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In this chapter, we analyze and compare various hardware-based epileptic seizure systems and discuss the challenges and opportunities for reducing power consumption and increasing the battery lifetime. Furthermore, we propose a power management model that employs patient-specific seizure patterns to manage the power consumption of the overall system. This model determines the patient-specific seizure pattern and switches the system to sleep mode when the likelihood of seizure occurrence is zero or very low. Our analysis shows that this power management model could effectively reduce the power consumption by 49% compared to the complex model, while the performance reduction is less than 1%.

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A Self-Aware Power Management Model for Epileptic Seizure Systems Based on Patient-Specific Daily Seizure Pattern

  • 章节

  • 发表日期：

    2025年

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  • Shiva Maleki Varnosfaderani,
  • Nabil J. Sarhan,
  • Mohammad Alhawari

摘要

In this chapter, we analyze and compare various hardware-based epileptic seizure systems and discuss the challenges and opportunities for reducing power consumption and increasing the battery lifetime. Furthermore, we propose a power management model that employs patient-specific seizure patterns to manage the power consumption of the overall system. This model determines the patient-specific seizure pattern and switches the system to sleep mode when the likelihood of seizure occurrence is zero or very low. Our analysis shows that this power management model could effectively reduce the power consumption by 49% compared to the complex model, while the performance reduction is less than 1%.