Hardware-efficient classification is essential for applications such as medical implants, wearables, and IoT devices, with severe energy and resources ...
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Abstract—Hardware-efficient classification is essential for ap- plications such as medical implants, wearables, and IoT devices,.
In this paper, we propose a compact and hardware-efficient one-dimensional convolutional neural network (1D CNN) structure for patient-specific early seizure ...
Jun 19, 2024 · We present an energy-efficient seizure detection approach involving a TC-ResNet and time- series analysis which is suitable for low-power edge ...
Jun 19, 2024 · We present an energy-efficient seizure detection approach involving a TC-ResNet and time-series analysis which is suitable for low-power edge ...
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Jan 10, 2024 · Spiking neural networks (SNNs) are modeled on biological neurons and are energy-efficient on neuromorphic hardware, which can be expected to ...
Phase synchronization phenomenon of two distant neuron populations for a short period of time just prior to a seizure episode is utilized for such prediction.
Abstract—This paper presents design, implementation and evaluation of an efficient embedded hardware for accurate automated detection of epileptic seizures.
In this paper, we demonstrate how spiking neural networks can achieve high performance in difficult cross-patient seizure detection settings, exceeding existing ...
We propose a cascaded two-stage seizure detection algorithm that is computationally efficient (resulting in a low-power hardware implementation) without ...