Publication
Epileptic seizure detection and monitoring is critical in healthcare, particularly for individuals requiring continuous oversight. Current methods, primarily based on electroencephalogram (EEG) technologies, face limitations due to their complexity in terms of acquisition, processing, comfort, and ease of use by the patient. Innovations in signal processing and machine learning have facilitated advances beyond traditional monitoring methods. In this context, an innovative approach that uses electrocardiogram (ECG) signals for seizure detection is introduced, offering a viable alternative to the complexities associated with EEG methodologies. This novel approach not only simplifies the monitoring process, but also significantly improves user comfort by avoiding the invasive nature of conventional EEG techniques.
In addition, the Multi-to-Single Knowledge Distillation (M2SKD) framework has been developed, aimed specifically at optimizing the balance between computational efficiency and diagnostic precision in wearable devices. This transition from a multi-biosignal to a single-biosignal model effectively mitigates the critical trade-offs between power consumption and algorithmic performance, crucial for the functionality of wearable technologies. This approach ensures that wearable systems maintain high accuracy levels, a fact substantiated by extensive simulations evaluating the framework's performance across various edge computing platforms.
Addressing the prevalent challenges of high memory and computational demands in neural network training within resource-constrained environments, the novel Bio-BPfree methodology is introduced. This innovative approach abandons the conventional backpropagation technique, which is not suitable for low-power environments, in favor of a specialized learning process tailored for wearable biomedical systems. By deploying a unique set of objective functions and leveraging multiple forward passes, this methodology significantly reduces memory and computational requirements. This makes it particularly well-suited for continuous health monitoring applications, where efficiency is paramount. The validation of this approach using multiple datasets not only underscores its effectiveness but also demonstrates its practical applicability in real-world scenarios, highlighting significant improvements in performance and efficiency.
The narrative then shifts towards Federated Learning (FL), focusing on its application in ensuring data privacy while maintaining the efficiency of seizure detection models. A decentralized FL framework is presented, specifically designed to tackle the challenges associated with non-independent and identically distributed (Non-IID) data across medical facilities. The framework incorporates adaptive ensemble learning and a strategic deployment phase, facilitating the creation of personalized, efficient, and privacy-compliant seizure detection models suitable for wearable technology.
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