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

Showing 1 to 10 of 132 articles

An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices.

IEEE transactions on biomedical circuits and systems
Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring. However, most of them have relatively low energy efficiency, and lack configurability in classification leads number and inference algorithm models. A multi-lead EC...

Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.

IEEE transactions on biomedical circuits and systems
This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have ga...

Scalable Multi-FPGA HPC Architecture for Associative Memory System.

IEEE transactions on biomedical circuits and systems
Associative memory is a cornerstone of cognitive intelligence within the human brain. The Bayesian confidence propagation neural network (BCPNN), a cortex-inspired model with high biological plausibility, has proven effective in emulating high-level ...

RRAM-Based Spiking Neural Network With Target-Modulated Spike-Timing-Dependent Plasticity.

IEEE transactions on biomedical circuits and systems
The spiking neural network (SNN) training with spike timing-dependent plasticity (STDP) for image classification usually requires a lot of neurons to extract representative features and(or) needs an external classifier. Conventional bio-inspired lear...

High-Performance Method and Architecture for Attention Computation in DNN Inference.

IEEE transactions on biomedical circuits and systems
In recent years, The combination of Attention mechanism and deep learning has a wide range of applications in the field of medical imaging. However, due to its complex computational processes, existing hardware architectures have high resource consum...

BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.

IEEE transactions on biomedical circuits and systems
Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the t...

Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection.

IEEE transactions on biomedical circuits and systems
Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recogn...

RVDLAHA: An RISC-V DLA Hardware Architecture for On-Device Real-Time Seizure Detection and Personalization in Wearable Applications.

IEEE transactions on biomedical circuits and systems
Epilepsy is a globally distributed chronic neurological disorder that may pose a threat to life without warning. Therefore, the use of wearable devices for real-time detection and treatment of epilepsy is crucial. Additionally, personalizing disease ...

AI Accelerator With Ultralightweight Time-Period CNN-Based Model for Arrhythmia Classification.

IEEE transactions on biomedical circuits and systems
This work proposes a classification system for arrhythmias, aiming to enhance the efficiency of the diagnostic process for cardiologists. The proposed algorithm includes a naive preprocessing procedure for electrocardiography (ECG) data applicable to...

Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring.

IEEE transactions on biomedical circuits and systems
Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as object detection and classification. Unfortunately, these models are not yet widely adopted in health monitoring due to exceptionally high requiremen...