RESUMEN
People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.
RESUMEN
Electrical activity recordings are critical for evaluating and understanding brain function. We present a novel wireless, implantable, and battery-free device, namely the Wireless Neurosensing System (WiNS), and for the first time, we evaluate multichannel recording capabilities in vivo. For a preliminary evaluation, we performed a benchtop experiment with emulated sinusoidal signals of varying amplitude and frequency, representative of neuronal activity. We later performed and analyzed electrocortical recordings in rats of evoked somatosensory activity in response to three paradigms: hind/fore limb and whisker stimulation. Wired recordings were used for comparison and validation of WiNS. We found that through the channel multiplexing element of WiNS, it is possible to perform multichannel recordings with a maximum sampling rate of â¼10 kHz for a total of eight channels. This sampling rate is appropriate for monitoring the full range of neuronal signals of interest, from low-frequency population recordings of electrocorticography and local field potentials to high-frequency individual neuronal spike recordings. These in vivo experiments demonstrated that the evoked neuronal activity recorded with WiNS is comparable to that recorded with a wired system under identical circumstances. Analysis of critical parameters for interpreting the somatosensory evoked activity showed no statistically significant difference between the parameters obtained by a wired system versus those obtained using WiNS. Therefore, WiNS can match the performance of more invasive recording systems. WiNS is a groundbreaking technology with potential applications throughout neuroscience as it offers a simple alternative to address the pitfalls of battery-powered neuronal implants.