Masanao Nakano

A Wearable Healthcare System With a 13.7

To prevent lifestyle diseases, wearable bio-signal monitoring systems for daily life monitoring have attracted attention. Wearable systems have strict size and weight constraints, which impose significant limitations of the battery capacity and the signal-to-noise ratio of bio-signals. This report describes an electrocardiograph (ECG) processor for use with a wearable healthcare system. It comprises an analog front end, a 12-bit ADC, a robust Instantaneous Heart Rate (IHR) monitor, a 32-bit Cortex-M0 core, and 64 Kbyte Ferroelectric Random Access Memory (FeRAM). The IHR monitor uses a short-term autocorrelation (STAC) algorithm to improve the heart-rate detection accuracy despite its use in noisy conditions. The ECG processor chip consumes 13.7 μA for heart rate logging application.

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This report describes a robust method of Instantaneous Heart Rate (IHR) detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the interval of R-waves. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable biosignal monitoring systems, various noises (e.g. muscle artifacts from myoelectric signals, electrode motion artifacts) increase incidences of misdetection and false detection because the power consumption and electrode distance of the wearable sensor are limited to reduce its size and weight. To prevent incorrect detection, we use a short-time autocorrelation technique. The proposed method uses similarity of the waveform of the QRS complex. Therefore, it has no threshold calculation Process and it is robust for noisy environment. Simulation results show that the proposed method improves the success rate of IHR detection by up to 37%.

A Noise Tolerant ECG Processor

This paper describes an electrocardiograph (ECG) monitoring SoC using a non-volatile MCU (NVMCU) and a noise-tolerant instantaneous heartbeat detector. The novelty of this work is the combination of the non-volatile MCU for normally off computing and a noise-tolerant-QRS (heartbeat) detector to achieve both low-power and noise tolerance. To minimize the stand-by current of MCU, a non-volatile flip-flop and a 6T-4C NVRAM are used. Proposed plate-line charge-share and bit-line non-precharge techniques also contribute to mitigate the active power overhead of 6T-4C NVRAM. The proposed accurate heartbeat detector uses coarse-fine autocorrelation and a template matching technique. Accurate heartbeat detection also contributes system-level power reduction because the active ratio of ADC and digital block can be reduced using heartbeat prediction. Measurement results show that the fully integrated ECG-SoC consumes 6.14 μA including 1.28- μA non-volatile MCU and 0.7- μA heartbeat detector.

Instantaneous Heart Rate detection using short-time autocorrelation for wearable healthcare systems

This report describes an electrocardiograph (ECG) processor for use with a wearable healthcare system. It comprises an analog front end, a 12-bit ADC, a robust Instantaneous Heart Rate (IHR) monitor, a 32-bit Cortex-M0 core, and 64 Kbyte Ferroelectric Random Access Memory (FeRAM). The IHR monitor uses a short-term autocorrelation (STAC) algorithm to improve the heart-rate detection accuracy despite its use in noisy conditions. The ECG processor chip consumes 13.7 μA for heart rate logging application.

Normally Off ECG SoC With Non-Volatile MCU and Noise Tolerant Heartbeat Detector

This paper describes a robust method for heart beat detection from noisy electrocardiogram (ECG) signals. Generally, the QRS-complex of heart beat is extracted from the ECG using a threshold. However, in a noisy condition such a mobile and wearable bio-signal monitoring system, noise increases the incidence of misdetection and false detection of QRS-complex. To prevent incorrect detection, we introduce a novel template matching algorithm. The template waveform can be generated autonomously using a short-term autocorrelation method, which leverages the similarity of QRS-complex waveforms. Simulation results show the proposed method achieves state-of-the-art noise tolerance of heart beat detection.

A 14 µA ECG processor with robust heart rate monitor for a wearable healthcare system

This paper describes an electrocardiograph (ECG) monitoring SoC using a non-volatile MCU (NVMCU) and a noise tolerant instantaneous heart rate (IHR) monitor. The novelty of this work is the combination of the non-volatile MCU for normally-off computing and a noise-tolerant-QRS (heart beat) detection algorithm to achieve both low-power and noise tolerance. To minimize the stand-by current of MCU, a non-volatile flip-flop and a 6T-4C NVRAM are employed. Proposed plate-line charge-share and bit-line non-precharge techniques also contribute to mitigate the active power overhead of 6T-4C NVRAM. The proposed accurate heart beat detector employs a coarse-fine autocorrelation and a template matching technique. Accurate heart beat detection also contributes system level power reduction because the active ratio of ADC and digital block can be reduced using a heart beat prediction. Then, at least 25% active time can be reduced. Measurement results show the fully integrated ECG-SoC consumes 6.14μA including 1.28-μA nonvolatile MCU and 0.7-μA heart rate extractor.

Noise tolerant QRS detection using template matching with short-term autocorrelation

This paper describes a robust method of Instantaneous Heart Rate (IHR) and R-peak detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the R-wave interval. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable bio-signal monitoring systems, noise increases the incidence of misdetection and false detection of R-peaks. To prevent incorrect detection, we introduce a short-term autocorrelation (STAC) technique and a small-window autocorrelation (SWAC) technique, which leverages the similarity of QRS complex waveforms. Simulation results show that the proposed method improves the noise tolerance of R-peak detection.

A 6.14µA normally-off ECG-SoC with noise tolerant heart rate extractor for wearable healthcare systems

This paper presents a low-power wearable biosignal monitoring system. The proposed system can communicate with smartphones using Near Field Communication (NFC) to check vital signs easily at any time. It comprises a battery, electrodes, a triaxial accelerometer IC, an NFC tag IC, and a biosignal processor LSI. The proposed biosignal processor LSI, fabricated using a 130-nm CMOS process, comprises heart rate monitoring circuits, a 32-kbyte ferroelectric random access memory (FeRAM), an accelerometer interface, and an NFC interface. The proposed system consumes 38.1 μA for logging application at 32-kHz operating frequency, with 3.0-V supply voltage.

Noise-tolerant instantaneous heart rate and R-peak detection using short-term autocorrelation for wearable healthcare systems

This report describes an electrocardiograph (ECG) processor for use with a wearable healthcare system. It comprises an analog front end, a 12-bit ADC, a robust Instantaneous Heart Rate (IHR) monitor, a 32-bit Cortex-M0 core, and 64 Kbyte Ferroelectric Random Access Memory (FeRAM). The IHR monitor uses a short-term autocorrelation (STAC) algorithm to improve the heart-rate detection accuracy despite its use in noisy conditions. The ECG processor chip consumes 13.7μA for heart rate logging application.

A 38 μA wearable biosignal monitoring system with near field communication

To prevent lifestyle diseases, wearable bio-signal monitoring systems for daily life monitoring have attracted attention. Wearable systems have strict size and weight constraints, which impose significant limitations of the battery capacity and the signal-to-noise ratio of bio-signals. The novelty of this work is the hardware implementation of a noise-tolerant heart rate extraction algorithm that can achieve low-power performance with high reliability. This report describes comparisons of the heart rate extraction algorithm performance and the dedicated hardware implementation of short-term autocorrelation (STAC) method. The proposed heart rate extractor, implemented in 65-nm CMOS process using Verilog-HDL, consumes 1.65 μA at 32.768-kHz operating frequency with 1.1 V supply voltage.