AI Medical Compendium Journal:
IEEE transactions on biomedical circuits and systems

Showing 81 to 90 of 132 articles

Application of Deep Compression Technique in Spiking Neural Network Chip.

IEEE transactions on biomedical circuits and systems
In this paper, a reconfigurable and scalable spiking neural network processor, containing 192 neurons and 6144 synapses, is developed. By using deep compression technique in spiking neural network chip, the amount of physical synapses can be reduced ...

Myoelectric Control of a Soft Hand Exoskeleton Using Kinematic Synergies.

IEEE transactions on biomedical circuits and systems
Soft hand exoskeletons offer a lightweight, low-profile alternative to rigid rehabilitative robotic systems, enabling their use to restore activities of daily living (ADL) in those with hand paresis due to stroke or other conditions. The hand exoskel...

Energy-Efficient Intelligent ECG Monitoring for Wearable Devices.

IEEE transactions on biomedical circuits and systems
Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart prob...

A Multilayer-Learning Current-Mode Neuromorphic System With Analog-Error Compensation.

IEEE transactions on biomedical circuits and systems
Internet-of-things applications that use machine-learning algorithms have increased the demand for application-specific energy-efficient hardware that can perform both learning and inference tasks to adapt to endpoint users or environmental changes. ...

Efficient Epileptic Seizure Prediction Based on Deep Learning.

IEEE transactions on biomedical circuits and systems
Epilepsy is one of the world's most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patients' life. In this paper, a novel patient-specific seizure prediction technique based on deep learning...

MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor With Stochastic Spike-Driven Online Learning.

IEEE transactions on biomedical circuits and systems
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-...

BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform With a Nine-Core Processor and BLE Connectivity.

IEEE transactions on biomedical circuits and systems
Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. N...

Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network.

IEEE transactions on biomedical circuits and systems
An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associate...

Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia.

IEEE transactions on biomedical circuits and systems
Accurate monitoring of the depth of anesthesia (DoA) is essential for intraoperative and postoperative patient's health. Commercially available electroencephalograph (EEG)-based DoA monitors are recommended only for certain anesthetic drugs and speci...

Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection.

IEEE transactions on biomedical circuits and systems
The paper proposes an innovative deep convolutional neural network (DCNN) combined with texture map for detecting cancerous regions and marking the ROI in a single model automatically. The proposed DCNN model contains two collaborative branches, name...