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Temperature is one of the most frequently measured physical quantities in clinical environment and a good biomarker of illness. The need for reusable probes, which have to be sterilized to prevent infections, requires the metrological qualification of thermometer probes in response to ageing effects induced by several sterilization processes. In this study, we investigated the effect of repeated sterilizations on both a commonly-used autoclavable thermistor probe and a prototypal Fiber Bragg Gratings (FBG) probe for temperature measurements. Findings highlighted a greater reliability of the proposed FBG probe than the commercial thermistor. Specifically, the FBG probe was able to withstand repeated sterilization cycles while maintaining stability in the measurement accuracy. Moreover, the proposed configuration for the FBG probe was immune to unwanted mechanical strain by providing a dynamic response fit for the measurement of temperature.

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules, namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring

3D-stacked CMOS SPAD based image sensors hold the promise for better sensitivity and more functionality per pixel. The technology enables to separate detection from computation onto different chips, or tiers, that are stacked onto one another. One advantage is to be able to independently optimize detection and processing in dedicated processes. Another is to achieve extremely low skews across large chips, thus enabling accurate timing over multi-megapixel image sensors. A further advantage is the potential of implementing advanced functionality requiring large arrays of computational units directly connected with the detectors, thus paving the way to on-chip convolutional neural networks and deep learning engines. In this paper we review several technologies enabling this interesting evolution and examples of possible implementations in the context of actual applications.

Smart hydrogels are inherently biocompatible hydrophilic three-dimensional polymer networks able to undergo a volume-phase transition in the presence of an analyte. By molecular imprinting and/or aptamer-based approaches they can be tailored for a wide range of analytes with high selectivity. In combination with the biocompatibility, this makes hydrogels very promising candidates for biomedical sensor applications. However, to date hydrogels are rarely used for that purpose as the reliable detection of their swelling state remains a challenge. Here we report on a newly developed biocompatible bending sensor platform which can be equipped with almost any smart hydrogel, thereby paving the way for biomedical applications.

In this work, a novel multispectral sensing system consisting of nanostructured filter matrix and a charge-coupled device (CCD)-based image sensor has been developed to overcome the limitation of the conventional pigment filtered sensors, which are difficult to be fabricated at a microscale and usually showing a pronounced degradation. By designing the filters in guided-mode resonance (GMR) architecture, light transmission efficiencies of ~90% with low sidebands and sharp peaks can be obtained, which are critical characteristics for realizing precise optical measurement systems. To optimize the transmission functions, various materials and structural parameters have been simulated. Electron beam nanolithography is employed in the device fabrication to fabricate pixel-wise independent filter functions. After being characterized in terms of their wavelength filtering capability, the developed GMR filters are then combined with image sensors, particularly for addressing biological applications.

We present a multilayer dielectrophoresis (DEP) chip for programmable trapping of single cells and obtaining electrical impedance spectroscopy (IS) data for the trapped cells. Our aim is to characterize the heterogeneity of tumor cells using impedance spectra. The final goal is the realization of an automated system for isolating tumor cells from blood samples, trapping cells, and measuring each single cell in a wide frequency band. In this contribution we introduce the design, describe the fabrication technology, and present simulations. In contrast to previous work of other workgroups, we address all traps individually and use an optimized electrode geometry. Additionally, an efficient fabrication process is applied.

Hyoid bone movement is an important physiological event during swallowing that contributes to normal swallowing function. In order to determine the adequate hyoid bone movement, clinicians conduct an X-ray videofluoroscopic swallowing study, which even though it is the gold-standard technique, has limitations such as radiation exposure and cost. Here, we demonstrated the ability to track the hyoid bone movement using a non-invasive accelerometry sensor attached to the surface of the human neck. Specifically, deep neural networks were used to mathematically describe the relationship between hyoid bone movement and sensor signals. Training and validation of the system were conducted on a dataset of 400 swallows from 114 patients. Our experiments indicated the computer-aided hyoid bone movement prediction has a promising performance when compared with human experts’ judgements, revealing that the universal pattern of the hyoid bone movement is acquirable by the highly nonlinear algorithm. Such a sensor-supported strategy offers an alternative and widely available method for online hyoid bone movement tracking without any radiation side-effects and provides a pronounced and flexible approach for identifying dysphagia and other swallowing disorders.

This paper relates to a genuine wrist pulse oximeter, which is a noninvasive medical device that can measure the pulse rate and oxygen saturation level in a person's blood. The device is novel due to its innovative design. It is a new type of reflective oximeter, which has a concave structure for housing the optical source and sensor. The neo-reflective sensor module of the device is designed to send the sensor data to a nearby intelligent mobile phone using wireless data transmission. The pulse oximeter has been developed and calibrated, and the calibration curve was analyzed. The innovative design of this pulse oximeter would enable the user to wear the low-cost device on one wrist continuously throughout the day, without the inconvenience of a conventional finger pulse oximeter.

This paper presents a non-linear framework employing a robust Polynomial filter for accomplishing enhancement of mammographic abnormalities outcoming from biomedical instrumentation, that is, X-rays instrumentation. The approach proposed in this work utilizes a linear combination of Type-0 and Type-II Polynomial filters as a generalized filtering solution to achieve enhancement of mammographic masses and calcifications irrespective of the nature of background tissues. A Type-0 filter provides contrast enhancement, suppressing the ill effects of background noise. On the other hand, Type-II filter performs edge enhancement leading to preservation of finer details. Contrast Improvement Index (CII) is used as a performance measure to quantify the degree of improvement in contrast of the region-of interest (ROI). In addition, estimation of signal-to-noise ratio (in terms of PSNR and ASNR) is carried out to account for the suppression in background noise levels and over-enhancements of the processed mammograms. These measures are used as a mechanism to optimally select the filter parameters and also serve as a quantifying platform to compare the performance of the proposed filter with other non-linear enhancement techniques to be used for diverse biomedical image sensors.

In this review, we have focused over MEMS (Micro Electro Mechanical System) technology in the area of biomedical, which have played in important role-as diagnostic equipment in the diagnoses of different human body parts. The miniaturization of classical “bulky” measurement techniques has led to the realization of complex analytic systems, including such sensors as a Biochem Lab-On-Chip. This rapid progress in miniature devices and instrumentation development will significantly impact the practice of medical care as well as future advances in the biomedical field. Recently, MEMS along with modern IC also effected significantly on electrochemical, optical and acoustic wave transducer technologies, which have emerged as some of the most promising biomedical sensing technologies. Here, important features of these technologies, along with new developments and some of the applications, are presented.

A digital baseband Application-Specific Integrated Circuit (ASIC) (yclic Redundancy Checkis developed for a microchip transponder to transmit signals and temperature levels from biomedical monitoring devices. The transmission protocol is adapted from the ISO/IEC 11784/85 standard. The module has a decimation filter that employs only a single adder-subtractor in its datapath. The filtered output is coded with cyclic redundancy check and transmitted through backscattering Load Shift Keying (LSK) modulation to a reader. Fabricated using the 0.18-μm CMOS technology, the module occupies 0.116 mm2 in chip area (digital baseband: 0.060 mm2, decimation filter: 0.056 mm2), and consumes a total of less than 0.9 μW of power (digital baseband: 0.75 μW, decimation filter: 0.14 μW)., {"references":["X. Liu, J. Zhou, Y. Yang, B. Wang, J. Lan, C. Wang, J. Luo, W. L. Goh, T.T.H. Kim, and M. Je, \"A 457 nW near-threshold cognitive multi-functional ECG processor for long-term cardiac monitoring,\" IEEE JSSC, vol. 49, pp. 2422–2434, Nov. 2014.","I.M. Wentholt, M.A. Vollebregt, A.A. Hart, J.B. Hoekstra, and J.H. DeVries, \"Comparison of a Needle-Type and a Microdialysis Continuous Glucose Monitor in Type 1 Diabetic Patients,\" in Diabetes Care, vol. 28, 2005. pp. 2871–2876.","\"ISO 11784:1996 Radio frequency identification of animals – Code structure\" by International Organization for Standardization.","\"ISO 11785:1996 Radio frequency identification of animals – Technical concept\" by International Organization for Standardization.","R.G. Lyons, Understanding Digital Signal Processing, Pearson Education, 2010, ch. 10.","A. Antoniou, Digital Signal Processing, Toronto: McGraw-Hill, 2006, p. 830.","L. Milić, Multirate Filtering for Digital Signal Processing, New York: Hershey, 2009, p. 35.","M.P. Donadio, CIC Filter Introduction, Iowegian, July 2000."]}

A theoretical analysis is presented on the accuracy of a novel method to extract the dielectric constant of deep tissues. The method exploits the variations of the electric fields on the surface of a multilayer dielectric scattering model and their dependence on dielectric properties of the deep tissues. The dielectric constant at a specific depth is expressed as a linear combination of the surface electric field samples. Critical parameters that affect the accuracy are determined through a numerical study, and basic guidelines for sensor design optimization are provided, using an emulation of a human torso. It is concluded that accuracies with errors

Nowadays Smart Wearable Systems (SWS) subject, emerges as one of the most popular research topic. Becouse of being comprimised from multiple disciplines, this area is favored by researchers of different areas. Devices of SWS are designed to measure physiological parameters for illnesses, chronic diseases, daily activity monitoring and other purposes, now it is finding more and more place in our lives. We can see the effects of SWS in health, sports, entertainment, education, business and other fileds, especially for the projects of health sector many products are available to the end users. Different SWS that are classified according to its features and methods of use have common features such as ability to make high-capacity wireless communication and housing embedded sensors. When invetigating the studies of SWS that include various forms and numbers of sensors, it is seen that most preferred sensors are; ECG sensor, Glucose sensor, body temperature sensor, blood pressure sensor, SpO(2) sensor and Accelerometer. General details of SWS and leading research projects from past to present are described within the scope of this article.

— Long term recording of biomedical signals such as ECG, EMG, respiration and other information (e.g. body motion) can improve diagnosis and potentially monitor the evolution of many widespread diseases. However, long term monitoring requires specific solutions, portable and wearable equipment that should be particularly comfortable for patients. The key-issues of portable biomedical instrumentation are: power consumption, long-term sensor stability, comfortable wearing and wireless connectivity. In this scenario, it would be valuable to realize prototypes using available technologies to assess long-term personal monitoring and foster new ways to provide healthcare services. The aim of this work is to discuss the advantages and the drawbacks in long term monitoring of biopotentials and body movements using textile electrodes embedded in clothes. The textile electrodes were embedded into garments; tiny shirt and short were used to acquire electrocardiographic and electromyographic signals. The garment was equipped with low power electronics for signal acquisition and data wireless transmission via Bluetooth. A small, battery powered, biopotential amplifier and three-axes acceleration body monitor was realized. Patient monitor incorporates a microcontroller, analog-to-digital signal conversion at programmable sampling frequencies. The system was able to acquire and to transmit realtime signals, within 10 m range, to any Bluetooth device (including PDA or cellular phone). The electronics were embedded in the shirt resulting comfortable to wear for patients. Small size MEMS 3-axes accelerometers were also integrated.

The U.S. health care system is one of the most advanced and costly systems in the world. The health services supply/demand gap is being enlarged by the aging population coupled with shortages in the traditional health care workforce and new information technology workers. This will not change if the current medical system adheres to the traditional hospital-centered model. One promising solution is to incorporate patient-centered, point-of-care test systems that promote proactive and preventive care by utilizing technology advancements in sensors, devices, communication standards, engineering systems, and information infrastructures. Biomedical devices optimized for home and mobile health care environments will drive this transition. This dissertation documents research and development focused on biomedical device design for this purpose (including a wearable wireless pulse oximeter, motion sensor, and two-thumb electrocardiograph) and, more importantly, their interactions with other medical components, their supporting information infrastructures, and processing tools that illustrate the effectiveness of their data. The GumPack concept and prototype introduced in Chapter 2 addresses these aspects, as it is a sensor-laden device, a host for a local body area network (BAN), a portal to external integration frameworks, and a data processing platform. GumPack sensor-component design (Chapters 3 and 4) is oriented toward surface applications (e.g., touch and measure), an everyday-carry form factor, and reconfigurability. Onboard tagging technology (Chapters 5 and 6) enhances sensor functionality by providing, e.g., a signal quality index and confidence coefficient for itself and/or next-tier medical components (e.g., a hub). Sensor interaction and integration work includes applications based on the GumPack design (Chapters 7 through 9) and the Medical Device Coordination Framework (Chapters 10 through 12). A high-resolution, wireless BAN is presented in Chapter 8, followed by a new physiological use case for pulse wave velocity estimation in Chapter 9. The collaborative MDCF work is transitioned to a web-based Hospital Information Integration System (Chapter 11) by employing database, AJAX, and Java Servlet technology. Given the preceding sensor designs and the availability of information infrastructures like the MDCF, medical platform-oriented devices (Chapter 12) could be an innovative and efficient way to design medical devices for hospital and home health care applications.

peer-reviewed The aim of this study is to model and design an efficient wireless system that should be easy to integrate with other technologies or infrastructures at a low cost. The system is reading analog information recorded by a biomedical sensor in a transmitting unit attached to the patient. The recorded data is converted digitally using ADC and sent to FSK transmitter through FPGA. Verilog HDL has been used to implement the required functions of the FPGA. SIMULINK software has been used to model and simulate Frequency-Shift Keying (FSK) Transmitter/ Receiver suitable for short-range communications. A two-tone FSK signal is generated, passed through a noisy channel, down converted to baseband and passed to FM detector to restore the original transmitted bit stream. The behavioral HDL design has been interfaced to the SIMULINK model and the overall performance has been verified. EI

Using a biomedical sensor setup RIfS we have investigated the kinetic behavior of human albumin (Alb), human fibrinogen (Fib), and human immunoglobulin G (IgG) adsorbed onto surfaces of chitosan. Polystyrene (PS) was used as the control material in this study. The optical thickness of three kinds of proteins measured by RIfS was related to their molecular dimensions and potential orientations on a film surface. According to the operation principle of RIfS and the molecular dimensions of three kinds of proteins, the adsorbed layers of proteins onto the surface of chitosan and PS was calculated by using a newly introduced equation. The microstructure of the chitosan and polystyrene film and the surfaces with adsorbed proteins were imaged by atomic force microscopy (AFM). With AFM analyses the lateral distribution of the protein molecules on surfaces have been recognized. The results show that the number of adsorbed layers of the three proteins on the surface of chitosan are 0.635 for Alb, 0.158 for Fib and 0.0967 for IgG, and of polystyrene are: 0.577 for IgG, 0.399 for Fib, 0.336 for Alb. This study confirmed that RIfS is a useful tool for the analysis of plasma proteins adsorbed on a surface of biomaterials. Results show that at first on the surface of chitosan film much more Alb than Fib was adsorbed which demonstrated that chitosan has a antithrombus function. Secondly, on the surface of chitosan film more Alb and less Fib were adsorbed than on the surface of PS film, which demonstrated that chitosan has a better blood compatibility than polystyrene. Thirdly, the calculated layer number of the three proteins indicated that on both chitosan and PS substrates monolayer coatings form.

Comunicació presentada a: 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS), celebrat del 6 al 9 d'octubre de 2021 virtualment In situ monitoring of biomedical parameters with implantable sensors can provide information to trigger interventional or therapeutic actions. However, these sensors require bulky components for power or for interrogation that hinder miniaturization. We have proposed a wireless sensing method based in passive rectification of high frequency current bursts that flow through the tissues by volume conduction. Here we report the evaluation of a 0.98 mm-thick, passive, and flexible temperature sensor based on this method. The injectable microsensor obtained an accuracy of ±2.1%. This opens the possibility of continuous and in situ temperature sensing with minimal invasiveness. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreements No 724244 (eAXON) and 963955 (SENSO-eAXON)), and the UPF INNOValora programme, which is co-financed by the Generalitat de Catalunya and the European Regional Development Fund. Antoni Ivorra gratefully acknowledges the financial support by ICREA under the ICREA Academia programme.