Abstract:
Recent developments in brain-machine interface technology have rendered seizure prediction possible. However, the transmission of a large volume of electrophysiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computational resources, especially for power-critical wearable and implantable medical devices. Although many data compression methods can be adopted to compress the signals to reduce communication bandwidth requirement, they require complex compression and reconstruction procedures before the signal can be used for seizure prediction. In this paper, we propose C2 SP-Net, a framework to jointly solve compression, prediction, and reconstruction without extra computation overhead. The framework consists of a plug-and-play in-sensor compression matrix to reduce transmission bandwidth requirements. The compressed signal can be utilized for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Compression and classification overhead from the energy consumption perspective, prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated using various compression ratios. The experimental results illustrate that our proposed framework is energy efficient and outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.6% in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.
Abstract:
We propose a label-free biosensor based on a porous silicon resonant microcavity and localized surface plasmon resonance. The biosensor detects SARS-CoV-2 antigen based on engineered trimeric angiotensin converting enzyme-2 binding protein, which is conserved across different variants. Robotic arms run the detection process including sample loading, incubation, sensor surface rinsing, and optical measurements using a portable spectrometer. Both the biosensor and the optical measurement system are readily scalable to accommodate testing a wide range of sample numbers. The limit of detection is 100 TCID50/ml. The detection time is 5 min, and the throughput of one single robotic site is up to 384 specimens in 30 min. The measurement interface requires little training, has standard operation, and therefore is suitable for widespread use in rapid and onsite COVID-19 screening or surveillance.
Abstract:
Recent advances in wearable bioelectronics have driven various healthcare applications, such as the monitoring, sensing, and treating of various diseases. However, unsustainable batteries and toxic power solutions hinder their use at the skin interface or in vivo. As a promising power solution, supercapacitors have attracted the attention of researchers. However, there are still several drawbacks, such as the transparency, stretchability, biocompatibility, and flexibility of these materials, when these energy reservoirs are used as power supplies for skin-interfaced electronics. In this work, a novel microfabrication approach for fabricating supercapacitors using anodic aluminum oxide templates is presented. In this work, a large capacitance value of 15.02 mF cm−2, as well as good transparency, stretchability, and biocompatibility are obtained. Thus, it is verified that the proposed supercapacitors are suitable as skin-interfaced power solutions.
Abstract:
In this study, we report two fiber optic-biolayer interferometry (FO-BLI)-based biosensors for the rapid detection of SARS-CoV-2 neutralizing antibodies (NAbs) and binding antibodies (BAbs) in human serum. The use of signal enhancer 3,3′-diaminobenzidine enabled the detection of NAbs, anti-receptor binding domain (anti-RBD) BAbs, and anti-extracellular domain of spike protein (anti-S-ECD) BAbs up to as low as 10 ng/mL in both buffer and 100-fold diluted serum. NAbs and BAbs could be detected individually over 7.5 and 13 min, respectively, or simultaneously by prolonging the detection time of the former. The protocol for the detection of BAbs could be utilized for detection of the RBD-N501Y variant with equal sensitivity and speed. Results of the NAbs and the anti-RBD BAbs biosensors correlated well with those of the corresponding commercial assay kit. Clinical utility of the two FO-BLI biosensors were further validated using a small cohort of samples randomly taken from 16 enrolled healthy participants who received inactivated vaccines. Two potent serum antibodies were identified, which showed high neutralizing capacities toward RBD and pseudovirus. Overall, the rapid automated biosensors can be used for an individual sample measurement of NAbs and BAbs as well as for high-throughput analysis. The findings of this study would be useful in COVID-19 related studies in vaccine trials, research on dynamics of the immune response, and epidemiology studies.
Abstract:
This handbook of biochips is composed of five main sections, all intended to describe the most currently conducted research activities in biochips. Consequently, the various included 61 chapters contains in section 1, wearable and implantable biosensing technologies, which include 16 chapters to describe the latest results in biosensors such as blood pressure, biopotentials, neurorecording, optical neural interfaces, and biosensors of different vital signs. Section 2, formed of 9 chapters and titled multi-chip smart neuroprostheses, concerns artificial olfactory systems, closed-loop devices, brain-computer interfaces, and visual stimulation systems. Section 3, grouping 11 chapters about lab-on-chip devices for diagnosis, monitoring and drug delivery. Some chapters in this section cover DNA detection, capacitive cells sensing, glucose monitoring, nuclear magnetic resonance, optical biosensing, porous silicon-based sensing, and other brain-on-a-chip devices. In section 4 titled telemetry and wireless link related biochips, there are 18 chapters sharing the last research intended to improve the capacitive, inductive, and optical links to wirelessly transmit data and power to various biochip-based applications such as Doppler radar sensor platform, intracortical brain-machine Interfaces, wireless capsule navigation within the body, healthy radios, and security and innovation protection of biochips. In section 5 we focus on main microstimulators, 8 chapters include retinal and subretinal visual prostheses, a foot-drop stimulator, endocardial stimulation system, etc.
Abstract:
Stroke is the leading cause of death and disability after ischemic heart disease. However, there is lacking a noninvasive long-time monitoring technique for stroke diagnosis and therapy.The photoacoustic imaging approach reconstructs images of an object based on the energy excitation by optical absorption and its conversion to acoustic waves, due to corresponding thermoelastic expansion, which has optical resolution and acoustic propagation. This emerging functional imaging method is a non-invasive technique. Due to its precision, this method is particularly attractive for stroke monitoring purpose. In this paper, we review the achievements of this technology and its applications on stroke, as well as the development status in both animal and human applications. Also, various photoacoustic systems and multi-modality photoacoustic imaging are introduced as for potential clinical applications. Finally, the challenges of photoacoustic imaging for monitoring stroke are discussed.
Abstract:
Continuous drug monitoring is a promising alternative to current therapeutic drug monitoring strategies and has a strong potential to reshape our understanding of pharmacokinetic variability and to improve individualised therapy. This review highlights recent advances in biosensing technologies that support continuous drug monitoring in real time. We focus primarily on aptamer-based biosensors, wearable and implantable devices. Emphasis is given to the approaches employed in constructing biosensors. We pay attention to sensors’ biocompatibility, calibration performance, long-term characteristics stability and measurement quality. Last, we discuss the current challenges and issues to be addressed in continuous drug monitoring to make it a promising, future tool for individualised therapy. The ongoing efforts are expected to result in fully integrated implantable drug biosensing technology. Thus, we may anticipate an era of advanced healthcare in which wearable and implantable biochips will automatically adjust drug dosing in response to patient health conditions, thus enabling the management of diseases and enhancing individualised therapy.
Abstract:
Seizure prediction for drug-refractory epilepsy patients can improve their quality of life, reduce their anxiety, and help them take the necessary precautions. Nowadays, numerous deep learning algorithms have been proposed to predict seizure onset and obtain better performance than traditional machine learning methods. However, these methods require a large set of parameters and large hardware resources; they also have high energy consumption. Therefore, these methods cannot be implemented on compact, low-power wearable, or implantable medical devices. The devices should operate on a real-time basis to continually inform the epileptic patients. In this paper, we describe energy-efficient and hardware-friendly methods to predict the epileptic seizures. A model of only 45 kB was obtained by the neural architecture search and was evaluated across three datasets. The overall sensitivity, false prediction rate, and area under receiver operating characteristic curve were 99.81%, 0.005/h, 1 and 93.48%, 0.063/h, 0.977 and 85.19%, 0.116/h, 0.933, respectively, for three public datasets. This model was further reduced with several methods for model compression. The performances for the model sizes less than 50 kB for scalp EEG data and less than 10 kB for intracranial EEG data outperformed all the other models of similar model sizes. In particular, the energy consumption estimation was less than 10 mJ per inference for scalp EEG signal and less than 0.5 mJ per inference for intracranial EEG signal, which meet the requirements for low-power wearable and implantable devices, respectively.
Abstract:
Obstructive sleep apnea (OSA), a common sleep disorder disease, affects millions of people. Without appropriate treatment, this disease can provoke several health-related risks including stroke and sudden death. A variety of treatments have been introduced to relieve OSA. The main present clinical treatments and undertaken research activities to improve the success rate of OSA were covered in this paper. Additionally, guidelines on choosing a suitable treatment based on scientific evidence and objective comparison were provided. This review paper specifically elaborated the clinically offered managements as well as the research activities to better treat OSA. We analyzed the methodology of each diagnostic and treatment method, the success rate, and the economic burden on the world. This review paper provided an evidence-based comparison of each treatment to guide patients and physicians, but there are some limitations that would affect the comparison result. Future research should consider the consistent follow-up period and a sufficient number of samples. With the development of implantable medical devices, hypoglossal nerve stimulation systems will be designed to be smart and miniature and one of the potential upcoming research topics. The transcutaneous electrical stimulation as a non-invasive potential treatment would be further investigated in a clinical setting. Meanwhile, no treatment can cure OSA due to the complicated etiology. To maximize the treatment success of OSA, a multidisciplinary and integrated management would be considered in the future.
Abstract:
Recent review papers have investigated seizure prediction, creating the possibility of preempting epileptic seizures. Correct seizure prediction can significantly improve the standard of living for the majority of epileptic patients, as the unpredictability of seizures is a major concern for them. Today, the development of algorithms, particularly in the field of machine learning, enables reliable and accurate seizure prediction using desktop computers. However, despite extensive research effort being devoted to developing seizure detection integrated circuits (ICs), dedicated seizure prediction ICs have not been developed yet. We believe that interdisciplinary study of system architecture, analog and digital ICs, and machine learning algorithms can promote the translation of scientific theory to a more realistic intelligent, integrated, and low-power system that can truly improve the standard of living for epileptic patients. This review explores topics ranging from signal acquisition analog circuits to classification algorithms and dedicated digital signal processing circuits for detection and prediction purposes, to provide a comprehensive and useful guideline for the construction, implementation and optimization of wearable and integrated smart seizure prediction systems.
Abstract:
Functional synapses in the central nervous system depend on a chemical signal exchange process that involves neurotransmitter delivery between neurons and receptor cells in the neuro system. Abnormal neurotransmitter levels and distributions can cause intractable diseases involving descending/ascending modulatory pathways or dysfunctional organs. The detection of abnormal neurotransmitter levels is one of the most promising techniques in the diagnosis of brain diseases. Also, numerous effective methods for the detection of neurotransmitters in vivo have been fabricated. Nowadays, electrochemical, optical, magnetic, and microdialysis methods are among the main techniques used to detect neurotransmitters. Herein, we review current techniques for detecting eight types of neurotransmitters with a focus on in vivo neurotransmitter tracking methods intended for the real-time diagnosis of brain disorders.
Abstract:
Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.