and connectivity between COTC and the dorsal attention network distinguished ASD and TD participants.
Several research arenas and clinical applications are reliant on biopotential recordings, such as electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), and neural interfaces including brain computer interface (BCI). Here, we present a low-cost, biopotential, acquisition hardware platform board (PSUEEG platform) suitable for a wide range of recording tasks. Implementations of the hardware include applications requiring 8 or 16 channels of biopotential recordings, and 3-axis accelerometer measurements, among other modalities. The device firmware allows for flexible software configuration through USB. Power and data are transmitted between the device and base computer through an electrically isolated USB. The device is compatible with a range of computer operating systems, including Windows, Linux, and OSX. Additionally, we have crafted data acquisition under a range of programming platforms, including C++, Python, MATLAB Simulink, and LabView. Notably, we have demonstrated the interface with the Matlab PsychToolbox and the popular BCI2000 platform. The acquisition system with can be used in educational and research-based applications, neural interfaces, and clinical interfaces. For education and research, we have utilized this platform in BCI work, as well as demonstrated comparable classification performance for different paradigms.
Arrhythmia refers to any abnormal change from the normal electrical impulses of the heart. Some arrhythmias are manifested as abnormal heartbeat. Effective heartbeat classification is helpful for computer aided diagnosis. Conventional heartbeat classification methods work on information of multiple leads, and need heuristic or hand-crafted feature extraction. In this paper, we propose a new heartbeat classification approach based on a recent deep learning architecture called multi-scale convolutional neural networks (MCNN). A unique feature of our work is that we take single lead ECG as input, rhythm information is not taken into consideration. Such a single-lead setting, although more challenging than multi-lead cases, is often faced in medical practice due to advancements in mobile ECG devices and hence much needed. We exploit the power of convolutional neural networks for find discriminative features in heartbeat time series. The algorithm was tested on public datasets. The overall accuracy is 0.8866, the accuracy on supraventricular ectopic beat is 0.9600, and accuracy on ventricular ectopic beat is 0.9250. The performance is comparable with conventional method using features hand crafted by human experts.
Wrong-way driving on highways is an important issue in many countries as it can potentially put the lives of many at risk. In Japan, approximately 200 instances of wrong-way driving occur annually, and preventative countermeasures, such as road arrows, have been implemented. However, the incidence of wrong-way driving has not decreased since the introduction of these countermeasures, and stronger countermeasures are therefore necessary. More than 70% of wrong-way drivers are elderly individuals, and, in Japan, over 30% of elderly individuals have diseases leading to cognitive decline. In this paper, we focus on the reduction of visual cognitive function due to mild cognitive impairment (MCI), and the effects of visual countermeasures on patients with MCI, as determined using a computer graphics movie and an infrared eye tracker to investigate gaze movements. We analyzed differences in fixation points and the quantity of saccades between patients with MCI and healthy individuals. Patients with MCI were found to have delayed identification of wrong-way driving. This suggests that deficits in visual attention and deterioration of visual cognitive function in dynamic environments may be factors underlying wrong-way driving in patients with MCI.
Mathematical modeling is a widely used technique for describing the temporal behavior of biological systems. One of the most challenging topics in computational systems biology is the calibration of nonlinear models, i.e. the estimation of their unknown parameters. The state of the art methods in this field are the frequentist and Bayesian approaches. For both of them, the performances and accuracy of results highly depend on the sampling technique employed. Here, we test a novel Bayesian procedure for parameter estimation, called Conditional Robust Calibration (CRC), comparing two different sampling techniques: uniform and logarithmic Latin Hypercube Sampling (LHS). CRC is an iterative algorithm based on parameter space sampling and on the estimation of parameter density functions. We apply CRC with both sampling strategies to the Lotka-Volterra model and we obtain a more precise and reliable solution through logarithmically spaced samples.
Many algorithms have been used to estimate respiratory rate (RR) from Photoplethysmography (PPG) recently. However, the accuracy and time consumption are still a challenging issue. In this paper, we propose a novel algorithm for RR estimation using Joint Sparse Signal Reconstruction (JSSR) based on Regularized Sparsity Adaptive Matching Pursuit (RSAMP) in a real-time fashion. The algorithm has been tested on Capnobase dataset and the results showed that the mean absolute error (MAE) and root mean squared error between estimates and references are 1.09 breaths per minute (bpm) and 2.44 bpm, respectively. And our method only costs 0.54 seconds for calculation.
Effective detection of arrhythmia is an important task in the remote monitoring of electrocardiogram (ECG). The traditional ECG recognition depends on the judgment of the clinicians' experience, but the results suffer from the probability of human error due to the fatigue. To solve this problem, an ECG signal classification method based on the images is presented to classify ECG signals into normal and abnormal beats by using two-dimensional convolutional neural networks (2D-CNNs). First, we compare the accuracy and robustness between one-dimensional ECG signal input method and two-dimensional image input method in AIexNet network. Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AIexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification. The performance evaluated on the MIT-BIH arrhythmia database demonstrates that the proposed method can achieve the accuracy of 98% and maintain high accuracy within SNR range from 20 dB to 35 dB. The experiment shows that the 2D-CNNs initialized with AIexNet weights performs better than one-dimensional signal method without a large-scale dataset.
Brain morphometry study plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features rooted in surface foliation theory. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer's disease and healthy control subjects demonstrate the efficiency and efficacy of our method.
The present paper shows the design a development and on-line measure system for blood urea concentration during dialysis treatment, using non-invasive technical by employ the optical sensing in the visible range. Previous results obtained in Military Hospital Centre in Mexico DF., displayed experimental evidence of a clear correlation between blood urea contents and optical absorbance in the red region obtained through the arterial line in hemodialysis machine. Our hypothesis considers that the cause of this dependence is the volume change of the red cells with the urea concentration due to osmotic pressure and regulation mechanism in cellular volume. The reduction in the cells volume would lead to decrease in the proportional absorbance value. Measurements were performed during hemodialysis procedures in the Military Hospital Center in Mexico City, show good correlation. The most relevant interfering parameter was the water extraction along the dialysis treatment, given that changes in the blood fluid and arterial pressure (hypertension, hypotension), thus affecting the measurement principle
The electrocardiogram (ECG) has been widely used for diagnosis purposes of heart diseases. Good quality ECG are utilized by the physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. One prominent artifact is the high frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes. Noise severely limits the utility of the recorded ECG and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG denoising. In this paper, we proposed a new ECG denoising method based on the recently developed Empirical Mode Decomposition (EMD). The proposed EMD-based method is able to remove high frequency noise with minimum signal distortion. The method is validated through experiments on the MIT-BIH database. Both quantitative and qualitative results are given. The results show that the proposed method provides very good results for denoising.
In a cryo electron microscopy experiment, the data is noisy 2-D projection images of the 3-D electron scattering intensity where the orientation of the projections is not known. In previous work we have developed a solution for this problem based on a maximum likelihood estimator that is computed by an expectation maximization algorithm. In the expectation maximization algorithm the expensive step is the expectation which requires numerical evaluation of 3- or 5-dimensional integrations of a square matrix of dimension equal to the number of Fourier series coefficients used to describe the 3-D reconstruction. By taking advantage of the rotational properties of spherical harmonics, we can reduce the integrations of a matrix to integrations of a scalar. The key property is that a rotated spherical harmonic can be expressed as a linear combination of the other harmonics of the same order and the weights in the linear combination factor so that each of the three factors is a function of only one of the Euler angles describing the orientation of the projection. Numerical example of the reconstructions is provided based on Nudaurelia Omega Capensis virus
This work investigates arm acceleration as a control signal for functional electrical stimulation (FES) of the upper limb during reaching and grasping. We segment the reach and grasp motion into phases and present an artificial neural network (ANN) approach that estimates the phase of the reaching cycle from accelerometer signals. We then select the stimulator command that maximizes successful triggering without unnecessary risk to the patient's safety. Our results suggest that the algorithm successfully generalizes between sessions and patients but is less successful at generalizing between different motions
In this paper, we present a technique for optimizing a fuzzy system using a genetic algorithm that works for patient status monitoring in the operating room. The genetic algorithm adjusts rule weights, outputs, and input membership functions to maximize the area under a receiver operator curve (ROC) for final classification. Compared to pre-optimization, the optimized fuzzy inference system increased ROC area from 0.68 to 0.77, which can be translated to an increase in specificity from 74% to 82%, at a fixed sensitivity of 58%
An accurate system model is essential for reconstructing high-resolution images. While the system response of a positron emission tomography (PET) system can be measured directly, the process is often difficult to perform and time-consuming. Here we propose a maximum likelihood method for estimating the detector response from projections of a point source at a set of radial locations in the field of view. The detector response functions for all radial bins are estimated simultaneously. No interpolation is required. Compared to the method of direct measurement, the proposed approach requires less measurements and results in sparse system matrix because a factored system matrix is used, which reduces the reconstruction time. We conducted computer simulation to demonstrate the feasibility of the proposed method
In this work we present a technique for applying Blind Source Separation (BSS) to single channel recordings of Electromagnetic (EM) brain signals. Single channel recordings of brain signals are preprocessed through the method of delays, and the delay matrix processed with the BSS technique described here called LSDIAGTD which uses temporal decorrelation to implement the now popular Independent Component Analysis (ICA) algorithm. This allows the identification and extraction of statistically independent sources underlying these single channel recordings. In particular we depict the analysis of single channel recordings from a Brain-Computer Interfacing paradigm. We show that BSS technique applied in this way extracts a series of codebook vectors representing the spectral content underlying the recorded signal. It then becomes possible to identify and extract particular rhythmic activity underlying the recordings. We show that rhythmic activity in the 8 to 12Hz band can be extracted in the case of imagined hand movements for a particular BCI paradigm.
In the present paper, we propose a new pulse measurement methodology based on thermal imaging (contact-free). The method capitalizes both on the thermal undulation produced by the traveling pulse as well as the periodic expansion of the compliant vessel wall. The paper reports experiments on 34 subjects, where it compares the performance of the new pulse measurement method to the one we reported previously. The measurements were ground-truthed through a piezo-electric sensor. Statistical analysis reveals that the new imaging methodology is more accurate and robust than the previous one. Its performance becomes nearly perfect, when the vessel is not obstructed by a thick fat deposit.
The system which provides short-term cardiovascular regulation has a very complex structure, resulting from the non-linear interaction among several different mechanisms: they include baroreceptors, peripheral chemoreceptors, lung-stretch receptors, a direct CNS response to hypoxia and hypercapnia, local vessel response to changes in blood gas content. Furthermore, during dynamic exercise a feedforward mechanism anticipates cardiovascular requirements, and interacts with the respiratory and muscle pumps. Aim of this work is to summarize the complexity of this system, and to point out the role of individual mechanisms and their mutual relations, with the help of a comprehensive mathematical model developed by the authors in previous years. Examples of system response are discussed during various acute cardiovascular perturbations (pressure changes changes in blood gas content, dynamic exercise) and model results compared with existing data in the literature. These examples emphasize the great complexity, richness and variability of the autonomic cardiovascular control system. Simulations suggest that mathematical models and computer simulation techniques may represent essential tools to comprehend and deepen our understanding of complex, multifactorial systems, the behaviour of which cannot be fully revealed by simple qualitative analysis. Models play an important role in modern physiology as a repository of knowledge and to integrate disparate data inside an integrative coherent structure.
