Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

Power Approaches for Implantable Medical Devices.

Implantable medical devices have been implemented to provide treatment and to assess in vivo physiological information in humans as well as animal models for medical diagnosis and prognosis, therapeutic applications and biological science studies. The advances of micro/nanotechnology dovetailed with novel biomaterials have further enhanced biocompatibility, sensitivity, longevity and reliability in newly-emerged low-cost and compact devices. Close-loop systems with both sensing and treatment functions have also been developed to provide point-of-care and personalized medicine. Nevertheless, one of the remaining challenges is whether power can be supplied sufficiently and continuously for the operation of the entire system. This issue is becoming more and more critical to the increasing need of power for wireless communication in implanted devices towards the future healthcare infrastructure, namely mobile health (m-Health). In this review paper, methodologies to transfer and harvest energy in implantable medical devices are introduced and discussed to highlight the uses and significances of various potential power sources

Design and Implementation of Wireless Point-Of-Care Health Monitoring Systems: Diagnosis For Sleep Disorders and Cardiovascular Diseases

Chronic sleep disorders are present in 40 million people in the United States. More than 25 million people remain undiagnosed and untreated, which accounts for over $22 billion in unnecessary healthcare costs. In addition, another major chronic disease is the heart diseases which cause 23.8% of the deaths in the United States. Thus, there is a need for a low cost, reliable, and ubiquitous patient monitoring system. A remote point-of-care system can satisfy this need by providing real time monitoring of the patient\u27s health condition at remote places. However, the currently available POC systems have some drawbacks; the fixed number of physiological channels and lack of real time monitoring. In this dissertation, several remote POC systems are reported to diagnose sleep disorders and cardiovascular diseases to overcome the drawbacks of the current systems. First, two types of remote POC systems were developed for sleep disorders. One was designed with ZigBee and Wi-Fi network, which provides increase/decrease the number of physiological channels flexibly by using ZigBee star network. It also supports the remote real-time monitoring by extending WPAN to WLAN with combination of two wireless communication topologies, ZigBee and Wi-Fi. The other system was designed with GSM/WCDMA network, which removes the restriction of testing places and provides remote real-time monitoring in the true sense of the word. Second, a fully wearable textile integrated real-time ECG acquisition system for football players was developed to prevent sudden cardiac death. To reduce power consumption, adaptive RF output power control was implemented based on RSSI and the power consumption was reduced up to 20%. Third, as an application of measuring physiological signals, a wireless brain machine interface by using the extracted features of EOG and EEG was implemented to control the movement of a robot. The acceleration/deceleration of the robot is controlled based on the attention level from EEG. The left/right motion of eyeballs of EOG is used to control the direction of the robot. The accuracy rate was about 95%. These kinds of health monitoring systems can reduce the exponentially increasing healthcare costs and cater the most important healthcare needs of the society

Study of a Self-Powered Lactate and Glucose Biosensor Platform

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 202x-202x. Tutor/Director: xxxxLactate and Glucose detection and monitoring have shown to have a significant impact on patients’ wellbeing, for allowing the prognosis of worsening patient conditions in hospital settings and assisting on early diagnosis and detection of diabetes mellitus complications. Biological Fuel Cell (BFC) technology allows the transformation from chemical to electrical energy and has recently emerged as a key lithium-ion battery competitor for its sustainability, miniaturization power, and high energy density. Their characteristics make them an interesting alternative to power electronic devices, and their possible application in the development of medical measurement platforms. Point of Care (POC) Biosensor devices powered with BFC present a compelling perspective to medical monitoring and individualized proactive healthcare since these types of devices allow near-patient settings and encourage a more personalized medicine approach to improve quality of life in developed countries. The two main objectives of this project are to develop a biosensing platform architecture for Glucose and Lactate Fuel Cells and the use of a commercially available DC-DC converter to apply said BFC to power the instrumentation and obtain a self-powered application. The proposed dual-layered platform attempts to provide a viable biosensor structure, based on the sensing of current proportional to sample concentration, a later amplification, and an event detection circuit to be used as a comparator. This study and proposal, if developed into reality, would comply with the “ASSURED” criteria for POC tests, which states they must be Affordable, Sensitive, Specific, User-friendly, Robust and rapid, Equipment-free, and Deliverable to those who need them

Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications

Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making. In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passive—powered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%. One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data