Abstract The vast majority of studies that process and analyze neural signals are conducted on cloud computing resources, which is often necessary for the demanding requirements deep network workloads. However, applications such as epileptic seizure detection stand to benefit from edge devices can securely sensitive medical data in a real-time personalised manner. In this work, we propose novel...

Epileptic seizures are commonly studied using the electroencephalogram (EEG) for the measurement of brain electrical signals, which often show characteristic seizure waveforms. The automatic extraction of this seizure waveform from the recorded data has many implications, including the possibility of a real time system to warn the patient of an impending seizure. Such detection has hitherto pro...

Background: Many investigations based on nonlinear methods have been carried out for the research of seizure detection. However, some of these nonlinear measures cannot achieve satisfying performance without considering the basic rhythms of epileptic EEGs. New method: To overcome the defects, this paper proposed a framework on wavelet-based nonlinear features and extreme learning machine (ELM) ...

The implantable closed-loop system for epileptic seizure detection and neuromodulation is getting more attention in recent years. The architecture design for seizure signal sensing and analyzing has been proposed, but the implementation of the classifier for unsupervised seizure detection is still strongly desired. The k-nearest neighbor (KNN) classification algorithm is one commonly used class...

Let g ∈ Q be not −1 or a square. Let Pg denote the set of primes p such that g is a primitive root mod p. Let 1 ≤ a ≤ f, (a, f) = 1. Under the Generalized Riemann Hypothesis (GRH) it can be shown that the set of primes p ∈ Pg with p ≡ a(mod f) has a natural density. In this note this density is explicitly evaluated. This generalizes a classical result of Hooley.

This paper describes the design and test results of a 3-stage automated system for neonatal EEG seizure detection. Stage I of the system is the initial detection stage, and identifies overlapping 5-s segments of suspected seizure activity in each EEG channel. In Stage II, the detected segments from Stage I are spatiotemporally clustered to produce multi-channel candidate seizures. In Stage III,...

In recent years, an increasing number of studies have investigated the effects of closed-loop anti-epileptic treatments. Most of the current research still is very labour intensive: real-time treatment is manually triggered and conclusions can only be drawn after multiple days of manual review and annotation of the electroencephalogram (EEG). In this paper we propose a technique based on reserv...