Implantable sensors for heart failure monitoring

Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive measurements even if, in the latest years, invasive microelectromechanical systems (MEMS) sensors have emerged as a valuable approach for precise and continuous monitoring of cardiac activity. The main challenges in designing cardiac MEMS sensors are represented by miniaturization, biocompatibility, and long-term stability. Here, we present a MEMS piezoresistive cardiac sensor capable of continuous monitoring of LV activity over time following epicardial implantation with a pericardial patch graft in adult minipigs. In acute and chronic scenarios, the sensor was able to compute heart rate with a root mean square error lower than 2 BPM. Early after up to 1 month of implantation, the device was able to record the heart activity during the most important phases of the cardiac cycle (systole and diastole peaks). The sensor signal waveform, in addition, closely reflected the typical waveforms of pressure signal obtained via intraventricular catheters, offering a safer alternative to heart catheterization. Furthermore, histological analysis of the LV implantation site following sensor retrieval revealed no evidence of myocardial fibrosis. Our results suggest that the epicardial LV implantation of an MEMS sensor is a suitable and reliable approach for direct continuous monitoring of cardiac activity. This work envisions the use of this sensor as a cardiac sensing device in closed-loop applications for patients undergoing heart surgery.

Graphene as a Piezoresistive Material in Strain Sensing Applications

High accuracy measurement of mechanical strain is critical and broadly practiced in several application areas including structural health monitoring, industrial process control, manufacturing, avionics and the automotive industry, to name a few. Strain sensors, otherwise known as strain gauges, are fueled by various nanomaterials, among which graphene has attracted great interest in recent years, due to its unique electro-mechanical characteristics. Graphene shows not only exceptional physical properties but also has remarkable mechanical properties, such as piezoresistivity, which makes it a perfect candidate for strain sensing applications. In the present review, we provide an in-depth overview of the latest studies focusing on graphene and its strain sensing mechanism along with various applications. We start by providing a description of the fundamental properties, synthesis techniques and characterization methods of graphene, and then build forward to the discussion of numerous types of graphene-based strain sensors with side-by-side tabular comparison in terms of figures-of-merit, including strain range and sensitivity, otherwise referred to as the gauge factor. We demonstrate the material synthesis, device fabrication and integration challenges for researchers to achieve both wide strain range and high sensitivity in graphene-based strain sensors. Last of all, several applications of graphene-based strain sensors for different purposes are described. All in all, the evolutionary process of graphene-based strain sensors in recent years, as well as the upcoming challenges and future directions for emerging studies are highlighted.

A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring

Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.

A Review on Theory and Modelling of Nanomechanical Sensors for Biological Applications

Over the last decades, nanomechanical sensors have received significant attention from the scientific community, as they find plenty of applications in many different research fields, ranging from fundamental physics to clinical diagnosis. Regarding biological applications, nanomechanical sensors have been used for characterizing biological entities, for detecting their presence, and for characterizing the forces and motion associated with fundamental biological processes, among many others. Thanks to the continuous advancement of micro- and nano-fabrication techniques, nanomechanical sensors have rapidly evolved towards more sensitive devices. At the same time, researchers have extensively worked on the development of theoretical models that enable one to access more, and more precise, information about the biological entities and/or biological processes of interest. This paper reviews the main theoretical models applied in this field. We first focus on the static mode, and then continue on to the dynamic one. Then, we center the attention on the theoretical models used when nanomechanical sensors are applied in liquids, the natural environment of biology. Theory is essential to properly unravel the nanomechanical sensors signals, as well as to optimize their designs. It provides access to the basic principles that govern nanomechanical sensors applications, along with their intrinsic capabilities, sensitivities, and fundamental limits of detection.

Suspended Graphene Membranes with Attached Silicon Proof Masses as Piezoresistive Nanoelectromechanical Systems Accelerometers

Graphene is an atomically thin material that features unique electrical and mechanical properties, which makes it an extremely promising material for future nanoelectromechanical systems (NEMS). Recently, basic NEMS accelerometer functionality has been demonstrated by utilizing piezoresistive graphene ribbons with suspended silicon proof masses. However, the proposed graphene ribbons have limitations regarding mechanical robustness, manufacturing yield, and the maximum measurement current that can be applied across the ribbons. Here, we report on suspended graphene membranes that are fully clamped at their circumference and have attached silicon proof masses. We demonstrate their utility as piezoresistive NEMS accelerometers, and they are found to be more robust, have longer life span and higher manufacturing yield, can withstand higher measurement currents, and are able to suspend larger silicon proof masses, as compared to the previous graphene ribbon devices. These findings are an important step toward bringing ultraminiaturized piezoresistive graphene NEMS closer toward deployment in emerging applications such as in wearable electronics, biomedical implants, and internet of things (IoT) devices.

The Miniaturization of Cardiac Implantable Electronic Devices: Advances in Diagnostic and Therapeutic Modalities

The Fourth Industrial Revolution, characterized by an unprecedented fusion of technologies that is blurring the lines between the physical, digital, and biological spheres, continues the trend to manufacture ever smaller mechanical, optical and electronic products and devices. In this manuscript, we outline the way cardiac implantable electronic devices (CIEDs) have evolved into remarkably smaller units with greatly enhanced applicability and capabilities.