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.

Evaluation of Secrecy Capacity for Next-Generation Leadless Cardiac Pacemakers.

Secure communication can be considered as an integral part of the next generation implantable medical devices. With the advent of Physical Layer Security (PLS) methods, confidential messages can be transmitted without the use of encryption keys. For analyzing the effectiveness of PLS for next-generation leadless cardiac pacemakers, we provide secrecy analysis using a performance metric of secrecy capacity. Secrecy capacity defines the secure transmission rate between legitimate nodes without leakage of information to an eavesdropper and depends on respective channel attenuations. The legitimate and eavesdropper channel attenuations are evaluated by 3D numerical electromagnetic simulations using a detailed human model. We do not assume eavesdropper to be located in specific directions or positions and considers it to be located anywhere around the body. We evaluate the secrecy capacity by defining a spherical grid for eavesdropper positions around the body with a radius of 1 m. The secrecy capacity of the entire space is evaluated by extrapolating the grid to different radial distances using free space path loss model. Moreover, by fixing application based secure communication rate, the entire space is divided into secure and in-secure volumes. The in-secure volume consists of all the eavesdropper positions from which the pacemaker can be eavesdropped. We also evaluated the angle from which the maximum leakage of information takes place and referred it as "Eve's sweet spot angle." Data for channel attenuations from phantom and in-vivo experiments is also utilized to validate and observe the differences between simulations and experiments. This article will help in design of the communication module of implanted leadless cardiac pacemakers with enhanced security on the physical layer.

Extracting remote photoplethysmogram signal from endoscopy videos for vessel and capillary density recognition.

In this paper, we propose a new feature for finding lesions in gastrointestinal tissues. Polyps or cancerous parts have different capillary pattern compared with normal parts. There are polyps which have higher density of vessel or capillary pattern. This feature leads us to extract remote photoplethysmogram signal from different parts of videos from gastrointestinal tissue. Due to the fact that hemoglobin absorbs more light than surrounding tissues, more changes are expected to be observed in the parts with higher density of vessels and capillaries. In the experimental results, rPPG signals is extracted from colonoscopy and endoscopy videos. This feature is used to distinguish between normal and abnormal tissues. It is shown that power of rPPG signal can be used to find lesion areas.

Accurate heart rate estimation from camera recording via MUSIC algorithm.

In this paper, we propose an algorithm to extract heart rate frequency from video camera using the Multiple SIgnal Classification (MUSIC) algorithm. This leads to improved accuracy of the estimated heart rate frequency in cases the performance is limited by the number of samples and frame rate. Monitoring vital signs remotely can be exploited for both non-contact physiological and psychological diagnosis. The color variation recorded by ordinary cameras is used for heart rate monitoring. The orthogonality between signal space and noise space is used to find more accurate heart rate frequency in comparison with traditional methods. It is shown via experimental results that the limitation of previous methods can be overcome by using subspace methods.