Background: Using machine learning to combine wrist accelerometer (ACM) and
electrodermal activity (EDA) has been shown effective to detect primarily and secondarily …

Abstract Purpose of Review Machine Learning (ML) and Artificial Intelligence (AI) are data-
driven techniques to translate raw data into applicable and interpretable insights that can …

Introduction Wearable devices for continuous seizure monitoring have drawn increasing
attention in the field of epilepsy research. One of the parameters recorded by these devices …

Objective Wrist‐or ankle‐worn devices are less intrusive than the widely used
electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom …

Neurological conditions are the leading cause of disability and mortality combined,
demanding innovative, scalable, and sustainable solutions. Brain health has become a …

Objective There is a pressing need for reliable automated seizure detection in epilepsy care.
Performance evidence on ambulatory non‐electroencephalography‐based seizure …

Epilepsy is one of the significant neurological disorders affecting nearly 65 million people
worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were …

Seizure detection from EEG signals is crucial for diagnosing and treating neurological
disorders. However, accurately detecting seizures is challenging due to the complexity and …

Wearable devices have received significant attention recently for their ability to monitor
critical physiological signals noninvasively, such as electrocardiography …

This review article explores the transformative advancements in wearable biosignal sensors
powered by machine learning, focusing on four notable biosignals: electrocardiogram …