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.

This paper proposes a method of using a highly efficient spoof surface plasmon polariton (SSPP) endfire sensor for monitoring of aqueous glucose solutions and on-body glucose measurements. Occupying an area of around (52mm x 24mm), the proposed sensor radiates an endfire beam into a glucose water solution with a substantially reduced effective aperture. At the CPW port of the sensor, a pair of triangular ground planes was added to suppress the sidelobes, which have been proven to be counter-productive in glucose sensing. The slow wave nature of the proposed SSPP endfire sensor has further advantageously enabled the glucose concentrations to be measured with an increased sensitivity. The guiding body of the sensor was designed to simulate an electromagnetic beam propagating in a dense dielectric medium with a group velocity comparable to the phase velocity of light propagating in a glucose solution. Consistent with the results of our analysis, the experimental results of this work revealed that the gradual transition of slow electromagnetic wave from the SSPP endfire sensor to the glucose water solution has successfully enabled the glucose concentrations of a solution to be measured and displayed in the form of highly observable resonant frequency shifts in S11 parameters. The proposed design has achieved a high sensitivity of 150 MHz/mgmL-1 during onbody measurements.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human’s electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A $+$ B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Background: Globally, the cases of diabetes mellitus (diabetes) have increased in the past three decades, and it is recorded as one of the leading cause of death. This epidemic is a metabolic condition where the body cannot regulate blood glucose, thereby leading to abnormally high blood sugar. Genetic condition plays a significant role to determine a person susceptibility to the condition, a sedentary lifestyle and an unhealthy diet are behaviour that supports the current global epidemic. The complication that arises from diabetes includes loss of vision, peripheral neuropathy, cardiovascular complications and so on. Victims of this condition require constant monitoring of blood glucose which is done by the pricking of the finger. This procedure is painful, inconvenient and can lead to disease infection. Therefore, it is important to find a way to measure blood glucose non-invasively to minimize or eliminate the disadvantages encountered with the usual monitoring of blood glucose. Method: In this paper, we performed two experiments on 16 participants while electrocardiogram (ECG) data was continuously captured. In the first experiment, participants are required to consume 75 g of anhydrous glucose solution (oral glucose tolerance test) and the second experiment, no glucose solution was taken. We explored statistical and spectral analysis on HRV, HR, R-H, P-H, PRQ, QRS, QT, QTC and ST segments derived from ECG signal to investigate which segments should be considered for the possibility of achieving non-invasive blood glucose monitoring. In the statistical analysis, we examined the pattern of the data with the boxplot technique to reveal the change in the statistical properties of the data. Power spectral density estimation was adopted for the spectral analysis to show the frequency distribution of the data. Results: HRV segment obtained a statistical score of 81% for decreasing pattern and HR segment have the same statistical score for increasing pattern among the participants in the first quartile, median and mean properties. While ST segment has a statistical score of 81% for decreasing pattern in the third quartile, QT segment has 81% for increasing pattern for the median. From a total change score of 6, ST, QT, PRQ, P-H, HR and HRV obtained 4, 5, 4, 5 and 6 respectively. For spectral analysis, HRV and HR segment scored 81 and 75% respectively. ST, QT, PRQ have 75, 62 and 68% respectively. Conclusions: The results obtained demonstrate that HR, HRV, PRQ, QT and ST segments under a normal, healthy condition are affected by glucose and should be considered for modelling a system to achieve the possibility of non-invasive blood glucose measurement with ECG.

In this paper, an inhomogeneous human body model was presented to investigate the propagation characteristics when the human body was used as an antenna to achieve signal transmission. Specifically, the channel gain of four scenarios, namely, (1) both TX electrode and RX electrode were placed in the air, (2) TX electrode was attached on the human body, and RX electrode was placed in the air, (3) TX electrode was placed in the air, and RX electrode was attached on the human body, (4) both the TX electrode and RX electrode were attached on the human body, were studied through numerical simulation in the frequency range 1 MHz to 90 MHz. Furthermore, the comparisons of input efficiency, accepted efficiency, total efficiency, absorption power of human body, and electric field distribution of different distances of four aforementioned scenarios were explored when the frequency was at 44 MHz. In addition, the influences of different human tissues, electrode position, and the distance between electrode and human body on the propagation characteristics were investigated respectively at 44 MHz. The results showed that the channel gain of Scenario 4 was the maximum when the frequency was from 1 MHz to 90 MHz. The propagation characteristics were almost independent of electrode position when the human body was using as an antenna. However, as the distance between TX electrode and human body increased, the channel gain decreased rapidly. The simulations were verified by experimental measurements. The results showed that the simulations were in agreement with the measurements.

This paper proposes Young’s double slit method based high gain broadband higher order mode spoof surface plasmon leaky wave antenna sensor (SSPLWA) for dual beam and sensing applications. Following and mimicking the double slit principle in microwave frequencies, the proposed surface plasmon leaky wave antenna sensor operating on higher order modes has been successfully measured to obtain a wideband of more than 15GHz (88.5%) and generate two simultaneous radiating beams towards the endfire direction with a peak gain of 16.6 dBi. The proposed antenna sensor operates in a broad frequency range from 10GHz-25GHz and maintains a minimum gain of > 10 dBi in the whole operating band. The SSPLWA has been tested by designing two prototypes without and with additional horizontal conducting strips so as to improve the overall gain, reflection and efficiency. The prototype with horizontal strips has been further tested for sensing application i.e. aqueous glucose solutions and it is found that the proposed dual beam sensor is providing high sensitivity and very stable measurement results in terms of resonance frequency shifts or magnitude variations. The proposed antenna sensor has achieved a high peak efficiency of 95 percent and can be very useful for dual beam applications and wireless sensing measurements.