A Novel Flexible Antenna at Very High Frequency Band for On-Body Applications

This paper proposes a novel flexible antenna design operating at very high frequency (VHF) band for on-body applications such as human body communication (HBC). The antenna consists of back-to-back E-shaped fractal and complimentary structures designed over a thin flexible substrate. The overall design working on the principle of fractal geometries and capacitive coupling is highly beneficial to achieve better antenna characteristics even at low frequencies around 35 MHz–45 MHz that are being used for HBC application. The proposed antenna obtained a large bandwidth of around 10.0 MHz in air and a bandwidth of around 8.0 MHz during on-body operation. The antenna has been tested in three different scenarios viz. air, on-body single antenna and on-body communication using two antennas. The insertion loss is reduced to a minimum in all three scenarios, which is quite beneficial for better signal transmission. The size miniaturization with high flexibility in such low frequencies has also been achieved in the paper that makes the proposed design suitable for human body communication applications.     

An absolute magnitude deviation of HRV for the prediction of prediabetes with combined artificial neural network and regression tree methods

Early diagnosis of prediabetes is an effective solution to the rising cases of diabetes around the world. The heterogeneous physiological characteristics of the ECG signal recorded from the heart make it challenging to implement an efficient diagnostic system. Therefore, this paper proposes a new approach to handling the heterogeneous characteristics of heart rate variability (HRV) with an absolute magnitude deviation analysis and an integrated machine learning technique for prediabetes prediction. We conducted an oral glucose tolerance test to acquire a resting-state ECG signal and the corresponding blood glucose value. We analyzed the HRV pattern from the ECG signal with a block-sliding window technique. We proposed a hybrid model to classify normal and prediabetes based on the extent of the absolute deviation of HRV values and avoiding a single point of failure. We adopted the model from the classification and regression tree (CART) and neural network (NN) algorithms. The experimental results reveal that when the blood glucose level increases, the maximum and range values of CARTHRV decreases while the minimum value increases. The proposed hybrid model had a better performance than the two methods with 100% sensitivity, specificity, and F1-score measures against CART and NN that recorded?<?100% for the same number of prediabetes in the training and test sets. The outcome from the analysis shows that the changes in blood glucose can be observed in ECG signals. The fast approximation of the proposed method to 100% accuracy suggests that it is possible to achieve the diagnosis of prediabetes and overcome the discrepancies in physiological signals among individuals.