A Dual Parameter Synchronous Monitoring System of Brain Edema Based on the Reflection and Transmission Characteristics of Two-Port Test Network

Abstract

As a common secondary disease, edema after traumatic brain injury (TBI) can increase brain volume, resulting in elevated intracranial pressure (ICP), brain shift, and cerebral hernia, and can eventually lead to death. The real-time continuous monitoring of edema may significantly reduce mortality and disability. In this paper, a dual-parameter synchronous monitoring system of edema based on the reflection and transmission characteristics of the two-port test network was established; 15 rabbits were chosen to perform 24-h reflection phase shift (RPS) and transmission phase shift (TPS) simultaneously monitoring the experiments of brain edema. With the development of brain edema, the variation law of the RPS and TPS was investigated. Combined with the power amplitude spectrum and the principle of the two-port test network, the influence of frequency on the detection sensitivity of RPS and TPS was analyzed in detail, and the optimal detection frequency point was found. After that, the classification of three different degrees of edema is performed by the BP algorithm. In the animal experiment, the RPS showed a continuous increasing trend within time, and it presented the variation of (9.35910 • ± 1.65702 •), (12.60117 • ± 2.30218 •), and (16.33423 • ± 2.11118 •) after 6, 12, and 24 h, respectively. Meanwhile, the TPS showed a continuous downward trend with the variation of (−12.62555 • ± 0.99441 •), (−19.23976 • ± 1.27488 •), and (−27.26285 • ± 2.62291 •) after 6, 12, and 24 h, respectively. The RPS was negatively correlated with the TPS. The RPS and the TPS together as a recognition feature can achieve 100% accurate classification of three different brain edema severities. Based on these results, it can be concluded that the system established in this paper can monitor gradual increases in brain edema severity. Furthermore, neither the RPS nor the TPS can be set as the recognition feature alone to achieve the completely accurate classification, which shows the necessity of presenting two parameters in the monitoring process. INDEX TERMS Brain edema, two-port test network, synchronous monitoring, classification.

A noninvasive flexible conformal sensor for accurate real-time monitoring of local cerebral edema based on electromagnetic induction

“…Our previous finding showed a 24 h downward trend of the MIPS with largest change of −13.1121 ± 2.3953 • at the 24th hour in CE group and no obvious trend in the control group (−0.87795 ± 1.5146 • at 24th hour). This conformal MIPS sensor is consistent with previous studies in monitoring and distinguishing CE (Li et al, 2017a;Li et al, 2017b;Li et al, 2017c;Zhao et al, 2019;Li et al, 2019), whereas the change trend of MIPS in the experimental group was opposite. Then, the differences between the experimental group and the control group were statistically analyzed (Table 2).…”

“…Proper clinical intervention in early stage of CE helps improve prognosis and prevent complications. Our previous researches illustrated that the MIPS method can achieve early identification of ICP rising and the pathophysiological changes of the brain ( Sun et al, 2019 ; Li et al, 2019 ; Zhao et al, 2019 ). Subsequent studies will focus on establishing the predicting model for CE based on advanced data processing and machine learning.…”

“…Thus, this experiment conduct animal models under controlled pathological conditions to better simulate the pathological progression of CE in a repeatable manner. Our previous researches had proved the validity of the CE model established by epidural freezing method (Kawai et al, 2003;Li et al, 2017a;Li et al, 2017b;Li et al, 2017c;Li et al, 2019). Rabbits were first anesthetized with pentobarbital (3%, 1 ml/kg) via ear vein.…”

Noninvasive real-time assessment of intracranial pressure after traumatic brain injury based on electromagnetic coupling phase sensing technology

“…where τ is the amplitude of the transmission coefficient, whose magnitude is equal to the modulus of T, and the phase of the transmission coefficient φ is the change between the phase of the transmission signal and the phase of the original incident signal, namely, the ECPS, whose size is related to the complex electrical impedance of tissues [10]. The electromagnetic properties of the main intracranial tissues, such as cerebrospinal fluid (CSF), cerebral blood flow (CBF), gray matter, white matter, and water, are quite different according to the reported study by C. Gabriel [11].…”

An amplitude-based characteristic parameter extraction algorithm for cerebral edema detection based on electromagnetic induction

“…At present, some studies have begun to use multi-frequency data combined with specific algorithms. Gen Li used multi-frequency reflection and transmission characteristics, combined with BP algorithm, to carry out brain edema monitoring [ 55 ]. Oziel also presented a single coil inductive device and the attendant algorithm for detection of changes in fluid/tissue ratio [ 52 ].…”