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Glycated hemoglobin A1c (HbA1c) is regarded as a gold standard to evaluate long-term blood glucose control, and it is also a crucial metric in diabetes screening, diagnosis, and management. However, thus far, the HbA1c measurement methods are invasive and painful. Considering that HbA1c levels are associated with cardiovascular autonomic neuropathy, in this paper, a novel Electrocardiogram (ECG)-based approach was presented for noninvasive and fast detection of HbA1c levels using 60-second, single-lead ECG waveform. For this purpose, a total of 317,105 ECG datasets encompassing 370 patients with diabetes were obtained using wearable devices. Furthermore, the ECG preprocessing was based on autocorrelation analysis. The convolutional neural networks with multisegment fusion and varied-weight (CNN-MFVW) were proposed to achieve ECG feature extraction and HbA1c detection. The results showed that the average accuracy, precision, recall, and F1-score of the proposed algorithm were 0.9015, 0.9051, 0.8991 and 0.9013 respectively. Moreover, the area under the curve (AUC) was up to 0.9899, which was higher than other algorithms of conventional CNN and CNN-LSTM. Therefore, we conclude that the proposed approach for noninvasive and fast detection of HbA1c levels has potentials in practical applications.

In this paper, a new approach based on human body communication (HBC) was presented for biometric verification. Specifically, the transmission gain S21 of human forearm is regarded as the biometric trait. For this purpose, three different forearm models were established and proposed to validate the aforementioned approach. Furthermore, the transmission gain S21 of 21 volunteers was obtained by the use of vector network analyzer (VNA) in the frequency range 0.3 MHz to 1500 MHz in the experiment. In addition, the convolutional neural network (CNN) which includes 3 convolution layers, 3 max pooling layers and 2 fully-connected layers was adopted in this paper. The influences of different optimizers and loss functions on CNN were investigated. The results showed that the recognition accuracy of CNN was 99.86% when the optimizer was set as Adadelta and the loss function was set as categorical crossentropy. We therefore suggest that the CNN has potentials to improve the recognition accuracy of biometric verification based on HBC.

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

Prediabetes is a metabolic condition before the occurrence of diabetes. The diagnosis of prediabetes can slow down or eliminate the growing cases of diabetes around the world. This paper presents a novel approach to identifying some vital physiological features for prediabetes prediction to stem the growing trend of type-2 diabetes. A standard OGTT experiment was conducted using BIOPAC 150MP, g-SAHARA and Mindray physiological device to capture continuous electrocardiogram (ECG) rhythm and electroencephalogram (EEG) of 40 human subjects while measuring blood glucose value at a regular interval. Features from the captured physiological signals were analyzed using an integrated space–time principal component analysis, independent component analysis, least absolute shrinkage and selector operator, and piecewise aggregate approximation techniques. The results from feature analysis show that certain features, namely HRV, QT, and ST from ECG; alpha, beta, and theta from the right parental hemisphere, along with alpha and delta from the left occipital hemisphere from EEG show significant correlation with change in the blood glucose. Furthermore, a weight-based multi-model was proposed by combining five (5) classification methods. The selected ECG and EEG features were applied for training the proposed multi-model classification, which is used to predict prediabetes. The evaluation of the multi-model performance produced accuracy, precision, and F1-measure of 92.0%, 88.8%, and 82.7% respectively, which is higher than the individual methods. The experimental results show that the coupling of multi-model electrophysiological data acquired with wearable multi-sensor devices can be utilized to diagnose diabetes early.

In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer’s forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data. Furthermore, a threshold-adaptive template matching (TATM) algorithm based on weighted Euclidean distance is proposed for rapid verification in this work. The results indicate that the chosen frequency for biometric verification is from 650 MHz to 750 MHz. The false acceptance rate (FAR) and false rejection rate (FRR) based on TATM are approximately 5.79% and 6.74%, respectively. In contrast, the FAR and FRR were 4.17% and 37.5%, 3.37% and 33.33%, and 3.80% and 34.17% using K-nearest neighbor (KNN) classification, support vector machines (SVM), and naive Bayesian method (NBM) classification, respectively. In addition, the running time of TATM is 0.019 s, whereas the running times of KNN, SVM and NBM are 0.310 s, 0.0385 s, and 0.168 s, respectively. Therefore, TATM is suggested to be appropriate for rapid verification use in wearable devices.

In the domain of reconnaissance, remote sensing, etc., people want to get high resolution images. There is a conflict between the resolution and the optical system defocus. In order to acquire high resolution images, some methods are taken to minish the defocus, but these ways affect the cost and weight of the system. In this paper, a novel technology was described for extending the depth of focus of the incoherent optical system; meanwhile this technology does not decrease the resolution of the images. In this technology, the aspheric optical element is used to encode the images as well as the digital signal processing is used to decode the images. It is significant for optical remote sensors to acquire high resolution images under complicated space environment.

This paper describes some of optimizitions in high speed photography, such as optimizing the aperture width for each image speed of a studed object in a rotating mirror framing camera, optimizing the rotating mirror speed for each slit width in a smear camera, optimizing designs of a rotating mirror camera and intermittentmechanism in a intermittent camera through the differentiation and calculation by means of computer, and selecting the optimiz-ed photographic frequence in the application of high speed camera. Finally, we deduce it is possible that smear cameras can be partially replaced with the framing camera through the data processing in some cases.

As for the temporal resolution of a scanning photography, its connotation and definition, its measuring method, and its assessing criterion could not be considered determinate and unitizing in high speed photography field. Those mentioned above, in this paper, have been discussed, and some suggestions to establish the assessing criterion of the temporal resolution have been presented.© (1989) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.