Non-Invasive Early Detection of Brain Aneurysm Symptoms by Fiber Bragg Grating Sensors

Abstract

Brain Aneurysms are a critical cerebrovascular condition where there is bulging of the arterial blood vessels, with the rupture adding to the mortality rates up to 50%. Early detection remains challenging as traditional diagnostic methods, such as MRI and CT Angiography, are invasive, expensive, and often inaccessible for the earliest screening. This study presents a novel approach using Fiber Bragg Grating (FBG) Optical sensor technology for the non–invasive, earliest detection of Brain Aneurysms identified via analysis of arterial pulse waveforms. The proposed system utilizes an FBG sensor fabricated using the phase mask technique and is mechanically placed in the temple region to detect subtle changes in pulse wave characteristics, which could potentially indicate a Brain aneurysm. The FBG sensor-based approach offers numerous advantages over conventional methods, such as immunity to electromagnetic interference, high bandwidth and strain sensitivity, light weight, compactness, low security risk, and the capability for continuous real-time monitoring. Initial results provide the system’s ability to detect minute pulse wave variations, which may indicate the unruptured aneurysms, potentially enabling earlier detection through a non-invasive and optical fiber technology-based approach.

Introduction

• Fiber Bragg Grating (FBG) sensors are emerging as a non-invasive alternative for real-time monitoring of temporal arterial pulse waves, offering higher sensitivity and accuracy than traditional methods.

• Brain aneurysms, particularly intracranial aneurysms (IA), result from bulging of weakened cerebral artery walls, potentially leading to hemorrhagic stroke.

• Turbulent blood flow, mechanical stress, and endothelial dysfunction contribute to aneurysm development. Risk factors include smoking, alcohol, hypertension, and genetic conditions.

• Women have been identified as more prone to unruptured aneurysms and de novo aneurysm growth.

2. Related Work and Existing Methods

• Current Detection Techniques:

  • MRI: Effective but unsuitable for patients with implants or claustrophobia.

  • CTA/MRA/DSA: Common but expensive, invasive, and involve radiation exposure.

  • 3D Catheter Angiography: Invasive and costly, with risks like renal effects and artery injury.

  • Surgical options: Clipping, coiling, and flow diversion, all with high risk.

• New Devices under research include:

  • Flow disruptors, endovascular clip systems, and PulseRider.

• Despite advancements, there's a lack of a truly patient-friendly, affordable, non-invasive device for early aneurysm detection.

3. Proposed Method: FBG-Based Headband Device

A. System Overview

• FBG Sensors:

  • Detect strain and pressure via wavelength shifts caused by arterial pulse waves.

  • Operate based on Bragg reflection principles.

• Sensor Placement:

  • Integrated into a lightweight headband positioned on the temple, where the skull is thinner and arteries are near the skin surface.

• Workflow:

  1. Sensor Selection: FBG chosen for its high sensitivity.

  2. Headband Design: Medical-grade materials used.

  3. Signal Processing:

     • Optical signals converted to digital via an interrogator.

     • Noise filtering and signal analysis using tools like OriginPro.

  4. Feature Extraction:

     • Detects waveform characteristics such as pulse amplitude, rise/fall time, dicrotic notch, etc.

     • Abnormal waveforms suggest aneurysmal risk.

B. Methodology and Experimental Setup

• Simulation:

  • Two datasets generated:

    • Normal waveform: ~1.2 Hz, high amplitude.

    • Aneurysm-prone: ~0.9 Hz, flat and low amplitude.

  • Parameters simulated: vessel strain, intracranial pressure (ICP), and pulse wave velocity.

• Data Processing:

  • Applied Savitzky–Golay filtering to preserve waveform peaks.

  • Extracted features: skewness, kurtosis, amplitude, and pulse wave velocity.

  • C-band FBG sensors (1530–1560 nm) created using phase mask technique.

  • Connected to Si–155 interrogator for real-time signal acquisition.

  • Data captured at 1 kHz sampling rate and analyzed using OriginPro.

• Calibration:

  • Used a digital stethoscope as reference.

  • Data exported in CSV format for post-processing.

4. Results & Discussion

• Normal vs Aneurysm-Prone Signal Characteristics:

  • Normal Pulse: High kurtosis, low skewness, fast rise time, and high amplitude.

  • Aneurysm-Prone Pulse: Flattened waveform, low amplitude, and slow rise time.

• Figures & Graphs:

  • Include synthetic data graphs and real signal waveforms comparing healthy vs pathological patterns.

Conclusion

This study shows that the FBG sensors, when integrated in the headband configuration, which is non-invasive, can detect the physiological parameters relevant to the brain aneurysm symptoms. The simulated waveforms and the analysis of results obtained show that there is a change in the waveform features, such as rise time, strain amplitude, and waveform asymmetry describe the differentiation between normal and aneurysm-prone conditions Through the strategic placement of FBG sensors over the superficial temporal artery, the system can capture critical physiological parameters such as pulse waveform morphology, cranial strain, and vascular compliance. The use of optical sensing makes the system immune to electromagnetic interference, lightweight, and suitable for integration into wearable devices like a headband. The combination of Python and OriginPro allows for real-time data logging, visualization, and signal analysis, making the system adaptable for both research and future clinical environments. By generating and analysing synthetic physiological waveforms, the system\'s capacity to detect abnormal patterns associated with aneurysmal conditions was validated at the simulation level. Although clinical testing has not yet been performed, the system offers a proof-of-concept that non-invasive FBG-based monitoring could serve as an early-warning tool for high-risk individuals. The proposal also illustrates the feasibility of developing scalable, modular, and real-time monitoring tools that align with current trends in smart healthcare and personalized diagnostics.

References

[1] K. Chethana, Akshay S, Swetha K, S. Malathi, A.S. Guru Prasad, Monitoring of heartbeat and breathing parameters with optical sensor using software tool, Optics & Laser Technology, Volume 180,2025,111552, ISSN 0030-3992, [2] Tataranu LG, Munteanu O, Kamel A, Gheorghita KL, Rizea RE. Advancements in Brain Aneurysm Management: Integrating Neuroanatomy, Physiopathology, and Neurosurgical Techniques. Medicina (Kaunas). 2024 Nov 6;60(11):1820. doi: 10.3390/medicina60111820. PMID: 39597005; PMCID: PMC11596862. [3] Brisman JL, Song JK, Newell DW. Cerebral aneurysms. N Engl J Med. 2006 Aug 31;355(9):928-39. doi: 10.1056/NEJMra052760. PMID: 16943405. [4] J. P. Carmo, A. M. F. da Silva, R. P. Rocha and J. H. Correia, \"Application of Fiber Bragg Gratings to Wearable Garments,\" in IEEE Sensors Journal, vol. 12, no. 1, pp. 261-266, Jan. 2012, doi: 10.1109/JSEN.2011.2161281. [5] Krzysztof Bartnik, Marcin Koba, Mateusz ?mietana, Advancements in optical fiber sensors for in vivo applications – A review of sensors tested on living organisms, Measurement, Volume 24, 2024, 113818, ISSN 0263-2241https://doi.org/10.1016/j.measurement.2023.113818. [6] Roriz P, Frazão O, Lobo-Ribeiro AB, Santos JL,Simões JA. Review of Fiber-Optic Pressure Sensors for Biomedical and Biomechanical Applications. J Biomed Opt. 2013May;18(5):50903. doi: 10.1117/1.JBO.18.5.050903. PMID: 23722494.. [7] Katayama, Kyoko & Chino, Shun & Kurasawa, Shintaro & Koyama, Shouhei & Ishizawa, Hiroaki & Fujimoto, Keisaku. (2019). Classification of Pulse Wave Signal Measured by FBG Sensor for Vascular Age and Arteriosclerosis Estimation. IEEE Sensors Journal. PP. 1-1. 10.1109/JSEN.2019.2952833. [8] Mendez, Alexis. (2016). Fiber Bragg grating sensors for biomedical applications. BM4B.1. 10.1364/BGPP.2016.BM4B.1M. Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin, “High resolution fiber distributed measurements with coherent OFDR,” in Proc. ECOC’00, 2000, paper 11.3.4, p. 109. [9] Issatayeva, A.; Beisenova, A.; Tosi, D.; Molardi, C. Fiber-Optic Based Smart Textiles for Real-Time Monitoring of Breathing Rate. Sensors 2020, 20, 3408. https://doi.org/10.3390/s20123408 [10] Padma S, Umesh S, Srinivas T, Asokan S. Carotid Arterial Pulse Waveform Measurements Using Fiber Bragg Grating Pulse Probe. IEEE J Biomed Health Inform. 2018 Sep;22(5):1415-1420. doi: 10.1109/JBHI.2017.2765701. Epub 2017 Oct 23. PMID: 29990008. [11] Massaroni, Carlo & Zaltieri, Martina & Presti, Daniela & Nicolò, Andrea & Tosi, Daniele & Schena, Emiliano. (2020). Fiber Bragg Grating Sensors for Cardiorespiratory Monitoring: A Review. IEEE Sensors Journal. PP. 1-1. 10.1109/JSEN.2020.2988692 [12] Din M, Agarwal S, Grzeda M, Wood DA, Modat M, Booth TC. Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. J Neurointerv Surg. 2023 Mar;15(3):262-271. doi: 10.1136/jnis-2022-019456. Epub 2022 Nov 14. PMID: 36375834; PMCID: PMC9985742. “PDCA12-70 data sheet,” Opto Speed SA, Mezzovico, Switzerland. [13] K. Katayama, S. Chino, S. Kurasawa, S. Koyama, H. Ishizawa, and K. Fujimoto, \"Classification of Pulse Wave Signal Measured by FBG Sensor for Vascular Age and Arteriosclerosis Estimation,\" in IEEE Sensors Journal, vol. 20, no. 5, pp. 2485-2491, 1 March 2020, doi: 10.1109/JSEN.2019.2952833. [14] Ho, Siu Chun Michael, et al. \"Fiber Bragg grating-based arterial localization device.\" Smart Materials and Structures 26.6 (2017): 065020. [15] Presti, Daniela Lo, et al. \"Wearable system based on flexible FBG for respiratory and cardiac monitoring.\" IEEE Sensors Journal 19.17 (2019): 7391-7398. [16] Leitão C, Ribau V, Afreixo V, Antunes P, André P, Pinto JL, Boutouyrie P, Laurent S, Bastos JM. Clinical evaluation of an optical fiber-based probe for the assessment of central arterial pulse waves. Hypertens Res. 2018 Nov;41(11):904-912. Doi: 10.1038/s41440-018-0089-2. Epub 2018 Aug 28. PMID: 30154504 [17] F. Zhang et al., \"Wearable Fiber Bragg Grating Sensors for Physiological Signal and Body Motion Monitoring: A Review,\" in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-24, 2025, Art. no. 4008424, doi: 10.1109/TIM.2025.3558819. [18] Zhang, Xuhui & Wang, Chunyang & Zheng, Tong & Wu, Haibin & Wu, Qing & Wang, Yunzheng. (2023). Wearable Optical Fiber Sensors in Medical Monitoring Applications: A Review. Sensors. 23. 10.3390/s23156671. [19] Theodosiou A. Recent Advances in Fiber Bragg Grating Sensing. Sensors (Basel). 2024 Jan 15;24(2):532. Doi: 10.3390/s24020532. PMID: 38257625; PMCID: PMC10819933. [20] Kashyap, Raman. Fiber Bragg gratings. Academic Press, 2009. [21] Hill, K. O., and G. Meltz. \"Fiber bragg grating technology fundamentals and overview. Lightwave Technology.\" Journal of 15.8 (1997): 1263-1276. [22] Haseda Y, Bonefacino J, Tam HY, Chino S, Koyama S, Ishizawa H. Measurement of Pulse Wave Signals and Blood Pressure by a Plastic Optical Fiber FBG Sensor. Sensors (Basel). 2019 Nov 21;19(23):5088. Doi: 10.3390/s19235088. PMID: 31766391; PMCID: PMC6928766 [23] Nandi, Somesh & Chethana, K. & Talabattula, Srinivas & Sharan, Dr. (2024). Investigation on FBG based optical sensor for pressure and temperature measurement in civil application. Optoelectronics Letters. 20. 531-536. 10.1007/s11801-024-3190-6.

Copyright

Copyright © 2025 Banashree K S, Chethana K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.