In addition, early diagnosis requires collection of patient's physiological information as soon as possible. Thus, patients need extra referral for imaging, which increases the difficulty and procedures of nursing, especially for severe cases. On the other hand, most imaging equipment is usually located in the radiology department.
Also, CT scans are based on radiation, so there is difficulty in performing repeat scans on children. The cost of CT or MRI scan is relatively expensive and these devices require expert operators. The current gold standard for the diagnosis of cerebral edema is mainly based on imaging methods, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Effective cerebral edema monitoring is still necessary which will help medical staff to adjust the therapeutic schedule in time. It has been reported that effective monitoring methods and timely intervention can help improve the prognosis. At this stage, it is characterized by extracellular accumulation of fluid resulting from disruption of the blood–brain barrier (BBB) and extravasations of serum proteins. Several hours after the onset of cerebral edema, intracranial changes gradually become dominated by vasogenic cerebral edema. In the early stage of cerebral edema, it is mainly cytotoxic edema characterized by intracellular accumulation of fluid and Na + resulting in cell swelling. Pathologically, cerebral edema can be divided into two subtypes: cytotoxic and vasogenic cerebral edema. Cerebral edema has been associated with high morbidity, high mortality, and high disability. Combined with this algorithm, the electromagnetic induction method has an excellent performance on the detection and monitoring of cerebral edema.Ĭerebral edema, which can be defined as the abnormal increase and accumulation of intracranial fluid, is a common condition secondary to stroke and traumatic brain injury (TBI). The proposed Ab-CPE algorithm is suitable for multi-frequency characteristic analysis. Moreover, the Ab-CPE algorithm was able to distinguish between acute and chronic phase of cerebral edema with a sensitivity of 85.0% and specificity of 91.0%. Also, in the early stage, it can detect cerebral edema with a sensitivity of 85.0% and specificity of 87.5%. The proposed Ab-CPE algorithm was able to detect cerebral edema with a sensitivity of 94.1% and specificity of 95.4%. Fourteen rabbits were enrolled to establish cerebral edema model and the 24 h real-time monitoring experiments were carried out for algorithm verification. We proposed an Amplitude-based Characteristic Parameter Extraction (Ab-CPE) algorithm for multi-frequency characteristic analysis over the frequency range of 1–100 MHz and investigated its performance in electromagnetic induction-based cerebral edema detection and distinction of its acute/chronic phase. Measurement of amplitude data over the frequency range of 1–100 MHz is conducted to evaluate the changes in cerebral edema. In this article, a flexible conformal electromagnetic two-coil sensor was employed as the electromagnetic induction sensor, associated with a vector network analyzer (VNA) for signal generation and receiving. The early diagnosis and monitoring of cerebral edema is of great importance to improve the prognosis. Cerebral edema is a common condition secondary to any type of neurological injury.
0 kommentar(er)
