28 research outputs found
Skin-Integrated wearable systems and implantable biosensors: a comprehensive review
Biosensors devices have attracted the attention of many researchers across the world. They have the capability to solve a large number of analytical problems and challenges. They are future ubiquitous devices for disease diagnosis, monitoring, treatment and health management. This review presents an overview of the biosensors field, highlighting the current research and development of bio-integrated and implanted biosensors. These devices are micro- and nano-fabricated, according to numerous techniques that are adapted in order to offer a suitable mechanical match of the biosensor to the surrounding tissue, and therefore decrease the body’s biological response. For this, most of the skin-integrated and implanted biosensors use a polymer layer as a versatile and flexible structural support, combined with a functional/active material, to generate, transmit and process the obtained signal. A few challenging issues of implantable biosensor devices, as well as strategies to overcome them, are also discussed in this review, including biological response, power supply, and data communication.This research was funded by FCT- FUNDAÇÃO PARA A CIÊNCIA E TECNOLOGIA, grant numbers: PTDC/EMD-EMD/31590/2017 and PTDC/BTM-ORG/28168/2017
Self-folding 3D micro antennas for implantable medical devices
Tese de Doutoramento em Engenharia Biomédica.Recent advances in device miniaturization have been enabling smart and small implantable medical devices. These are often powered by bulky batteries whose dimensions represent one of the major bottlenecks on further device miniaturization. However, alternative powering methods, such as electromagnetic waves, do not rely on stored energy and are capable of providing high energy densities per unit of area, thus increasing the potential for device miniaturization. Hence, we envision an implanted medical device with an integrated miniaturized antenna, capable of receiving a radiofrequency signal from an exterior source, and converting it to a DC signal, thus enabling remote powering.
This thesis addresses the analysis, design, fabrication and characterization of novel 3D micro antennas that can be integrated on 500 × 500 × 500 μm3 cubic devices, and used for wireless power transfer purposes. The analysis is built upon the theory of electrically small antennas in lossy media, and the antenna design takes into consideration miniaturization techniques which are compatible with the antenna fabrication process. For the antenna fabrication, a methodology that combines conventional planar photolithography techniques and self-folding was used. While photolithography allows the easy patterning of virtually every desired planar antenna configuration with reproducible feature precision, and the flexibility to easily and precisely change the antenna geometry and size, self-folding allows assembly of the fabricated planar patterns into a 3D structure in a highly parallel and scalable manner.
After fabrication, we characterized the fabricated antennas by measuring their S-parameters and radiation patterns, demonstrating their efficacy at 2 GHz when immersed in dispersive media such as water. This step required the development and test of multiple characterization setups based on connectors, RF probes and transmission lines and the use of an anechoic chamber. Moreover, we successfully show that the antennas can wireless transfer energy to power an LED, highlighting a proof of concept for practical applications. Our findings suggest that self-folding micro antennas could provide a viable solution for powering tiny micro devices.Os recentes avanços das tecnologias de miniaturização têm permitido o desenvolvimento de dispositivos médicos implantáveis inteligentes e mais pequenos. Estes são muitas vezes alimentados por baterias volumosas cujas dimensões limitam o nível de miniaturização alcançável por um micro dispositivo. No entanto, existem formas alternativas de alimentar estes dispositivos que não dependem de energia armazenada, tais como ondas eletromagnéticas, que são capazes de providenciar uma elevada densidade de energia por unidade de área, aumentando assim o potencial de miniaturização dos dispositivos. Desta forma, visionamos um dispositivo médico implantado, com uma antena miniaturizada e integrada, capaz de receber um sinal de rádio frequência a partir de uma fonte externa, e convertê-lo num sinal DC, permitindo assim a alimentação remota do aparelho. Esta tese apresenta a análise, desenho, fabrico e caracterização de micro antenas 3D, passíveis de serem integradas em micro dispositivos cúbicos (500 × 500 × 500 μm3), e utilizadas para fins de transferência de energia sem fios. A análise assenta na teoria das antenas eletricamente pequenas em meios com perdas, e o design da antena considera técnicas de miniaturização de antenas. Para o fabrico da antena foi utilizada uma metodologia que combina técnicas de fotolitografia planar e auto-dodragem (self-folding). Enquanto a fotolitografia permite a padronização de virtualmente todos os tipos de configurações planares de forma precisa, reprodutível, e com a flexibilidade para se mudar rapidamente a geometria e o tamanho da antena, o self-folding permite a assemblagem dos painéis planares fabricados numa estrutura 3D. Depois do fabrico, as antenas foram caracterizadas medindo os seus parâmetros S e diagramas de radiação, demonstrando a sua eficácia a 2 GHz quando imersas num meio dispersivo, tal como água. Esta etapa exigiu o desenvolvimento e teste de várias setups de caracterização com base em conectores, sondas de RF e linhas de transmissão, e ainda o uso de uma câmara anecóica. Além disso, mostramos com sucesso que as micro antenas podem receber e transferir o energia para um LED acendendo-o, destacando assim esta prova de conceito para aplicações práticas. Os nossos resultados sugerem que estas micro antenas auto-dobráveis podem fornecer uma solução viável para alimentar micro dispositivos implantáveis muito pequenos.Fundação para a Ciência e a Tecnologia (FCT) bolsa SFRH/BD/63737/2009
Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring
The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectation-maximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study. The ECG signal data that has been predicted is then compared with the actual ECG data stored in the main controller memory. Root mean square error (RMSE) is calculated to measure system performance. The simulation was performed on 13 ECG waves, each of them has 1000 samples. The simulation results show that the EM method has a lower predictive error value than the RI method. The average RMSE for the EM and RI methods is 4.77 and 6.63, respectively. The proposed method is expected to be used in the case of multi-node ECG monitoring, especially in the ZigBee application to minimize errors
Precordial Bipolar Leads for Mobile ECG Applications
Advances in measurement technology and wireless signal transfer have enabled the design of new, smaller and portable—even plaster-like—electrocardiographic (ECG) measurement devices that enable patient monitoring at home or in emergency situations. The development of new, miniaturized biomedical sensors has opened up possibilities for their application, but also set new demands on signal analysis and interpretation. In particular, the new small wireless systems often utilize bipolar electrodes that have a shorter interelectrode distance (IED) and different electrode locations from those of the standard 12-lead system. This affects the quality and the information content of the signal. The general objective of this thesis was to evaluate the performance of short IED precordial bipolar ECG leads and to determine their optimal location.
This thesis adopted three methods to assess the properties of new bipolar precordial ECG leads: modeling, body surface potential map (BSPM) data, and exercise ECG data. First, two realistic, three-dimensional (3D) thorax models and lead field analysis were used to evaluate whether modeling of the measuring sensitivity of ECG leads could be used as a tool for designing new ECG leads. Second, BSPM data was used to study whether short-distance bipolar leads (IED approximately 6 cm) provide an ECG signal that is adequate for clinical utilization. Third, BSPM data was used to define where a bipolar ECG lead should be located in order to maximize the ECG signal strength within healthy subjects. Finally, the value of bipolar leads for diagnosing two major cardiac conditions—left ventricular hypertrophy (LVH) and coronary artery disease (CAD)—was assessed.
It was found that the modeled measuring sensitivity corresponds to the changes in actual ECG signal strength, so modeling can be useful, especially in cases where in vivo measurements are impossible such as in designing implantable applications. Based on ECG data from 236 healthy subjects, all studied bipolar ECG leads with a short IED (approximately 6 cm) provided a detectable signal when compared to a low noise level of 15 μV and considering the P-wave as the smallest parameter. The optimal location of the bipolar lead was diagonally near the chest electrodes of the standard precordial leads V2, V3, and V4 (to maximize QRS amplitude), or above the chest electrodes of leads V1 and V2 (to maximize P-wave amplitude). In the selected clinical applications, LVH and CAD, the performance of bipolar leads was surprisingly good. In differentiating LVH (n=305) and healthy subjects (n=236), the performance of a correctly positioned small bipolar lead was similar to that of the traditional Sokolow-Lyon method. When differentiating CAD (n=255) patients from non-CAD (n=126) or low-likelihood of CAD (n=198) subjects, the overall performance of bipolar lead CM5 corresponded to that of standard lead V5.
These results indicate that short IED bipolar leads provide a signal that is adequate for clinical use. Furthermore, the performance of these leads was shown to be similar or even superior to that of the commonly used standard leads. It can be concluded that when correctly positioned, short IED bipolar leads are useful and can give additional value for clinical diagnostics. These results provide promising information on the applicability and potential of short IED bipolar ECG leads, and demonstrate that they are worth developing further
Design and implementation of a multi-modal sensor with on-chip security
With the advancement of technology, wearable devices for fitness tracking, patient monitoring, diagnosis, and disease prevention are finding ways to be woven into modern world reality. CMOS sensors are known to be compact, with low power consumption, making them an inseparable part of wireless medical applications and Internet of Things (IoT). Digital/semi-digital output, by the translation of transmitting data into the frequency domain, takes advantages of both the analog and digital world. However, one of the most critical measures of communication, security, is ignored and not considered for fabrication of an integrated chip. With the advancement of Moore\u27s law and the possibility of having a higher number of transistors and more complex circuits, the feasibility of having on-chip security measures is drawing more attention. One of the fundamental means of secure communication is real-time encryption. Encryption/ciphering occurs when we encode a signal or data, and prevents unauthorized parties from reading or understanding this information. Encryption is the process of transmitting sensitive data securely and with privacy. This measure of security is essential since in biomedical devices, the attacker/hacker can endanger users of IoT or wearable sensors (e.g. attacks at implanted biosensors can cause fatal harm to the user). This work develops 1) A low power and compact multi-modal sensor that can measure temperature and impedance with a quasi-digital output and 2) a low power on-chip signal cipher for real-time data transfer
Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging
Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET).
Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations.
Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes.
Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa
Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa.
Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia.
Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa.
Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua
2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society.
This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored
A pervasive body sensor network for monitoring post-operative recovery
Over the past decade, miniaturisation and cost reduction brought about by the semiconductor industry has led to computers smaller in size than a pin head, powerful enough to carry out the processing required, and affordable enough to be disposable. Similar technological advances in wireless communication, sensor design, and energy storage have resulted in the development of wireless “Body Sensor Network (BSN) platforms comprising of tiny integrated micro sensors with onboard processing and wireless data transfer capability, offering the prospect of pervasive and continuous home health monitoring. In surgery, the reduced trauma of minimally invasive interventions combined with initiatives to reduce length of hospital stay and a socioeconomic drive to reduce hospitalisation costs, have all resulted in a trend towards earlier discharge from hospital. There is now a real need for objective, pervasive, and continuous post-operative home recovery monitoring systems. Surgical recovery is a multi-faceted and dynamic process involving biological, physiological, functional, and psychological components. Functional recovery (physical independence, activities of daily living, and mobility) is recognised as a good global indicator of a patient’s post-operative course, but has traditionally been difficult to objectively quantify. This thesis outlines the development of a pervasive wireless BSN system to objectively monitor the functional recovery of post-operative patients at home. Biomechanical markers were identified as surrogate measures for activities of daily living and mobility impairment, and an ear-worn activity recognition (e-AR) sensor containing a three-axis accelerometer and a pulse oximeter was used to collect this data. A simulated home environment was created to test a Bayesian classifier framework with multivariate Gaussians to model activity classes. A real-time activity index was used to provide information on the intensity of activity being performed. Mobility impairment was simulated with bracing systems and a multiresolution wavelet analysis and margin-based feature selection framework was used to detect impaired mobility. The e-AR sensor was tested in a home environment before its clinical use in monitoring post-operative home recovery of real patients who have undergone surgery. Such a system may eventually form part of an objective pervasive home recovery monitoring system tailored to the needs of today’s post-operative patient.Open acces
Sensor technologies for quality control in engineered tissue manufacturing
The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells’ health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing
1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface
A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance