A 746 nW ECG Processor ASIC Based on Ternary Neural Network.

This paper presents an ultra-low power electrocardiography (ECG) processor application-specific integrated circuit (ASIC) for the real-time detection of abnormal cardiac rhythms (ACRs). The proposed ECG processor can support wearable or implantable ECG devices for long-term health monitoring. It adopts a derivative-based patient adaptive threshold approach to detect the R peaks in the PQRST complex of ECG signals.

A 2.63 μW ECG Processor With Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device.

An ultra-low power ECG processor ASIC (application specific integrated circuit) with R-wave detection and data compression is presented, which is designed for the long-term implantable cardiac monitoring (ICM) device for arrhythmia diagnosis. An adaptive derivative-based detection algorithm with low computation overhead for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats. In order to save as much as possible cardiac information with the limited memory size available in the ICM device, a hierarchical data buffer structure is proposed which saves 3 types of data, including the raw ECG data segments of 2 seconds, compressed ECG data segments of 45 seconds, and R-peak values and interval lengths of >2000 beat cycles.

A Wearable Skinlike Ultra-Sensitive Artificial Graphene Throat.

Most mute people cannot speak due to their vocal cord lesion. Herein, to assist mute people to "speak", we proposed a wearable skinlike ultrasensitive artificial graphene throat (WAGT) that integrated both sound/motion detection and sound emission in single device. In this work, the growth and patterning of graphene can be realized at the same time, and a thin poly(vinyl alcohol) film with laser-scribed graphene was obtained by a water-assisted transferring process.