Eis Annavini

• Nossum, Baard; Liverud, Anders Erik; Dapsance, Florian Albert; Annavini, Eis; Modi, Brijesh & Hafting, Torkel [Show all 8 contributors for this article] (2025). A Lightweight FPGA-Based Platform for Low-Power Neural Recording in Freely Moving Animals. In Redaktørkomite (Eds.), 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331586188. doi: 10.1109/embc58623.2025.11253988. Full text in Research Archive Show summary

  Understanding the neural mechanisms of sleep is critical for advancing treatments for neurodegenerative diseases. To investigate these mechanisms, long-term electrophysiological studies are conducted in animal models, where brain activity can be monitored continuously under controlled conditions. However, electrophysiological recordings from freely moving animals are typically performed using a tethered setup, which imposes constraints on natural behaviour. As a first step towards a miniaturised, wireless neural implant, we present a low-power system that enables direct control of the Intan RHD2132 electrophysiology chip using a custom FPGA-based stack. A light-weight yet robust implementation ensures efficient operation while maintaining the necessary flexibility for future integration into a compact, wireless system. The FPGA handles real-time signal processing and data transmission, with support for closed-loop stimulation and onboard data compression. Our system achieves continuous multi-channel recording at 20 kHz, with real-time wireless streaming and long-term data storage. The hardware design prioritises low power consumption, with the aim of enabling extended operation for uninterrupted sleep monitoring over 24 hoursClinical Relevance— This technology enables long-term, high-resolution sleep monitoring, which is essential for understanding the role of sleep in neurodegenerative disorders such as Alzheimer’s and Parkinson’s. By providing continuous, naturalistic brain recordings, this system may help identify early electrophysiological biomarkers of cognitive decline, with potential translational and therapeutics applications.

• Annavini, Eis; Quiceno, Felipe Duque & Boulland, Jean-Luc (2025). Wireless, Modular, and Implantable Sensor Platform for Real-time Health Monitoring. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). p. 1–7. doi: 10.1109/embc58623.2025.11252683. Full text in Research Archive Show summary

  Acquisition, processing, and visualization of data from various sensors are invaluable for teaching, biomedical research, diagnosis, and patient monitoring. Typically, each sensor system possesses its own dedicated and sometimes limited units for data logging and communication. To improve versatility, we introduce a platform capable of interfacing with a broad spectrum of external or implanted sensors, providing real-time signal processing and wireless transmission. The system's versatility stems from its modular design. This paper describes this platform, outlines the prototype's characteristics, and illustrates its potential application with a variety of physiological sensors. We further show that this compact device can be either used externally or as a subcutaneous implant after encasing it in the appropriate material.Clinical relevance- While this prototype is not intended to be a medical device, it has substantial clinical potential provided further development. It could offer a replacement for bulky and wired systems, thereby improving the working conditions of healthcare personnel. Furthermore, the in vivo animal experiments demonstrate the potential of this system for implantation, either as a short- to medium-term diagnostic device or possibly as a more permanent solution for continuous monitoring.

• Glott, Thomas; Wright, Daniel Nilsen; Nilsen, Ole Jacob; Annavini, Eis & Boulland, Jean-Luc (2025). Urodynamics with MEMS-Based pressure sensors, a clinical validation. Biosensors and Bioelectronics. ISSN 0956-5663. 278. doi: 10.1016/j.bios.2025.117320. Full text in Research Archive Show summary

  Cystometry is a urodynamic investigation used to examine the pressure-volume relationship during bladder filling. In clinical practice, cystometry is typically performed with water or air-filled catheters connected to an external transducer. These catheters, which are primarily suited for short-duration measurements, can compromise pressure transmission due to air bubbles, clogging, bending, and movements, and they are not sensitive enough to rapid pressure fluctuations. Moreover, ensuring proper leveling of the transducer is crucial for accurate measurement. To overcome these limitations, a MEMS pressure sensor prototype was previously developed. This study now evaluates the concurrent validity and reliability of this prototype against a conventional cystometry system during a series of clinical cases involving 33 patients undergoing routine cystometry. Excluding three sensors for which a correct placement during cystometry could not be verified, 83.3% of sensors produced useful data. Among these, 88% demonstrated a strong correlation with the reference system in the time-amplitude domain and 72% exhibited a high correlation in the time-frequency domain. Although these sensors were manually assembled and did not go through a standardized manufacturing process to enhance stability and detect potential failures, over 78.8% of the 33 sensors tested remained functional after the trial. Therefore, these results support the adoption of this technology in clinical practice, as it may improve diagnostic efficiency, reduce patient discomfort, and potentially be used for long-term and ambulatory urodynamics.

• Wright, Daniel Nilsen; Züchner, Mark; Annavini, Eis; Castillo, Manuel Jose Escalona; Teige, Lena Hammerlund & Tvedt, Lars Geir Whist [Show all 12 contributors for this article] (2024). From wires to waves, a novel sensor system for in vivo pressure monitoring. Scientific Reports. 14. doi: 10.1038/s41598-024-58019-5. Full text in Research Archive Show summary

  Pressure monitoring in various organs of the body is essential for appropriate diagnostic and therapeutic purposes. In almost all situations, monitoring is performed in a hospital setting. Technological advances not only promise to improve clinical pressure monitoring systems, but also engage toward the development of fully implantable systems in ambulatory patients. Such systems would not only provide longitudinal time monitoring to healthcare personnel, but also to the patient who could adjust their way-of-life in response to the measurements. In the past years, we have developed a new type of piezoresistive pressure sensor system. Different bench tests have demonstrated that it delivers precise and reliable pressure measurements in real-time. The potential of this system was confirmed by a continuous recording in a patient that lasted for almost a day. In the present study, we further characterized the functionality of this sensor system by conducting in vivo implantation experiments in nine female farm pigs. To get a step closer to a fully implantable system, we also adapted two different wireless communication solutions to the sensor system. The communication protocols are based on MICS (Medical Implant Communication System) and BLE (Bluetooth Low Energy) communication. As a proof-of-concept, implantation experiments in nine female pigs demonstrated the functionality of both systems, with a notable technical superiority of the BLE.

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• Korchynska, Solomiia; Signorelli, Letizia; Modi, Brijesh; Annavini, Eis; Okinina, Lina & Boccara, Charlotte (2024). MICROSTRUCTURE OF SLEEP AND SLEEP OSCILLATION DYNAMICS ACROSS DEVELOPMENT. Full text in Research Archive
• Modi, Brijesh; Annavini, Eis & Boccara, Charlotte (2023). Benchmarking algorithms to automatically detect sharp wave ripples. Full text in Research Archive
• Korchynska, Solomiia; Annavini, Eis; Modi, Brijesh; Vharegui, Luca & Boccara, Charlotte (2022). Sleep architecture dynamics across the lifespan. Full text in Research Archive
• Annavini, Eis; Korchynska, Solomiia; Babursah, Ela; Hafting, Torkel & Boccara, Charlotte (2022). A benchmark for sharp wave-ripple detection algorithms. Full text in Research Archive

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