Experimental Phantom-Based Security Analysis for Next-Generation Leadless Cardiac Pacemakers.

With technological advancement, implanted medical devices can treat a wide range of chronic diseases such as cardiac arrhythmia, deafness, diabetes, etc. Cardiac pacemakers are used to maintain normal heart rhythms. The next generation of these pacemakers is expected to be completely wireless, providing new security threats. Thus, it is critical to secure pacemaker transmissions between legitimate nodes from a third party or an eavesdropper. This work estimates the eavesdropping risk and explores the potential of securing transmissions between leadless capsules inside the heart and the subcutaneous implant under the skin against external eavesdroppers by using physical-layer security methods. In this work, we perform phantom experiments to replicate the dielectric properties of the human heart, blood, and fat for channel modeling between in-body-to-in-body devices and from in-body-to-off-body scenario. These scenarios reflect the channel between legitimate nodes and that between a legitimate node and an eavesdropper. In our case, a legitimate node is a leadless cardiac pacemaker implanted in the right ventricle of a human heart transmitting to a legitimate receiver, which is a subcutaneous implant beneath the collar bone under the skin. In addition, a third party outside the body is trying to eavesdrop the communication. The measurements are performed for ultrawide band (UWB) and industrial, scientific, and medical (ISM) frequency bands. By using these channel models, we analyzed the risk of using the concept of outage probability and determine the eavesdropping range in the case of using UWB and ISM frequency bands. Furthermore, the probability of positive secrecy capacity is also determined, along with outage probability of a secrecy rate, which are the fundamental parameters in depicting the physical-layer security methods. Here, we show that path loss follows a log-normal distribution. In addition, for the ISM frequency band, the probability of successful eavesdropping for a data rate of 600 kbps (Electromyogram (EMG)) is about 97.68% at an eavesdropper distance of 1.3 m and approaches 28.13% at an eavesdropper distance of 4.2 m, whereas for UWB frequency band the eavesdropping risk approaches 0.2847% at an eavesdropper distance of 0.22 m. Furthermore, the probability of positive secrecy capacity is about 44.88% at eavesdropper distance of 0.12 m and approaches approximately 97% at an eavesdropper distance of 0.4 m for ISM frequency band, whereas for UWB, the same statistics are 96.84% at 0.12 m and 100% at 0.4 m. Moreover, the outage probability of secrecy capacity is also determined by using a fixed secrecy rate.

Convergence and stability of quantized Hopfield networks operating in a fully parallel mode.

Hopfield neural networks (HNNs) have proven useful in solving optimization problems that require fast response times. However, the original analog model has an extremely high implementation complexity, making discrete implementations more suitable. Previous work has studied the convergence of discrete-time and quantized-neuron models but has limited the analysis to either two-state neurons or serial operation mode. Nevertheless, two-state neurons have poor performance, and serial operation modes lose fast convergence, which is characteristic of analog HNNs. This letter is the first in the field analyzing the convergence and stability of quantized Hopfield networks (QHNs)-with more than two states-operating in fully parallel mode. Moreover, this letter presents some further analysis on the energy minimization of this type of network. The main conclusion drawn is that QHNs operating in fully parallel mode always converge to a stable state or a cycle of length two and any stable state is a local minimum of the energy.

Wideband phantoms of different body tissues for heterogeneous models in body area networks.

One of the key issues about wireless technologies is their interaction with the human body. The so-called internet of things will comprise many devices that will transmit either around or through the human body. These devices must be tested either in their working medium, when possible, or in the most realistic one. For this purpose, tissue-like phantoms are the best alternative to carry out realistic analyses of the performance of body area networks. In addition, they are the conventional way to certify the compliance of commercial standards by these devices. However, the number of phantoms that work in large bandwidths is limited in literature. This work aims at presenting chemical solutions that will be useful to prepare a variety of wideband tissue phantoms. Besides, the colon was mimicked in two ways, the healthy tissue and the malignant one, taking into account studies that relate changes on the relative permittivity with cancer. They were designed on the basis of acetonitrile in aqueous solutions as described in a previous work. Thus, many scenarios could be developed such as multilayers which imitate parts of the heterogeneous body.

Experimental UWB frequency analysis for implant communications.

Implantable biomedical sensors with the ability to transmit wirelessly real-time physiological data to an external unit can enable better management of chronic diseases. The IEEE Standard 802.15.6-2012 specifies the implementation of implant communications within 402-405 MHz, which unfortunately allows low data transmission rates only. Ultra wideband (UWB) interfaces within 3.1-10.6 GHz offer a number of advantages at the expense of higher path losses. Efforts to characterize the implant UWB channel have been undertaken via computer simulations, but these may not capture completely the effects on the implant radio channel of multiple physiological functions. To overcome these limitations we provide insight into the frequency-domain behavior of the UWB implant channel within 3.1-8.5 GHz based on propagation measurements in a liquid phantom and a living swine. A thorough comparison of the relative received power in phantom-based and in vivo measurements for the in-body to on-body (IB2OB) and in-body to off-body (IB2OFF) channel scenarios are presented.

Experimental Path Loss Models for In-Body Communications Within 2.36-2.5 GHz.

Biomedical implantable sensors transmitting a variety of physiological signals have been proven very useful in the management of chronic diseases. Currently, the vast majority of these in-body wireless sensors communicate in frequencies below 1 GHz. Although the radio propagation losses through biological tissues may be lower in such frequencies, e.g., the medical implant communication services band of 402 to 405 MHz, the maximal channel bandwidths allowed therein constrain the implantable devices to low data rate transmissions. Novel and more sophisticated wireless in-body sensors and actuators may require higher data rate communication interfaces. Therefore, the radio spectrum above 1 GHz for the use of wearable medical sensing applications should be considered for in-body applications too. Wider channel bandwidths and smaller antenna sizes may be obtained in frequency bands above 1 GHz at the expense of larger propagation losses. Therefore, in this paper, we present a phantom-based radio propagation study for the frequency bands of 2360 to 2400 MHz, which has been set aside for wearable body area network nodes, and the industrial, scientific, medical band of 2400 to 2483.5 MHz. Three different channel scenarios were considered for the propagation measurements: in-body to in-body, in-body to on-body, and in-body to off-body. We provide for the first time path loss formulas for all these cases.

Bandwidth resource management for neural signal telemetry.

Recent advances in modern neurocomputing have shown the utmost necessity of wireless communication systems that allow real-time (RT) monitoring of neural signals meeting several requirements such as source compression and high fidelity of the received signal. Neural recordings require multielectrode probes with up to hundreds of electrodes and transmission of signals wirelessly over a limited bandwidth (BW). In this paper, a RT resource management algorithm is proposed so that adequate source compression is applied to each channel in order to fit them into the available BW. Performance of the algorithm is analyzed using dynamically changing BW and neural recordings with different neural activity characteristics.

Ultra wideband for wireless real-time monitoring of neural signals.

Performance of an ultra wideband (UWB) wireless system for real-time neural signal monitoring is evaluated by comparing spiking characteristics between transmitted and received signals for different experimental set-ups. Spike detection quality is selected as the main spiking characteristic of evaluated signals. Results are presented in receiver-operating characteristics and area-under-the-curve (AUC). In order to assess spike detection quality, a set of artificially generated neural signals is constructed from real neural recordings such that the ground truth is known. Data analysis shows how channel signal-to-noise-ratio (SNR) variation affects AUC in different signal SNR cases. Signals with low SNRs get less affected by reduced channel SNRs than those with higher SNR. Increasing bit error rate modifies spiking characteristics such that an under-estimation of the spiking frequency occurs due to spike losses. For practical application of real-time neural signal monitoring, UWB seems to offer best transmission conditions in a near-body environment.