Search Results (27)

Flexible and biocompatible sensors are vital for a wide range of biomedical applications, including real-time health monitoring, intracranial pressure monitoring, knee replacement surgeries, wearables, and smart prosthetics. While various highly sensitive and stable pressure sensors have been demonstrated, they often lack the conformability and biocompatibility crucial for their wider application in various bio-integrated electronic systems. Herein, a piezoelectric pressure sensor is proposed using a hybrid polymer composite by leveraging the unique properties of Chitosan and Parylene C. Various material characterisations, such as XRD and FTIR, were performed to reveal structural and chemical characteristics of the novel composite material. Next, electromechanical characterisations of the pressure sensor were performed to reveal its dynamic sensing properties. The pressure sensor exhibits excellent sensitivity for both pressure and frequency, as well as cyclic stability (10³ cycles), wide pressure range (20–70 kPa), and biocompatibility. Full article

Monitoring intracranial pressure (ICP) dynamics is critical for the management of traumatic brain injury, stroke, other neurosurgical conditions, and cerebral blood flow autoregulation; however, invasive ICP monitoring carries risks such as infection, hemorrhage, and sensor zero drift. Increasing evidence suggests that ICP waveform morphology provides clinically relevant information beyond mean ICP value alone. In this first-in-human prospective comparative clinical study, we evaluated the feasibility and accuracy of a novel, fully passive, non-invasive ICP pulse waveform monitoring system (Archimedes 02) based on the detection of eyeball mechanical movement. Fifteen intensive care unit patients (6 males, 9 females; mean age 57.1 ± 18.8 years) with clinically indicated invasive ICP monitoring or external ventricular drainage were enrolled. Three-minute monitoring sessions were performed to simultaneously acquire non-invasive ICP pulse waveforms, invasive ICP waveforms, and invasive radial artery blood pressure (ABP) waveforms. Averaged waveforms were derived for each patient and compared graphically and using correlation analysis. Non-invasive ICP pulse waves recorded with Archimedes 02 showed a strong correlation with invasive ICP waveforms ($\overline{\mathrm{R}}$ = 0.965). In contrast, correlations between non-invasive ICP and ABP waveforms ($\overline{\mathrm{R}}$ = 0.699), as well as between invasive ICP and ABP waveforms ($\overline{\mathrm{R}}$ = 0.749), were lower. These findings indicate that the non-invasive signal primarily reflects ICP dynamics rather than arterial blood pressure. This novel non-invasive ICP monitoring approach has the potential to enhance neurocritical care, particularly in settings where invasive monitoring is impractical or unavailable. Further validation in larger and more diverse patient populations is warranted. Full article

Accurate monitoring of intracranial pressure (ICP) is critical for the diagnosis and management of neurological disorders. Although various ICP sensors have been developed, their sensitivity is often limited, restricting their ability to detect subtle pressure variations. Therefore, there is a pressing need to develop ICP sensors with enhanced sensitivity to improve measurement accuracy and patient outcomes. In this paper, a highly sensitive and precise pressure sensor for intracranial pressure (ICP) monitoring was proposed. Theoretically, the beam-membrane-island structure was introduced and optimized to improve sensitivity and linearity compared to a flat membrane structure. The notches etched at beam end were designed for further improving sensitivity. Experimentally, the designed sensor achieved a sensitivity of 1.59 mV/V//kPa and a nonlinearity of −0.22% F.S. Additionally, the sensor can detect pressure with centimeter water column (cm H₂O) resolution, making it suitable for ICP monitoring. This technology holds broad application prospects in the field of medical devices. Full article

Intracranial pressure (ICP) pulse wave morphology, including the ratios of the three characteristic peaks (P1, P2, and P3), offers valuable insights into intracranial dynamics and brain compliance. Traditional invasive methods for ICP pulse wave monitoring pose significant risks, highlighting the need for non-invasive alternatives. This pilot study investigates a novel non-invasive method for monitoring ICP pulse waves through closed eyelids, using a specially designed, liquid-filled, fully passive sensor system named ‘Archimedes 02’. To our knowledge, this is the first technological approach that enables the non-invasive monitoring of ICP pulse waveforms via closed eyelids. This study involved 10 healthy volunteers, aged 26–39 years, who underwent resting-state non-invasive ICP pulse wave monitoring sessions using the ‘Archimedes 02’ device while in the supine position. The recorded signals were processed to extract pulse waves and evaluate their morphological characteristics. The results indicated successful detection of pressure pulse waves, showing the expected three peaks (P1, P2, and P3) in all subjects. The calculated P2/P1 ratios were 0.762 (SD = ±0.229) for the left eye and 0.808 (SD = ±0.310) for the right eye, suggesting normal intracranial compliance across the cohort, despite variations observed in some individuals. Physiological tests—the Valsalva maneuver and the Queckenstedt test, both performed in the supine position—induced statistically significant increases in the P2/P1 and P3/P1 ratios, supporting the notion that non-invasively recorded pressure pulse waves, measured through closed eyelids, reflect intracranial volume and pressure dynamics. Additionally, a transient hypoemic/hyperemic response test performed in the upright position induced signal changes in pressure recordings from the ‘Archimedes 02’ sensor that were consistent with intact cerebral blood flow autoregulation, aligning with established physiological principles. These findings indicate that ICP pulse waves and their dynamic changes can be monitored non-invasively through closed eyelids, offering a potential method for brain monitoring in patients for whom invasive procedures are not feasible. Full article

Background/Objectives: This study aimed to evaluate the results of continuous intracranial pressure (ICP) monitoring in children with macrocephaly or rapidly increasing head circumference (HC) diagnosed as benign external hydrocephalus (BEH). Here, we report the absolute ICP measurements, ICP pulsatility, and slow ICP waves after at least 48 h of continuous monitoring in a cohort of 36 children diagnosed with BEH. Methods: A prospective study of continuous ICP monitoring was performed in 36 consecutive children with macrocephaly (HC above the 97.5th percentile) or rapidly increasing HC (at least crossing two percentile curves), diagnosed with BEH (22 boys and 14 girls with a mean age of 23.6 ± 13.3 months, minimum: 6, maximum 65), using an epidural sensor. For the first four children in the study, hard copies of the ICP values were obtained using an analog recorder. Starting from the fifth patient, the ICP signal was sampled at 200 Hz and stored on a computer using a computer-based data acquisition and analysis system (LabChart v8.1 software). Results: Clinical signs or symptoms were identified in 20 patients (55.6%). Delayed motor or language development was noted in 18 (50%) and 20 (55.6%) patients, respectively. In 13 patients, the enlargement of the subarachnoid spaces was found to be associated with an additional condition. The median of mean ICP values for the entire cohort was 17 mmHg, with a minimum of 6.7 mmHg and a maximum of 29 mmHg. All patients exhibited a percentage of B waves exceeding 20% during the night, with a median value of 47.4% (min: 23.2, max: 75). Three children had nocturnal plateau waves. At night, regular ICP recordings alternated with periods of significant increases in ICP, often exceeding 10 mmHg above baseline values. High-amplitude B waves were noted during these episodes, and the amplitude of the cardiac waveform at the peak of the B waves was consistently greater than 5 mmHg, displaying an abnormal morphology (P2 > P1). A ventriculoperitoneal shunt was implanted in 30 of the 36 patients. Conclusions: Patients with BEH may present significant abnormalities in ICP. Monitoring this variable in certain cases can assist in determining the necessity for surgical treatment. Full article

Background and Objectives: Normal-tension glaucoma (NTG) is a subtype of primary open-angle glaucoma characterized by progressive optic nerve damage despite intraocular pressure (IOP) remaining within the normal range. The underlying pathophysiology of NTG remains incompletely understood, and its diagnosis is often delayed due to the lack of a definitive screening tool. This study aimed to evaluate differences in intracranial pressure pulse wave amplitude recorded from closed eyelids between NTG patients and control subjects using a novel non-invasive monitoring technology. Materials and Methods: A cross-sectional observational study was conducted, enrolling NTG patients and age-matched controls. Intracranial pressure pulse wave signals were recorded from closed eyelids using the ’Archimedes’ 02 device, which employs a highly sensitive digital pressure sensor and hydromechanical coupling for signal transmission. The amplitude of recorded intracranial pressure pulse waves was analyzed and compared between groups. Statistical analyses were performed using IBM SPSS Statistics 30.0, with significance set at p < 0.05. Results: A total of 140 participants were enrolled, including 68 NTG patients and 72 controls. After applying exclusion criteria, 63 NTG patients and 68 controls were included in the final analysis. The median intracranial pressure pulse wave amplitude was significantly higher in NTG patients (0.1326 a.u.) than in controls (0.0889 a.u.), with p = 0.01. Conclusions: These findings suggest that intracranial pressure pulse wave monitoring may serve as a potential biomarker for NTG. Further studies are needed to determine the diagnostic accuracy, sensitivity, and specificity of this technology for NTG detection. Full article

Goal: Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. RAP, a metric for measuring intracranial compensatory reserve (and therefore compliance), holds promise. It is derived using the moving correlation between intracranial pressure (ICP) and the pulse amplitude of ICP (AMP). RAP remains largely unexplored in cases of moderate to severe acute traumatic neural injury (also known as traumatic brain injury (TBI)). The goal of this work is to explore the general description of (a) RAP signal patterns and behaviors derived from ICP pressure transducers, (b) temporal statistical relationships, and (c) the characterization of the artifact profile. Methods: Different summary and statistical measurements were used to describe RAP’s pattern and behaviors, along with performing sub-group analyses. The autoregressive integrated moving average (ARIMA) model was employed to outline the time-series structure of RAP across different temporal resolutions using the autoregressive (p-order) and moving average orders (q-order). After leveraging the time-series structure of RAP, similar methods were applied to ICP and AMP for comparison with RAP. Finally, key features were identified to distinguish artifacts in RAP. This might involve leveraging ICP/AMP signals and statistical structures. Results: The mean and time spent within the RAP threshold ranges ([0.4, 1], (0, 0.4), and [−1, 0]) indicate that RAP exhibited high positive values, suggesting an impaired compensatory reserve in TBI patients. The median optimal ARIMA model for each resolution and each signal was determined. Autocorrelative function (ACF) and partial ACF (PACF) plots of residuals verified the adequacy of these median optimal ARIMA models. The median of residuals indicates that ARIMA performed better with the higher-resolution data. To identify artifacts, (a) ICP q-order, AMP p-order, and RAP p-order and q-order, (b) residuals of ICP, AMP, and RAP, and (c) cross-correlation between residuals of RAP and AMP proved to be useful at the minute-by-minute resolution, whereas, for the 10-min-by-10-min data resolution, only the q-order of the optimal ARIMA model of ICP and AMP served as a distinguishing factor. Conclusions: RAP signals derived from ICP pressure sensor technology displayed reproducible behaviors across this population of TBI patients. ARIMA modeling at the higher resolution provided comparatively strong accuracy, and key features were identified leveraging these models that could identify RAP artifacts. Further research is needed to enhance artifact management and broaden applicability across varied datasets. Full article

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. Multivariate machine learning models have proven particularly effective in this domain, capturing intricate relationships among multiple variables simultaneously and enabling the accurate modeling of cerebral physiologic signals. These models facilitate the development of advanced diagnostic and prognostic tools, promote patient-specific interventions, and improve therapeutic outcomes. Additionally, machine learning models offer great flexibility, allowing different models to be combined synergistically to address complex challenges in sensor-based data analysis. Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. This review explores the use of multivariate machine learning models in cerebral physiology as a whole, with an emphasis on sensor-derived signals related to hemodynamics, cerebral oxygenation, metabolism, and other modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) where applicable. It will detail the operational principles, mathematical foundations, and clinical implications of these models, providing a deeper understanding of their significance in monitoring cerebral function. Full article

Patients with acute brain injury (ACI) often require mechanical ventilation (MV) and are subject to pulmonary complications, thus justifying the use of Airway Clearance Techniques (ACTs), but their effects on intracranial pressure (ICP) are unknown. This study investigates the neurological and hemodynamics safety of an ACT called ventilator hyperinflation (VHI) in patients with ACI. This was a randomized clinical equivalence trial, which included patients aged ≥ 18 years with a clinical diagnosis of hemorrhagic stroke, with symptom onset within 48 h. The participants were randomly allocated to the Experimental Group (EG, n = 15), which underwent VHI followed by tracheal aspiration (TA), and the Control Group (CG, n = 15), which underwent TA only. Neurological safety was verified by analyzing the morphology of the ICP wave through the non-invasive B4C sensor, which detects bone deformation of the skull, resulting in a P2/P1 ratio and TTP, and hemodynamics through a multi-parameter monitor. Evaluations were recorded during five instances: T1 (baseline/pre-VHI), T2 (post-VHI and before TA), T3 (post-TA), T4 and T5 (monitoring 10 and 20 min after T3). The comparison between groups showed that there was no effect of the technique on the neurological variables with a mean P2/P1 ratio [F (4,112) = 1.871; p = 0.120; np2 = 0.063] and TTP [F (4,112) = 2.252; p = 0.068; np2 = 0.074], and for hemodynamics, heart rate [F (4,112) = 1.920; p = 0.112; np2 = 0.064] and mean arterial pressure [F(2.73, 76.57) = 0.799; p = 0.488; np2 = 0.028]. Our results showed that VHI did not pose a neurological or hemodynamics risk in neurocritical patients after ACI. Full article

The modeling and forecasting of cerebral pressure–flow dynamics in the time–frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure–flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications. Full article

Polydimethylsiloxane (PDMS) has emerged as a promising candidate for the dielectric layer in implantable sensors due to its exceptional biocompatibility, stability, and flexibility. This study introduces an innovative approach to produce graphene-reinforced PDMS (Gr-PDMS), where graphite powders are exfoliated into mono- and few-layer graphene sheets within the polymer solution, concurrently forming cross-linkages with PDMS. This method yields a uniformly distributed graphene within the polymer matrix with improved interfaces between graphene and PDMS, significantly reducing the percolation threshold of graphene dispersed in PDMS from 10% to 5%. As-synthesized Gr-PDMS exhibits improved mechanical and electrical properties, tested for potential use in capacitive pressure sensors. The results demonstrate an impressive pressure sensitivity up to 0.0273 kpa⁻¹, 45 times higher than that of pristine PDMS and 2.5 times higher than the reported literature value. The Gr-PDMS showcases excellent pressure sensing ability and stability, fulfilling the requirements for implantable intracranial pressure (ICP) sensors. Full article

The contemporary monitoring of cerebrovascular reactivity (CVR) relies on invasive intracranial pressure (ICP) monitoring which limits its application. Interest is shifting towards near-infrared spectroscopic regional cerebral oxygen saturation (rSO₂)-based indices of CVR which are less invasive and have improved spatial resolution. This study aims to examine and model the relationship between ICP and rSO₂-based indices of CVR. Through a retrospective cohort study of prospectively collected physiologic data in moderate to severe traumatic brain injury (TBI) patients, linear mixed effects modeling techniques, augmented with time-series analysis, were utilized to evaluate the ability of rSO₂-based indices of CVR to model ICP-based indices. It was found that rSO₂-based indices of CVR had a statistically significant linear relationship with ICP-based indices, even when the hierarchical and autocorrelative nature of the data was accounted for. This strengthens the body of literature indicating the validity of rSO₂-based indices of CVR and potential greatly expands the scope of CVR monitoring. Full article

Hydrocephalus is a clinical disorder caused by excessive cerebrospinal fluid (CSF) buildup in the ventricles of the brain, often requiring permanent CSF diversion via an implanted shunt system. Such shunts are prone to failure over time; an ambulatory intracranial pressure (ICP) monitoring device may assist in the detection of shunt failure without an invasive diagnostic workup. Additionally, high resolution, noninvasive intracranial pressure monitoring will help in the study of diseases such as normal pressure hydrocephalus (NPH) and idiopathic intracranial hypertension (IIH). We propose an implantable, continuous, rechargeable ICP monitoring device that communicates via Bluetooth with mobile applications. The design requirements were met at the lower ICP ranges; the obtained error fell within the idealized ±2 mmHg margin when obtaining pressure values at or below 20 mmHg. The error was slightly above the specified range at higher ICPs (±10% from 20–100 mmHg). The system successfully simulates occlusions and disconnections of the proximal and distal catheters, valve failure, and simulation of A and B ICP waves. The mobile application accurately detects the ICP fluctuations that occur in these physiologic states. The presented macro-scale prototype is an ex-vivo model of an implantable, rechargeable ICP monitoring system that has the potential to measure clinically relevant ICPs and wirelessly provide accessible and continuous data to aid in the workup of shunt failure. Full article

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice. Full article

Implantable sensors normally require devices with excellent biocompatibility and flexibility as well as wireless communication. Silk fibroin (SF) is an ideal material for implantable electronic devices due to its natural biodegradability and biocompatibility. In this work, we prepared SF protein materials with different force/chemical properties through mesoscopic regulation, and realized full protein replacement from substrate to dielectric elastomer for implantable sensors, so as to achieve controlled complete degradation. In wireless tests simulating intracranial pressure, the SF-based all-protein sensor achieved a sensitivity up to 4.44 MHz/mmHg in the pressure range of 0–20 mmHg. In addition, the sensor is insensitive to temperature changes and tissue environments, and can work stably in simulated body fluids for a long time. This work provides a wireless passive, all-protein material solution for implantable pressure sensors. Full article