To overcome light interference, including a large DC offset and ambient light variation, a robust photoplethysmogram (PPG) readout chip is fabricated using a 0.13-μm complementary metal–oxide–semiconductor (CMOS) process. Against the large DC offset, a saturation detection and current feedback circuit is proposed to compensate for an offset current of up to 30 μA. For robustness against optical path variation, an automatic emitted light compensation method is adopted. To prevent ambient light interference, an alternating sampling and charge redistribution technique is also proposed. In the proposed technique, no additional power is consumed, and only three differential switches and one capacitor are required. The PPG readout channel consumes 26.4 μW and has an input referred current noise of 260 pArms.

A low power and low noise reconfigurable analog front-end (AFE) system on a chip (SoC) for biosignal acquisition is presented. The presented AFE can be reconfigured for use in electropotential, bioimpedance, electrochemical, and photoelectrical modes. The advanced healthcare services based on multiparameter physiological biosignals can be easily implemented with these multimodal and highly reconfigurable features of the proposed system. The reconfigurable gain and input referred noise of the core instrumentation amplifier block are 25 dB to 52 dB, and 1 μV_RMS, respectively. The power consumption of the analog blocks in one readout channel is less than 52 μW. The reconfigurable capability among various modes of applications including electrocardiogram, blood glucose concentration, respiration, and photoplethysmography are shown experimentally.

A low-power and low-noise dynamic instrumentation amplifier (IA) for biopotential acquisition is presented. A dynamic IA that can reduce power consumption with a timely piecewise power-gating method, and noise level with an alternating input and chopper stabilization technique is fabricated with a 0.13-μm CMOS. Using the reconfigurable architecture of the IA, various combinations of the low-noise schemes are investigated. The combination of power gating and chopper stabilization shows a lower noise performance than the combination of power gating and alternating input switching scheme. This dynamic IA achieved a power reduction level of 50% from 10 µA to 5 µA and a noise reduction of 90% from 9.1 µVrms to 0.92 µVrms with the combination of the power gating and chopper stabilization scheme.

This paper presents an ultralow power 0.6 V 116 nW neural spike acquisition integrated circuit with analog spike extraction. To reduce power consumption, an ultralow power self-biased current-balanced instrumentation amplifier (IA) is proposed. The passive RC lowpass filter in the amplifier acts as both DC servo loop and self-bias circuit. The spike detector, based on an analog nonlinear energy operator consisting of a low-voltage open-loop differentiator and an open-loop gate-bulk input multiplier, is designed to emphasize the high frequency spike components nonlinearly. To reduce the spike detection error, the adjacent spike merger is also proposed. The proposed circuit achieves a low IA current consumption of 46.4 nA at 0.6 V, noise efficiency factor (NEF) of 1.81, the bandwidth from 102 Hz to 1.94 kHz, the input referred noise of 9.37 μVrms, and overall power consumption of 116 nW at 0.6 V. The proposed circuit can be used in the ultralow power spike pulses acquisition applications, including the neurofeedback systems on peripheral nerves with low neuron density.

In this paper, we investigated the validity of the WHAM (wearable heart activity monitor) in the clinical applications, which has been implemented as a wearable ambulatory device for continuously and long-term monitoring user's cardiac conditions. To this end, using the WHAM and the conventional Holter monitor the ECG signals over 24 hours were recorded during daily activities. The signal from the WHAM was compared with that from the conventional Holter monitor in terms of the readability of the signal, the quality of the signal, and the accuracy of arrhythmia detection. The performance of the WHAM was a little lower as compared with the conventional Holter monitor, although showing no significant difference (the readability of the signal: 97.2% vs 99.3%; the quality of the signal: 0.97 vs 0.98; the accuracy of arrhythmia detection: 96.2% vs 98.1%). From these results, it is likely that the WHAM shows the performance enough to be used in the clinical application as a wearable ambulatory monitoring device

In developing a wrist blood pressure monitor of high and reliable accuracy, the effect of different pressurization methods on the accuracy of blood pressure measurement at the wrist using oscillometry is investigated in this paper. 30 volunteers are recruited and blood pressure readings are taken with three different methods of pressurizing the wrist. It was found that measurement of mean arterial pressure (MAP) is more accurate when the wrist is locally compressed directly over the radial artery (-2.6 ± 11.4 mmHg) or with a region of surrounding tissue (10.3 ± 6.0 mmHg) than when the whole wrist is compressed by a conventional, constricting cuff (-11.4 ± 16.4 mmHg). Characteristics of accuracy, however, differ between the two local pressurization methods. While a square airbag that compresses the wrist directly over the radial artery may measure the most accurate MAP on average, the range of errors among individuals is large. Contrarily, measurements taken by pressurizing a region over the radial artery with a bladder are least affected by individual variability. In order to measure blood pressure accurately at the wrist while unbiased by the population-based algorithmic compensation to ensure accuracy among different individuals, therefore, the use of local pressurization method may be the most appropriate.