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. Two tiny machine learning classifiers are used for the accurate classification of ACRs. A 3-layer feed-forward ternary neural network (TNN) is designed, which classifies the QRS complex's shape, followed by the adaptive decision logics (DL). The proposed processor requires only 1 KB on-chip memory to store the parameters and ECG data required by the classifiers. The ECG processor has been implemented based on fully-customized near-threshold logic cells using thick-gate transistors in 65-nm CMOS technology. The ASIC core occupies a die area of 1.08 mm2. The measured total power consumption is 746 nW, with 0.8 V power supply at 2.5 kHz real-time operating clock. It can detect 13 abnormal cardiac rhythms with a sensitivity and specificity of 99.10% and 99.5%. The number of detectable ACR types far exceeds the other low power designs in the literature.

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 modified swinging-door-trending (SDT) method is proposed for the ECG data compression. The ASIC has been implemented based on fully-customized near-threshold standard cells using the thick-gate transistors in 65-nm CMOS technology for low dynamic power consumption and leakage. The ASIC core occupies a die area of 1.77 mm2. The measured total power is 2.63 µW, which is among the ECG processors with the lowest core power consumption. It exhibits a relatively high positive precision rate (P+) of 99.3% with a sensitivity of 98.2%, in contrast to the similar designs in literature with the same core power consumption level. Also, an ECG data compression ratio (CR) of up to 17.0 has been achieved, with a good trade-off between the compression efficiency and loss.

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. In virtue of the skinlike and low-resistant substrate, the WAGT has a high detection sensitivity (relative resistance changes up to 150% at 133 Ω) and an excellent sound-emitting ability (up to 75 dB at 0.38 W power and 2 mm distance). On the basis of the excellent mechanical-electrical performance of graphene structure, the sound detecting and emitting mechanisms of WAGT are realized and discussed. For sound detection, both the motion of larynx and vibration of vocal cord contribute to throat movements. For sound emission, a thermal acoustic model for WAGT was established to reveal the principle of sound emitting. More importantly, a homemade circuit board was fabricated to build a dual-mode system, combining the detection and emitting systems. Meanwhile, different human motions, such as strong and small throat movements, were also detected and transformed into different sounds like "OK" and "NO". Therefore, the implementation of these sound/motion detection acoustic systems enable graphene to achieve device-level applications to system-level applications, and those graphene acoustic systems are wearable for its miniaturization and light weight.