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

Showing 101 to 110 of 132 articles

Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors.

IEEE transactions on biomedical circuits and systems
Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. The...

A New Method for Automatic Sleep Stage Classification.

IEEE transactions on biomedical circuits and systems
Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for autom...

On Multiple AER Handshaking Channels Over High-Speed Bit-Serial Bidirectional LVDS Links With Flow-Control and Clock-Correction on Commercial FPGAs for Scalable Neuromorphic Systems.

IEEE transactions on biomedical circuits and systems
Address event representation (AER) is a widely employed asynchronous technique for interchanging "neural spikes" between different hardware elements in neuromorphic systems. Each neuron or cell in a chip or a system is assigned an address (or ID), wh...

Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

IEEE transactions on biomedical circuits and systems
We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework...

HTM Spatial Pooler With Memristor Crossbar Circuits for Sparse Biometric Recognition.

IEEE transactions on biomedical circuits and systems
Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and tempora...

A Hybrid CMOS-Memristor Neuromorphic Synapse.

IEEE transactions on biomedical circuits and systems
Although data processing technology continues to advance at an astonishing rate, computers with brain-like processing capabilities still elude us. It is envisioned that such computers may be achieved by the fusion of neuroscience and nano-electronics...

Deep Neural Networks for Identifying Cough Sounds.

IEEE transactions on biomedical circuits and systems
In this paper, we consider two different approaches of using deep neural networks for cough detection. The cough detection task is cast as a visual recognition problem and as a sequence-to-sequence labeling problem. A convolutional neural network and...

Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System.

IEEE transactions on biomedical circuits and systems
We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a ge...

Improving Detection Accuracy of Memristor-Based Bio-Signal Sensing Platform.

IEEE transactions on biomedical circuits and systems
Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive d...

Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks.

IEEE transactions on biomedical circuits and systems
We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon h...