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

A low-noise transimpedance amplifier for BLM-based ion channel recording

High-throughput screening (HTS) using ion channel recording is a powerful drug discovery technique in pharmacology. Ion channel recording with planar bilayer lipid membranes (BLM) is scalable and has very high sensitivity. A HTS system based on BLM ion channel recording faces three main challenges: (i) design of scalable microfluidic devices; (ii) design of compact ultra-low-noise transimpedance amplifiers able to detect currents in the pA range with bandwidth >10 kHz; (iii) design of compact, robust and scalable systems that integrate these two elements. This paper presents a low-noise transimpedance amplifier with integrated A/D conversion realized in CMOS 0.35 µm technology. The CMOS amplifier acquires currents in the range ±200 pA and ±20 nA, with 100 kHz bandwidth while dissipating 41 mW. An integrated digital offset compensation loop balances any voltage offsets from Ag/AgCl electrodes. The measured open-input input-referred noise current is as low as 4 fA/Root Hz at ±200 pA range. The current amplifier is embedded in an integrated platform, together with a microfluidic device, for current recording from ion channels. Gramicidin-A, alpha-haemolysin and KcsA potassium channels have been used to prove both the platform and the current-to-digital converter

Wireless Power Transfer Techniques for Implantable Medical Devices:A Review

Wireless power transfer (WPT) systems have become increasingly suitable solutions for the electrical powering of advanced multifunctional micro-electronic devices such as those found in current biomedical implants. The design and implementation of high power transfer efficiency WPT systems are, however, challenging. The size of the WPT system, the separation distance between the outside environment and location of the implanted medical device inside the body, the operating frequency and tissue safety due to power dissipation are key parameters to consider in the design of WPT systems. This article provides a systematic review of the wide range of WPT systems that have been investigated over the last two decades to improve overall system performance. The various strategies implemented to transfer wireless power in implantable medical devices (IMDs) were reviewed, which includes capacitive coupling, inductive coupling, magnetic resonance coupling and, more recently, acoustic and optical powering methods. The strengths and limitations of all these techniques are benchmarked against each other and particular emphasis is placed on comparing the implanted receiver size, the WPT distance, power transfer efficiency and tissue safety presented by the resulting systems. Necessary improvements and trends of each WPT techniques are also indicated per specific IMD

Recent Progress of Development of Optogenetic Implantable Neural Probes

As a cell type-specific neuromodulation method, optogenetic technique holds remarkable potential for the realisation of advanced neuroprostheses. By genetically expressing light-sensitive proteins such as channelrhodopsin-2 (ChR2) in cell membranes, targeted neurons could be controlled by light. This new neuromodulation technique could then be applied into extensive brain networks and be utilised to provide effective therapies for neurological disorders. However, the development of novel optogenetic implants is still a key challenge in the field. The major requirements include small device dimensions, suitable spatial resolution, high safety, and strong controllability. In this paper, I present a concise review of the significant progress that has been made towards achieving a miniaturised, multifunctional, intelligent optogenetic implant. I identify the key limitations of current technologies and discuss the possible opportunities for future development

Low Power Circuits for Smart Flexible ECG Sensors

Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W

A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89

Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images

Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy

Deep learning approaches for human activity recognition using wearable technology

The need for long-term monitoring of individuals in their natural environment has initiated the development of a various number of wearable healthcare sensors for a wide range of applications: medical monitoring in clinical or home environments, physical activity assessment of athletes and recreators, baby monitoring in maternity hospitals and homes etc. Neural networks (NN) are data-driven type of modelling. Neural networks learn from experience, without knowledge about the model of phenomenon, but knowing the desired 'output' data for the training 'input' data. The most promising concept of machine learning that involves NN is the deep learning (DL) approach. The focus of this review is on approaches of DL for physiological activity recognition or human movement analysis purposes, using wearable technologies. This review shows that deep learning techniques are useful tools for health condition prediction or overall monitoring of data, streamed by wearable systems. Despite the considerable progress and wide field of applications, there are still some limitations and room for improvement of DL approaches for wearable healthcare systems, which may lead to more robust and reliable technology for personalized healthcare

AHEAD: Automatic Holistic Energy-Aware Design Methodology for MLP Neural Network Hardware Generation in Proactive BMI Edge Devices

The prediction of a high-level cognitive function based on a proactive brain–machine interface (BMI) control edge device is an emerging technology for improving the quality of life for disabled people. However, maintaining the stability of multiunit neural recordings is made difficult by the nonstationary nature of neurons and can affect the overall performance of proactive BMI control. Thus, it requires regular recalibration to retrain a neural network decoder for proactive control. However, retraining may lead to changes in the network parameters, such as the network topology. In terms of the hardware implementation of the neural decoder for real-time and low-power processing, it takes time to modify or redesign the hardware accelerator. Consequently, handling the engineering change of the low-power hardware design requires substantial human resources and time. To address this design challenge, this work proposes AHEAD: an automatic holistic energy-aware design methodology for multilayer perceptron (MLP) neural network hardware generation in proactive BMI edge devices. By taking a holistic analysis of the proactive BMI design flow, the approach makes judicious use of the intelligent bit-width identification (BWID) and configurable hardware generation, which autonomously integrate to generate the low-power hardware decoder. The proposed AHEAD methodology begins with the trained MLP parameters and golden datasets and produces an efficient hardware design in terms of performance, power, and area (PPA) with the least loss of accuracy. The results show that the proposed methodology is up to a 4X faster in performance, 3X lower in terms of power consumption, and achieves a 5X reduction in area resources, with exact accuracy, compared to floating-point and half-floating-point design on a field-programmable gate array (FPGA), which makes it a promising design methodology for proactive BMI edge devices

HEAR: Approach for Heartbeat Monitoring with Body Movement Compensation by IR-UWB Radar

Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland–Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring