Smart Patch for Non-Invasive Blood Pressure Monitoring in Epileptic Seizure Patients via the Sole

Murtadha Mohammed Mahdi; Sadik Kamel Gharghan; Saleem Lateef Mohammed; Ibrahim Amer Ibrahim

doi:10.51173/jt.v6i1.1624

Authors

Murtadha Mohammed Mahdi Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Sadik Kamel Gharghan Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Saleem Lateef Mohammed Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Ibrahim Amer Ibrahim Al Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq

DOI:

https://doi.org/10.51173/jt.v6i1.1624

Keywords:

Blood Pressure, Diastolic, Epileptic Seizure, Non-Invasive Photoplethysmography, Plethysmography, Smart Patch, Systolic

Abstract

Epileptic seizures can cause sudden blood pressure changes, requiring continuous monitoring. However, traditional blood pressure monitoring methods are often invasive and uncomfortable for the patient. In addition, it is difficult to measure blood pressure during seizures. This research aims to design a non-invasive, comfortable device to monitor blood pressure during epileptic seizures continuously. Photoplethysmography (PPG) signals from the sole of the patient's foot were used to extract blood pressure data. A smart patch was designed to be worn comfortably on foot for continuous monitoring during seizures. The results show that the average systolic and diastolic blood pressure errors were 2.838 and 4.494 mmHg during epileptic seizures, respectively. These blood pressure changes could be related to the onset of seizures, suggesting that the device and methodology could be combined with other measures to analyze and predict seizure activity. This research offers a non-invasive and comfortable solution for continuous blood pressure monitoring during seizures, which may affect seizure prediction and management.

Downloads

Download data is not yet available.

Author Biographies

Murtadha Mohammed Mahdi, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Sadik Kamel Gharghan, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Saleem Lateef Mohammed, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Ibrahim Amer Ibrahim, Al Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq

Biomedical Engineering Department

References

World Health Organization, ‘Epilepsy Fact Sheet,’ Mar. 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/epilepsy.

Epilepsy Foundation, ‘Epilepsy Overview,’ Oct. 2020. [Online]. Available: https://www.epilepsy.com/learn/about-epilepsy-basics/what-epilepsy.

A. Middleton, S. L. Fritz, and M. Lusardi, "Walking speed: the functional vital sign," J. Aging Phys. Act., vol. 23, no. 2, pp. 314-322, Apr. 2015, https://doi.org/10.1123/japa.2013-0236.

J. van Andel, C. Ungureanu, R. Aarts, F. Leijten, and J. Arends, "Using photoplethysmography in heart rate monitoring of patients with epilepsy," Epilepsy Behav., vol. 45, pp. 247-251, Mar. 2015, https://doi.org/10.1016/j.yebeh.2015.02.018.

M. Glasstetter, S. Böttcher, N. Zabler, N. Epitashvili, M. Dümpelmann, M. P. Richardson, and A. Schulze-Bonhage, "Identification of ictal tachycardia in focal motor- and non-motor seizures by means of a wearable PPG sensor," Sensors, vol. 21, no. 18, article no. 6017, Sep. 2021, https://doi.org/10.3390/s21186017.

R. El Atrache, E. Tamilia, F. Mohammadpour Touserkani, S. Hammond, C. Papadelis, K. Kapur, M. Jackson, B. Bucciarelli, M. Tsuboyama, R. A. Sarkis, and T. Loddenkemper, "Photoplethysmography: a measure for the function of the autonomic nervous system in focal impaired awareness seizures," Epilepsia, vol. 61, no. 8, pp. 1617-1626, Aug. 2020, https://doi.org/10.1111/epi.16621.

A. Chittala, T. Bhupathi, D. P. Alakunta and N. K. Punna, "Machine Learning and IoT Based EEG Signal Classification for Epileptic Seizures Detection," 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, 2021, pp. 531-537, https://doi.org/10.1109/RTEICT52294.2021.9573727.

S. P. Shashikumar, A. J. Shah, Q. Li, G. D. Clifford and S. Nemati, "A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology," 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA, 2017, pp. 141-144, https://doi.org/10.1109/BHI.2017.7897225.

Epilepsy Society, ‘Recovery after a seizure,’ [Online]. Available: https://www.epilepsysociety.org.uk/recovery-after-seizure#.YjvNKi1Q3n0.

J. -L. Song, Q. Li, B. Zhang, M. B. Westover and R. Zhang, "A New Neural Mass Model Driven Method and Its Application in Early Epileptic Seizure Detection," in IEEE Transactions on Biomedical Engineering, vol. 67, no. 8, pp. 2194-2205, Aug. 2020, https://doi.org/10.1109/TBME.2019.2957392.

I. A. Ibrahim, J. Santhosh and M. Moghavvemi, "A new approach for an effective eye movement artifact elimination from EEG signal," 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia, 2015, pp. 557-562, https://doi.org/10.1109/ICSIPA.2015.7412253.

H. N. Lemus and R. A. Sarkis, "Epilepsy care in nursing facilities: Knowledge gaps and opportunities," Epilepsy Behav., vol. 138, article no. 108997, 2023, https://doi.org/10.1080/14740338.2022.2023128.

B. N. Blond, K. Detyniecki, and L. J. Hirsch, "Assessment of treatment side effects and quality of life in people with epilepsy," Neurologic Clinics, vol. 34, no. 2, pp. 395-410, May 2016, https://doi.org/10.1016/j.ncl.2015.11.002.

Epilepsy Foundation, ‘Epilepsy in the United States,’ Oct. 2020. [Online]. Available: https://www.epilepsy.com/learn/about-epilepsy-basics/epilepsy-united-states.

S. M. Krishna, J. V. Moxon, and J. Golledge, "A Review of the Pathophysiology and Potential Biomarkers for Peripheral Artery Disease," Int. J. Mol. Sci., vol. 16, no. 5, pp. 11294-11322, 2015. doi: 10.3390/ijms160511294.

M. J. Brodie and P. Kwan, "Epilepsy in elderly people," BMJ, vol. 331, no. 7528, pp. 1317-1322, Dec. 2005. doi: 10.1136/bmj.331.7528.1317.

A. Goshvarpour and A. Goshvarpour, "The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features," Phys. Eng. Sci. Med., vol. 43, pp. 119-134, 2020. doi: 10.1007/s13246-019-00825-7.

A. A. Asadi-Pooya, M. Emami, N. Ashjazadeh, A. Nikseresht, A. Shariat, P. Petramfar, G. Yousefipour, A. Borhani-Haghighi, S. Izadi, and A. Rahimi-Jaberi, "Reasons for uncontrolled seizures in adults; the impact of pseudointractability," Seizure, vol. 22, no. 4, pp. 271-274, 2013. doi: 10.1016/j.seizure.2013.01.010.

Patel S, Hu S, Sahu S, Patel M. A review on photoplethysmography and its emerging applications. Int J Biosens Bioelectron. 2019;5:197-202. doi:10.11648/j.ijbb.20190506.17.

Q. Wang, W. Zeng, and X. Dai, "Gait classification for early detection and severity rating of Parkinson's disease based on hybrid signal processing and machine learning methods," Cogn. Neurodyn., 2022. doi: 10.1007/s11571-022-09925-9.

K. S. -K. Ma, H. Hao, H. -C. Huang and Y. -H. Tang, "Entropy-Facilitated Machine Learning for Blood Pressure Estimation Using Electrocardiogram and Photoplethysmogram in a Wearable Device," 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 2021, pp. 1-6, doi: 10.1109/CISP-BMEI53629.2021.9624370.

A. Jubran, “Pulse oximetry,” Intensive Care Med., vol. 30, no. 11, pp. 2017–2020, 2004, doi: 10.1007/s00134-004-2399-x.

L. Hussain, "Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach," Cogn. Neurodyn., vol. 12, no. 3, pp. 271-294, Jun. 2018. doi: 10.1007/s11571-018-9477-1.

A. Souri, M. Y. Ghafour, A. M. Ahmed, F. Safara, A. Yamini, and M. Hoseyninezhad, "A new machine learning-based healthcare monitoring model for student's condition diagnosis in Internet of Things environment," Soft Computing, vol. 24, pp. 17111-17121, 2020. doi: 10.1007/s00500-020-05003-6.

Huang CM, Chan GSH, Ting CK. A comprehensive review of non-invasive blood pressure monitoring techniques. J Clin Monit Comput. 2019 Dec;33(6):901-913. doi: 10.1007/s10877-019-00348-w.

A. Jahanbekam, J. Baumann, R. D. Nass, C. Bauckhage, H. Hill, C. E. Elger, and R. Surges, "Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions," Epilepsia Open, vol. 6, no. 4, pp. 656-663, 2021. doi: 10.1002/epi4.12520.

T. Wibmer, C. Denner, C. Fischer, B. Schildge, S. Rüdiger, C. Kropf-Sanchen, W. Rottbauer, and C. Schumann, "Blood pressure monitoring during exercise: comparison of pulse transit time and volume clamp methods," Blood Pressure, vol. 24, no. 6, pp. 353-360, Nov. 2015. doi: 10.3109/08037051.2015.1053253.

K. Budidha and P. A. Kyriacou, "Photoplethysmography for quantitative assessment of sympathetic nerve activity (SNA) during cold stress," Front. Physiol., vol. 9, article no. 1863, Jan. 2019. doi: 10.3389/fphys.2018.01863.

F. Attivissimo, L. De Palma, A. Di Nisio, M. Scarpetta, and A. M. L. Lanzolla, "Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring," Sensors, vol. 23, no. 4, article no. 2321, 2023. doi: 10.3390/s23042321.

R. Mukkamala, D. H. Mathews, Y. Gao, M. P. Purnell and J. R. Hahn, ‘Noninvasive blood pressure monitoring using pulse transit time,’ in Proceedings of the IEEE, vol. 97, no. 3, pp. 390-403, March 2009. DOI: 10.1109/TBME.2018.2865751

S. Z. Hassanpour and M. R. Ayatollahi, ‘Blood pressure estimation using photoplethysmography and pulse transit time,’ in IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 2, pp. 275-282, Feb. 2014. DOI 10.1088/1361-6579/ab1f17

M. Simjanoska, M. Gjoreski, M. Gams, and A. M. Bogdanova, "Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques," Sensors, vol. 18, no. 4, article no. 1160, 2018. doi: 10.3390/s18041160.

G. Wang, M. Atef and Y. Lian, "Towards a Continuous Non-Invasive Cuffless Blood Pressure Monitoring System Using PPG: Systems and Circuits Review," in IEEE Circuits and Systems Magazine, vol. 18, no. 3, pp. 6-26, thirdquarter 2018, doi: 10.1109/MCAS.2018.2849261.

Xie, J., Chen, Y., Gong, E., Liu, J., Li, L., Sun, H., & Wang, H., A wearable cuffless blood pressure monitoring system using photoplethysmography and electrocardiogram signals. Journal of medical systems, 42(4), 68, 2018, doi: 10.1007/s10916-018-0916-y.

Zhang, Y., Feng, X., Zhu, X., Liu, Y., & Wang, X., A wearable cuffless blood pressure monitoring system using a single photoplethysmography sensor and convolutional neural network. Journal of healthcare engineering, 2021, 1-11. doi: 10.1155/2021/66.

Kim, D. H., Lee, D. Y., Kim, D. H., Lee, J. W., & Kim, J. H., Continuous blood pressure monitoring using pulse wave analysis and photoplethysmography. Scientific reports, 9(1), 1-8, 2019, doi: 10.1038/s41598-019-43147-8.

Shahin, S., Haghi, M., & Ward, R., Continuous non-invasive blood pressure monitoring using photoplethysmography and machine learning. Scientific reports, 10(1), 1-10, 2020, doi: 10.1038/s41598-020-64292-2.

G. Thambiraj, U. Gandhi, V. Devanand, and U. Mangalanathan, "Noninvasive cuffless blood pressure estimation using pulse transit time, Womersley number, and photoplethysmogram intensity ratio," Physiol. Meas., vol. 40, no. 7, article no. 075001, Jul. 2019. doi: 10.1088/1361-6579/ab1f17.

Nordic Semiconductor, "NRF2401 datasheet," 2021. [Online]. Available: https://www.nordicsemi.com/Products/Low-power-short-range-wireless/nRF24-series/nRF2401.

Wiki Company, "Ear-Loop PPG Sensor for Arduino," 2021. [Online]. Available: https://www.wiki-company.com/index.php?title=Ear-Loop_PPG_Sensor_for_Arduino.

Adafruit Industries. (2021). 3.2" TFT LCD Display. https://www.adafruit.com/product/1480.

SparkFun Electronics, "MP3 Player Shield Hookup Guide," 2021. [Online]. Available: https://learn.sparkfun.com/tutorials/mp3-player-shield-hookup-guide/all#introduction.

Y. Xu, M. Luo, T. Li, and G. Song, "ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold," Sensors, vol. 17, no. 12, article no. 2754, 2017. doi: 10.3390/s17122754.