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

Showing 61 to 70 of 132 articles

Long-Term Bowel Sound Monitoring and Segmentation by Wearable Devices and Convolutional Neural Networks.

IEEE transactions on biomedical circuits and systems
Bowel sounds (BSs), typically generated by the intestinal peristalses, are a significant physiological indicator of the digestive system's health condition. In this study, a wearable BS monitoring system is presented for long-term BS monitoring. The ...

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification.

IEEE transactions on biomedical circuits and systems
Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique dec...

Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection.

IEEE transactions on biomedical circuits and systems
There is an unmet need for a low-cost instrumented technology for detecting sanitation-related malodor as an alert for maintenance around shared toilets and emerging technologies for onsite waste treatment. In this article, our approach to an electro...

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG.

IEEE transactions on biomedical circuits and systems
Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly su...

A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.

IEEE transactions on biomedical circuits and systems
In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset...

Intelligent Fault-Prediction Assisted Self-Healing for Embryonic Hardware.

IEEE transactions on biomedical circuits and systems
This paper proposes novel methods for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The proposed self-healing recovers a faulty embryonic cell throu...

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals.

IEEE transactions on biomedical circuits and systems
The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural netw...

Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning.

IEEE transactions on biomedical circuits and systems
The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model t...

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography.

IEEE transactions on biomedical circuits and systems
Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created th...

Molecular and DNA Artificial Neural Networks via Fractional Coding.

IEEE transactions on biomedical circuits and systems
This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work had addressed molecular two-layer ANNs with binary inputs and arbitrary weights. In prior work using f...