Future of smart cardiovascular implants

Cardiovascular disease remains the leading cause of death in Western society. Recent technological advances have opened the opportunity of developing new and innovative smart stent devices that have advanced electrical properties that can improve diagnosis and even treatment of previously intractable conditions, such as central line access failure, atherosclerosis and reporting on vascular grafts for renal dialysis. Here we review the latest advances in the field of cardiovascular medical implants, providing a broad overview of the application of their use in the context of cardiovascular disease rather than an in-depth analysis of the current state of the art. We cover their powering, communication and the challenges faced in their fabrication. We focus specifically on those devices required to maintain vascular access such as ones used to treat arterial disease, a major source of heart attacks and strokes. We look forward to advances in these technologies in the future and their implementation to improve the human condition

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes

Sensing Movement: Microsensors for Body Motion Measurement

Recognition of body posture and motion is an important physiological function that can keep the body in balance. Man-made motion sensors have also been widely applied for a broad array of biomedical applications including diagnosis of balance disorders and evaluation of energy expenditure. This paper reviews the state-of-the-art sensing components utilized for body motion measurement. The anatomy and working principles of a natural body motion sensor, the human vestibular system, are first described. Various man-made inertial sensors are then elaborated based on their distinctive sensing mechanisms. In particular, both the conventional solid-state motion sensors and the emerging non solid-state motion sensors are depicted. With their lower cost and increased intelligence, man-made motion sensors are expected to play an increasingly important role in biomedical systems for basic research as well as clinical diagnostics

Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

Beyond Tissue replacement: The Emerging role of smart implants in healthcare

Smart implants are increasingly used to treat various diseases, track patient status, and restore tissue and organ function. These devices support internal organs, actively stimulate nerves, and monitor essential functions. With continuous monitoring or stimulation, patient observation quality and subsequent treatment can be improved. Additionally, using biodegradable and entirely excreted implant materials eliminates the need for surgical removal, providing a patient-friendly solution. In this review, we classify smart implants and discuss the latest prototypes, materials, and technologies employed in their creation. Our focus lies in exploring medical devices beyond replacing an organ or tissue and incorporating new functionality through sensors and electronic circuits. We also examine the advantages, opportunities, and challenges of creating implantable devices that preserve all critical functions. By presenting an in-depth overview of the current state-of-the-art smart implants, we shed light on persistent issues and limitations while discussing potential avenues for future advancements in materials used for these devices

Photovoltaic Energy Harvesting for Millimeter-Scale Systems

The Internet of Things (IoT) based on mm-scale sensors is a transformational technology that opens up new capabilities for biomedical devices, surveillance, micro-robots and industrial monitoring. Energy harvesting approaches to power IoT have traditionally included thermal, vibration and radio frequency. However, the achievement of efficient energy scavenging for IoT at the mm-scale or sub mm-scale has been elusive. In this work, I show that photovoltaic (PV) cells at the mm-scale can be an alternative means of wireless power transfer to mm-scale sensors for IoT, utilizing ambient indoor lighting or intentional irradiation of near-infrared (NIR) LED sources through biological tissue. Single silicon and GaAs photovoltaic cells at the mm-scale can achieve a power conversion efficiency of more than 17 % for silicon and 30 % for GaAs under low-flux NIR irradiation at 850 nm through the optimized device structure and sidewall/surface passivation studies, which guarantees perpetual operation of mm-scale sensors. Furthermore, monolithic single-junction GaAs photovoltaic modules offer a means for series-interconnected cells to provide sufficient voltage (> 5 V) for direct battery charging, and bypassing needs for voltage up-conversion circuitry. However, there is a continuing challenge to miniaturize such PV systems down to the sub mm-scale with minimal optical losses from device isolation and metal interconnects and efficient voltage up-conversion. Vertically stacked dual-junction PV cells and modules are demonstrated to increase the output voltage per cell and minimize area losses for direct powering of miniature devices for IoT and bio-implantable applications with low-irradiance narrowband spectral illumination. Dual-junction PV cells at small dimensions (150 µm x 150 µm) demonstrate power conversion efficiency greater than 22 % with more than 1.2 V output voltage under low-flux 850 nm NIR LED illumination, which is sufficient for batteryless operation of miniaturized CMOS IC chips. The output voltage of dual-junction PV modules with eight series-connected single cells is greater than 10 V while maintaining an efficiency of more than 18 %. Finally, I demonstrate monolithic PV/LED modules at the µm-scale for brain-machine interfaces, enabling two-way optical power and data transfer in a through-tissue configuration. The wafer-level assembly plan for the 3D vertical integration of three different systems including GaAs LED/PV modules, CMOS silicon chips, and neural probes is proposed.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163261/1/esmoon_1.pd

Optical power transfer and communication methods for wireless implantable sensing platforms

Ultrasmall scale implants have recently attracted focus as valuable tools for monitoring both acute and chronic diseases. Semiconductor optical technologies are the key to miniaturizing these devices to the long-sought sub-mm scale, which will enable long-term use of these devices for medical applications. This can also enable the use of multiple implantable devices concurrently to form a true body area network of sensors. We demonstrate optical power transfer techniques and methods to effectively harness this power for implantable devices. Furthermore, we also present methods for optical data transfer from such implants. Simultaneous use of these technologies can result in miniaturized sensing platforms that can allow for large-scale use of such systems in real world applications

Real-Time In Vivo Intraocular Pressure Monitoring using an Optomechanical Implant and an Artificial Neural Network

Optimized glaucoma therapy requires frequent monitoring and timely lowering of elevated intraocular pressure (IOP). A recently developed microscale IOP-monitoring implant, when illuminated with broadband light, reflects a pressure-dependent optical spectrum that is captured and converted to measure IOP. However, its accuracy is limited by background noise and the difficulty of modeling non-linear shifts of the spectra with respect to pressure changes. Using an end-to-end calibration system to train an artificial neural network (ANN) for signal demodulation we improved the speed and accuracy of pressure measurements obtained with an optically probed IOP-monitoring implant and make it suitable for real-time in vivo IOP monitoring. The ANN converts captured optical spectra into corresponding IOP levels. We achieved an IOP-measurement accuracy of ±0.1 mmHg at a measurement rate of 100 Hz, which represents a ten-fold improvement from previously reported values. This technique allowed real-time tracking of artificially induced sub-1 s transient IOP elevations and minor fluctuations induced by the respiratory motion of the rabbits during in vivo monitoring. All in vivo sensor readings paralleled those obtained concurrently using a commercial tonometer and showed consistency within ±2 mmHg. Real-time processing is highly useful for IOP monitoring in clinical settings and home environments and improves the overall practicality of the optical IOP-monitoring approach

Real-Time In Vivo Intraocular Pressure Monitoring using an Optomechanical Implant and an Artificial Neural Network

Optimized glaucoma therapy requires frequent monitoring and timely lowering of elevated intraocular pressure (IOP). A recently developed microscale IOP-monitoring implant, when illuminated with broadband light, reflects a pressure-dependent optical spectrum that is captured and converted to measure IOP. However, its accuracy is limited by background noise and the difficulty of modeling non-linear shifts of the spectra with respect to pressure changes. Using an end-to-end calibration system to train an artificial neural network (ANN) for signal demodulation we improved the speed and accuracy of pressure measurements obtained with an optically probed IOP-monitoring implant and make it suitable for real-time in vivo IOP monitoring. The ANN converts captured optical spectra into corresponding IOP levels. We achieved an IOP-measurement accuracy of ±0.1 mmHg at a measurement rate of 100 Hz, which represents a ten-fold improvement from previously reported values. This technique allowed real-time tracking of artificially induced sub-1 s transient IOP elevations and minor fluctuations induced by the respiratory motion of the rabbits during in vivo monitoring. All in vivo sensor readings paralleled those obtained concurrently using a commercial tonometer and showed consistency within ±2 mmHg. Real-time processing is highly useful for IOP monitoring in clinical settings and home environments and improves the overall practicality of the optical IOP-monitoring approach