Optimizing Cardiac Wireless Implant Communication: A Feasibility Study on Selecting the Frequency and Matching Medium

Cardiac wireless implantable medical devices (CWIMD) have brought a paradigm shift in monitoring and treating various cardiac conditions, including heart failure, arrhythmias, and hypertension. One of the key elements in CWIMD is the implant antenna which uses radio frequency (RF) technology to wirelessly communicate and transmit data to external devices. However, wireless communication with a deeply implanted antenna using RF can be challenging due to the significant loss of electromagnetic (EM) signal at the air-skin interface, and second, due to the propagation and reflection of EM waves from different tissue boundaries. The air-skin interface loss of the EM wave is pronounced due to the absence of a matching medium. This paper investigates the EM propagation losses in the human body and presents a choice of optimal frequency for the design of the cardiac implant antenna and the dielectric properties of the matching medium. First, the dielectric properties of all tissues present in the human thorax including skin, fat, muscle, cartilage, and heart are analyzed as a function of frequency to study the EM wave absorption at different frequencies. Second, the penetration of EM waves inside the biological tissues is analyzed as a function of frequency. Third, a transmission line (TL) formalism approach is adopted to examine the optimal frequency band for designing a cardiac implant antenna and the matching medium for the air-skin interface. Finally, experimental validation is performed at two ISM frequencies, 433 MHz and 915 MHz, selected from the optimal frequency band (0.4-1.5 GHz) suggested by our analytical investigation. For experimental validation, two off-the-shelf flexible dipole antennas operating at selected ISM frequencies were used. The numerical and experimental findings suggested that for the specific application of a cardiac implant with a penetration depth of 7-17 cm, the most effective frequency range for operation is within 0.4-1.5 GHz. The findings based on the dielectric properties of thorax tissues, the penetration depth of EM waves, and the optimal frequency band have provided valuable information on developing and optimizing CWIMDs for cardiac care applications.

Multichannel Wireless Neurosensing System for battery-free monitoring of neuronal activity

Electrical activity recordings are critical for evaluating and understanding brain function. We present a novel wireless, implantable, and battery-free device, namely the Wireless Neurosensing System (WiNS), and for the first time, we evaluate multichannel recording capabilities in vivo. For a preliminary evaluation, we performed a benchtop experiment with emulated sinusoidal signals of varying amplitude and frequency, representative of neuronal activity. We later performed and analyzed electrocortical recordings in rats of evoked somatosensory activity in response to three paradigms: hind/fore limb and whisker stimulation. Wired recordings were used for comparison and validation of WiNS. We found that through the channel multiplexing element of WiNS, it is possible to perform multichannel recordings with a maximum sampling rate of ∼10 kHz for a total of eight channels. This sampling rate is appropriate for monitoring the full range of neuronal signals of interest, from low-frequency population recordings of electrocorticography and local field potentials to high-frequency individual neuronal spike recordings. These in vivo experiments demonstrated that the evoked neuronal activity recorded with WiNS is comparable to that recorded with a wired system under identical circumstances. Analysis of critical parameters for interpreting the somatosensory evoked activity showed no statistically significant difference between the parameters obtained by a wired system versus those obtained using WiNS. Therefore, WiNS can match the performance of more invasive recording systems. WiNS is a groundbreaking technology with potential applications throughout neuroscience as it offers a simple alternative to address the pitfalls of battery-powered neuronal implants.

Fully Passive Flexible Wireless Neural Recorder for the Acquisition of Neuropotentials from a Rat Model

Wireless implantable neural interfaces can record high-resolution neuropotentials without constraining patient movement. Existing wireless systems often require intracranial wires to connect implanted electrodes to an external head stage or/and deploy an application-specific integrated circuit (ASIC), which is battery-powered or externally power-transferred, raising safety concerns such as infection, electronics failure, or heat-induced tissue damage. This work presents a biocompatible, flexible, implantable neural recorder capable of wireless acquisition of neuropotentials without wires, batteries, energy harvesting units, or active electronics. The recorder, fabricated on a thin polyimide substrate, features a small footprint of 9 mm × 8 mm × 0.3 mm and is composed of passive electronic components. The absence of active electronics on the device leads to near zero power consumption, inherently avoiding the catastrophic failure of active electronics. We performed both in vitro validation in a tissue-simulating phantom and in vivo validation in an epileptic rat. The fully passive wireless recorder was implanted under rat scalp to measure neuropotentials from its contact electrodes. The implanted wireless recorder demonstrated its capability to capture low voltage neuropotentials, including somatosensory evoked potentials (SSEPs), and interictal epileptiform discharges (IEDs). Wirelessly recorded SSEP and IED signals were directly compared to those from wired electrodes to demonstrate the efficacy of the wireless data. In addition, a convoluted neural network-based machine learning algorithm successfully achieved IED signal recognition accuracy as high as 100 and 91% in wired and wireless IED data, respectively. These results strongly support the fully passive wireless neural recorder's capability to measure neuropotentials as low as tens of microvolts. With further improvement, the recorder system presented in this work may find wide applications in future brain machine interface systems.