Implantable Intracranial Pressure Sensor with Continuous Bluetooth Transmission via Mobile Application

Abstract

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.

Low-cost electrocardiogram monitoring system for elderly people using LabVIEW

“…Furthermore, the shortage of human resources in the MINSA (Peruvian Ministry of Health) is worsened in the poorest regions, where the lack of personnel prevents timely care [8]. This is why the use of low-cost technologies can contribute to In the review of the literature, studies have been found that focus on designing a low-cost and easyto-use electrocardiogram (ECG), focusing on capturing cardiac electrical signals, filtering noise, and transmitting them [9], adding in some cases Bluetooth technology to facilitate the early diagnosis of cardiac diseases [10]- [12] while other authors [13]- [15] present an approach on the design of a simulator of electrocardiographic signals, highlighting the reduction of cost by converting analog to digital signals. On the other hand, there have been significant advances in the monitoring and detection of cardiac arrhythmias [16], [17] through the development of a portable ECG device [18], data transmission through artificial intelligence while other research [19] presented a prototype based on machine learning for the detection and classification of arrhythmias in low-cost ECG devices with MATLAB and PSoC4 [20], [21].…”