AIMC Topic: Signal Processing, Computer-Assisted

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Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

BioMed research international
Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectra...

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

Physiological measurement
Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. ...

Adaptive Neural Control of a Class of Output-Constrained Nonaffine Systems.

IEEE transactions on cybernetics
In this paper, we present a novel tracking controller for a class of uncertain nonaffine systems with time-varying asymmetric output constraints. Firstly, the original nonaffine constrained (in the sense of the output signal) control system is transf...

Rapid automated classification of anesthetic depth levels using GPU based parallelization of neural networks.

Journal of medical systems
The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is ...

Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

BioMed research international
The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagn...

Memristor-based multilayer neural networks with online gradient descent training.

IEEE transactions on neural networks and learning systems
Learning in multilayer neural networks (MNNs) relies on continuous updating of large matrices of synaptic weights by local rules. Such locality can be exploited for massive parallelism when implementing MNNs in hardware. However, these update rules r...

Autonomous unobtrusive detection of mild cognitive impairment in older adults.

IEEE transactions on bio-medical engineering
The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adu...

Construction of a fuzzy and Boolean logic gates based on DNA.

Small (Weinheim an der Bergstrasse, Germany)
Logic gates are devices that can perform logical operations by transforming a set of inputs into a predictable single detectable output. The hybridization properties, structure, and function of nucleic acids can be used to make DNA-based logic gates....

Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data.

IEEE transactions on cybernetics
Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classificat...

Kernel collaborative representation-based automatic seizure detection in intracranial EEG.

International journal of neural systems
Automatic seizure detection is of great significance in the monitoring and diagnosis of epilepsy. In this study, a novel method is proposed for automatic seizure detection in intracranial electroencephalogram (iEEG) recordings based on kernel collabo...