In-Sensor Artificial Intelligence and Fusion With Electronic Medical Records for At-Home Monitoring.

This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of electrocardiogram (ECG) and patient electronic medical record. An on-chip classifier combines analog reservoir-computer and artificial neural network to perform prediction without front-end data converter or feature extraction which reduces energy by 13× compared to digital baseline at normalized power efficiency of 528 TOPS/W, and reduces energy by 159× compared to RF transmission of all digitized ECG samples. The proposed AI framework predicts sepsis onset with 89.9% and 92.9% accuracy on patient data from Emory University Hospital and MIMIC-III respectively. The proposed framework is non-invasive and does not require lab tests which makes it suitable for at-home monitoring.

In-sensor neural network for high energy efficiency analog-to-information conversion.

This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-dimensional computing based on reservoir-computing paradigm to process electrocardiograph (ECG) signals locally in-sensor and reduce radio frequency transmission by more than three orders-of-magnitude. Instead of transmitting the naturally sparse ECG signal or extracted features, the on-chip analog-to-information converter analyzes the ECG signal through a nonlinear reservoir kernel followed by an artificial neural network, and transmits the prediction results. The proposed technique is demonstrated for detection of sepsis onset and achieves state-of-the-art accuracy and energy efficiency while reducing sensor power by [Formula: see text] with test-chips prototyped in 65 nm CMOS.