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We propose a simple method for fast and label-free identification of early-stage cervical cancerous tissues using a metamaterial (MM) terahertz (THz) biosensor with two resonant absorption frequencies at 0.286 THz and 0.850 THz, respectively. Because the two resonant absorption frequencies are very sensitive to the ambient change of dielectric properties, the cervical cancerous tissues can be distinguished from the normal tissues by analyzing the resonant frequency shift. The calculated sensitivity of the low resonant frequency and that of the high resonant frequency are 29 and 74 GHz/RIU, respectively. In the experiment, the cervical cancerous tissue samples were taken from different patients, and the cancerous and normal parts were determined by hematoxylin-eosin (HE) stain. Compared with the traditional method of directly detecting cancer tissue without the sensor, this method has the advantages of being simple, label free and fast. In addition, compared with biosensors which have a single resonant absorption frequency, the biosensors with double resonant absorption frequencies have higher accuracy. This method potentially can be applied for the detection and diagnosis of many other cancer tissues.

Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN, RMSSD, LF, HF, SD1, and SD2 (0.99, 0.89, 0.84, 0.89, 0.90 and 0.95, respectively).

Background Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly infectious and concealed virus that causes pneumonia, severe acute respiratory syndrome, and even death. Although the epidemic has been controlled since the development of vaccines and quarantine measures, many people are still infected, particularly in third‐world countries. Several methods have been developed for detection of SARS‐CoV‐2, but owing to its price and efficiency, the immune strip could be a better method for the third‐world countries. Methods In this study, two antibodies were linked to latex microspheres, using 1‐(3‐dimethylaminopropyl)‐3‐ethylcarbodiimide hydrochloride and N‐hydroxysuccinimide, as the bridge to decrease the cost further and improve the detection performance. The specificity of the lateral flow immunoassay strip (LFIA) was tested by several common viruses and respiratory bacterial infections. Besides, the reproducibility and stability of the LFIAs were tested on the same batch of test strips. Under optimal conditions, the sensitivity of LFIA was determined by testing different dilutions of the positive specimens. Results The proposed LFIAs were highly specific, and the limit of detection was as low as 25 ng/mL for SARS‐CoV‐2 antigens. The clinical applicability was evaluated with 659 samples (230 positive and 429 negative samples) by using both LFIA and rRT‐PCR. Youden’s index (J) was used to assess the performance of these diagnostic tests. The sensitivity and specificity were 98.22% and 97.93%, respectively, and J is 0.9615. The sensitivity and specificity were 98.22% and 97.93%, respectively, and J is 0.9615. In addition, the consistency of our proposed LFIA was analyzed using Cohen's kappa coefficient (κ = 0.9620). Conclusion We found disease stage, age, gender, and clinical manifestations have only a slight influence on the diagnosis. Therefore, the lateral flow immunoassay SARS‐CoV‐2 antigen test strip is suitable for point‐of‐care detection and provides a great application for SARS‐CoV‐2 epidemic control in the third‐world countries., Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a highly infectious and concealed virus that causes pneumonia, severe acute respiratory syndrome, and even death. Although the epidemic has been controlled since the development of vaccines and quarantine measures, many people are still infected, particularly in third‐world countries. Several methods have been developed for detection of SARS‐CoV‐2, but owing to its price and efficiency, the immune strip could be a better method for the third‐world countries. However, some disadvantages of the strip can be fixed to decrease the cost and improve the performance. In this study, two antibodies were linked to latex microspheres, using 1‐(3‐dimethylaminopropyl)‐3‐ethylcarbodiimide hydrochloride and N‐hydroxysuccinimide, as the bridge to decrease the cost further and improve the detection performance. Unlike the reverse‐transcriptase‐polymerase chain reaction (rRT‐PCR) test, the results showed that the proposed strip had high specificity for SARS‐CoV‐2 antigen, and no cross‐reactions with other common viruses was observed. Most importantly, the detection limit was as low as 25 ng/ml, and the cost decreased to $0.15 per strip. Thus, the proposed SARS‐CoV‐2 antigen test strip offers a cheap, rapid, sensitive, specific, and visual method for SARS‐CoV‐2 antigen detection and shows great potential for COVID‐19 epidemic control in third‐world countries.

High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems.

Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines time-frequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices.

Sensors, 21 (12), ISSN:1424-8220

Photoplethysmography (PPG) is increasingly used in digital health, exceptionally in smartwatches. The PPG signal contains valuable information about heart activity, and there is lots of research interest in its means and analysis for cardiovascular diseases. Unfortunately, to our knowledge, there is no arrhythmic PPG dataset publicly available-this paper attempt to provide a toolbox that can generate synthesized arrhythmic PPG signals. The model of a single PPG pulse in this toolbox utilizes two combined Gaussian functions. This toolbox supports synthesizing PPG waveform with regular heartbeats and three irregular heartbeats: compensation, interpolation, and reset. The user can generate a large amount of PPG data with a certain irregularity, with different sampling frequency, time length, and a range of noise types (Gaussian noise and multi-frequency noise) can be added to the synthesized PPG which can all be modified from the interface, and different types of arrhythmic PPGs (as calculated by the model) generated. The generation for large PPG datasets that simulate PPG collected from real humans could be used for testing the robustness of developed algorithms that are targeting arrhythmic PPG signals. Our PPG synthesis tool is publicly available.

Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.

Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening.

Highly emissive semiconductor quantum dots (QDs) with tunable color are valuable in many applications such as solid state lighting and bio-imaging. Herein, we report a facile synthetic method to Au:ZnCdS and Au:ZnCdS/ZnS core/shell QDs with tunable emission color. The highly active Au precursor (HAuCl4) is prevented to be decomposed at high reaction temperature using 1-dodecanethiol (DDT) as the surface ligand. High-quality Au:ZnCdS/ZnS core/shell QDs are prepared and the highest photoluminescence (PL) quantum yield (QY) can achieve 42% by overcoating of ZnS layer over the bare Au:ZnCdS core QDs. Furthermore, through using Au+ ion as the primary dopants and trivalent cation In3+ as co-dopants, the PL QY can be enhanced significantly because compensation of In3+ ion-codoping for the charge imbalance from Au+-doping. This codoping strategy may be applied to other related optical materials to control the optical properties based on our understanding for physical mechanism.

Hypertension is a common chronic cardiovascular disease (CVD). Early screening and diagnosis of hypertension plays a major role in its prevention and in the control of CVDs. Our study discusses the early screening of hypertension while using the morphological features of photoplethysmography (PPG). Numerous morphological features of PPG and its derivative waves were defined and extracted. Six types of feature selection methods were chosen to screen and evaluate these PPG morphological features. The optimal features were comprehensively analyzed in relation to the physiological processes of the cardiovascular circulatory system. Particularly, the intrinsic relation and physiological significance between the formation process of systolic blood pressure (SBP) and PPG morphology features were analyzed in depth. A variety of linear and nonlinear classification models were established for the comparison trials. The F1 scores for the normotension versus prehypertension, normotension and prehypertension versus hypertension, and normotension versus hypertension trials were 72.97%, 81.82%, and 92.31%, respectively. In summary, this study established a PPG characteristic analysis model and established the intrinsic relationship between SBP and PPG characteristics. Finally, the risk stratification of hypertension at different stages was examined and compared based on the optimal feature subset.

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.

In this paper, we design an L-shaped MEMS microspring. The formula for the spring constant and the stress distribution of the microspring are worked out in theory, which are proved correctly by FEM (Finite element method) simulation. Four L-shaped microsprings named as #1, #2, #3, #4 with different dimensions are fabricated. The tensile experiment and FEM also show that the fractured positions are all located in the corner of the microspring. And each stage of deformation of the microspring is the same, which is proved by the image captured by CCD during the tensile experiment. When the microspring is applied to a shock load, the upper end of the microspring has a much larger deformation than others, which is different from that of the static load. It offers reference for latter MEMS microspring design.

Open clinical trial data provide a valuable opportunity for researchers worldwide to assess new hypotheses, validate published results, and collaborate for scientific advances in medical research. Here, we present a health dataset for the non-invasive detection of cardiovascular disease (CVD), containing 657 data segments from 219 subjects. The dataset covers an age range of 20–89 years and records of diseases including hypertension and diabetes. Data acquisition was carried out under the control of standard experimental conditions and specifications. This dataset can be used to carry out the study of photoplethysmograph (PPG) signal quality evaluation and to explore the intrinsic relationship between the PPG waveform and cardiovascular disease to discover and evaluate latent characteristic information contained in PPG signals. These data can also be used to study early and noninvasive screening of common CVD such as hypertension and other related CVD diseases such as diabetes.

A photoplethysmogram (PPG) contains a wealth of cardiovascular system information, and with the development of wearable technology, it has become the basic technique for evaluating cardiovascular health and detecting diseases. However, due to the varying environments in which wearable devices are used and, consequently, their varying susceptibility to noise interference, effective processing of PPG signals is challenging. Thus, the aim of this study was to determine the optimal filter and filter order to be used for PPG signal processing to make the systolic and diastolic waves more salient in the filtered PPG signal using the skewness quality index. Nine types of filters with 10 different orders were used to filter 219 (2.1s) short PPG signals. The signals were divided into three categories by PPG experts according to their noise levels: excellent, acceptable, or unfit. Results show that the Chebyshev II filter can improve the PPG signal quality more effectively than other types of filters and that the optimal order for the Chebyshev II filter is the 4th order.

The most common method used for minimizing the occurrence of diabetes complications is frequent glucose testing to adjust the insulin dose. However, using blood glucose (BG) meters presents a risk of infection. It is of great importance to develop non-invasive BG detection techniques. To realize high-accuracy, low-cost and continuous glucose monitoring, we have developed a non-invasive BG detection system using a mixed signal processor 430 (MSP430) microcontroller. This method is based on the combination of the conservation-of-energy method with a sensor integration module, which collects physiological parameters, such as the blood oxygen saturation (SPO2), blood flow velocity and heart rate. New methods to detect the basal metabolic rate (BMR) and BV are proposed, which combine the human body heat balance and characteristic signals of photoplethysmography as well dual elastic chambers theory. Four hundred clinical trials on real-time non-invasive BG monitoring under suitable experiment conditions were performed on different individuals, including diabetic patients, senior citizens and healthy adults. A multisensory information fusion model was applied to process these samples. The algorithm (we defined it as DCBPN algorithm) applied in the model combines a decision tree and back propagation neural network, which classifies the physiological and environmental parameters into three categories, and then establishes a corresponding prediction model for the three categories. The DCBPN algorithm provides an accuracy of 88.53% in predicting the BG of new samples. Thus, this system demonstrates a great potential to reliably detect BG values in a non-invasive setting.

A new needle-to-droplet electrode structure with a micro discharge gap (2 mm) was designed to achieve direct current (DC) discharge plasma in ambient air with the aim of using the plasma to sterilize liquids. Without using noble gases or an external air flow, we succeeded in generating both a negative and positive DC plasma at atmospheric pressure. The plasma was driven by a 0 to −20,000 V, 100 W DC power supply. A stainless steel needle with a tip diameter of [Formula: see text] and a 200-[Formula: see text] droplet of bacteria-containing liquid served as the electrodes. At atmospheric pressure and room temperature (23[Formula: see text]C), utilizing the negative DC plasma, the discharge time lasted 10 s; the results showed that the higher the discharge voltage, the more efficient the sterilization effect. Conversely, when we applied a voltage of −5.5 kV, we found that the sterilization effect was more efficient for longer discharge times. Our findings demonstrate that Escherichia coli (E. coli) and Bacillus subtilis (B. subtilis) can be killed in about 30 s. Our experiments show that our sterilization method required less time and was more efficient for positive than for negative DC plasma under the same conditions.

Rationale and Objectives The aim of this study was to develop a pulmonary nodule viewing system to visualize and retrieve data from the Lung Image Database Consortium. Materials and Methods The pulmonary nodule viewing system, developed using Microsoft C++ and the .NET 2.0 Framework, is composed of a clinical information integrator, a nodule viewer, a search engine, and a data model. Results A pulmonary nodule viewing system using Lung Image Database Consortium data for computer-aided diagnosis research and training purpose was developed. Conclusions The pulmonary nodule viewing system can be used to build a pulmonary nodule database for computer-aided diagnosis research and medical education. It can also be used to view and retrieve large data sets efficiently.

Diffusion weighted images are affected by several artifacts and noise which complicate the analysis and interpretation of diffusion tensor image. The method we present in this thesis works for the denoising of diffusion weighted images, which based on the theory of adaptive wiener method and takes into account the multi-boundary character of the diffusion weighted image. In this thesis we modified the adaptive wiener method to obtain more reliable filter result when it is performed on the multi-boundary DWI image. The method was illustrated by the synthetic and real data and the result has supported the filtering methodology proposed in this thesis.

This paper deals with the problem of obtaining and analyzing texture features in medical images. For this purpose we make use of a method based on the multifractal concept to compute the characteristic vector of fractal dimension (MFanvg) for CT images of normal liver or lung, CT images of abnormal liver or lung, MRI images of them and so on. It was discovered from the results, whether we implement multifractal analysis based on multi-resolution to CT images or MRI images, that each element of the MFanvg had no difference for normal parenchyma images but not for abnormal images. These differences can be testified by the Variance (Var) of characteristic vector MFanvg, of which the Var value of normal parenchyma image is less than 0.01 but abnormal more than 0.01.

Based on our previous work, we develop a classifier using support vector machine to differentiate paroxysmal atrial fibrillation (AF) from sustained AF and propose an automatic scheme to predict the spontaneous termination of paroxysmal AF. Experimental studies over the challenge database of Physionet/CinC 2004 show that our method is able to achieve high classification accuracy and is fairly reliable. This in turn justifies our previous work.

A new method for noninvasive temperature estimation is proposed in this paper. Noninvasive temperature estimation is one of key problems of high-intensity focused ultrasound (HIFU). In tissue, temperature changes, caused by HIFU, cause gray variation of the B-mode ultrasonic image, which can be used to estimate temperature. In this paper, the relation between temperature and the energy of real-valued Gabor transform of difference image is studied. In 2D real-valued discrete Gabor transform (RDGT), the information, which is response to the temperature, can be detached from the other information such as noise. It is advantageous to estimating temperature. The in-vitro fresh pork tissue is used in the experiments. Experimental results show the relation between temperature and energy of RDGT is approximately linearity, and noninvasive estimation based on RDGT has a high temperature resolution. The RDGT can be applied to estimate temperature.

In this paper, we develop a new non-invasive method for atrial activity estimation in atrial fibrillation (AF) analysis. Our central approach is base on minimization of mutual information. To establish an appreciate separation model for independent component analysis (ICA), statistical properties of bioelectric independent sources corresponding to the ventricular and atrial activity (AA) are analyzed. During evaluation with real AF data, the proposed method is validated using two spectral parameters extracted from AA signals: main peak frequency (MPF) and spectral concentration (SC). Our results show that MPF ranges from 3.63 to 7.56 Hz and SP has a mean of 42.73, which suggest that the proposed method for AA extraction is very efficient.

Edge detection is an important pre-processing step in image analysis. Conventionally, mathematical morphology edge detection methods use single and symmetrical structure elements. But they are difficult to detect complex edge feature, because they are only sensitive to image edge which has the same direction of structure elements. This paper proposed a novel edge detection algorithm based on multi-structure elements morphology of eight different directions. We got eight different edge detection results by using morphological gradient algorithm respectively, and final edge result was got by using synthetic weighted method. The experimental results showed that the proposed algorithm was more efficient for edge detection than conventional mathematical morphological edge detection algorithms and differential edge detection operators.