IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 50, NO. 3, MARCH 2015

Woojae Lee, Member, IEEE, and SeongHwan Cho, Senior Member, IEEE

Chairman: Dr.Shih-Chung ChenAdviser: Dr.Ji-Jer HuangPresenter:Chun-Hao Chou

Date:2015/11/25

Integrated All Electrical Pulse Wave Velocity and

Respiration Sensors Using Bio-Impedance

Outline

Introduction

Material and Method

Result

Conclusion

INTRODUCTION

WITH many of the developed and developing countries around the world facing an increase in the aging population,affordable health care has become an important issue.

While there are many challenges to overcome in various aspects of health care, there are also opportunities for new electrical devices that can aid or replace existing health care systems.

INTRODUCTION

PWV (pulse wave velocity) is a measure of arterial stiffness and can be used to diagnose symptoms such as hypertension, stroke, and arrhythmia.

In order to measure PWV, which is basically a physical movement of the blood pulse, previous methods have relied on non-electrical approaches such as ultrasound , catheter, or Photoplethysmography (PPG)

INTRODUCTION

For PWV measurement, the time difference between the ECG signal from the heart and the BI (Bio-impedance) signal from the wrist is calculated.

Material and Method

Bio-impedance is the electrical impedance that depends on the various composites of the body.

In this work, we exploit bio-impedance to detect the movement of the pulse wave in an artery for PWV detection and volume change in the abdomen for respiration monitoring.

Material and Method

• Where an AC current is injected to a local body and the voltage across it is measured.

• When a pulse wave propagates, it changes the volume of the artery and causes impedance variation,generating an amplitude-modulated signal.

Material and Method

The volume change of the abdomen due to inhalation and exhalation causes change in impedance,which can be monitored by the BI technique.

Material and Method

PWV can be measured by detecting the time it takes for the pulse wave to travel from the heart to the wrist.

Material and Method

Unfortunately, such systems would suffer from sampling rate mismatch between the ECG and the BI sensor.

Material and Method

The ECG signal is sampled only at the BI sensor, which eliminates sampling rate mismatch.

Material and Method

To measure ECG, AC current is not injected, the mixer is bypassed, and the bandwidth of the IA is adjusted for the ECG signal.

Material and Method

In order to extract the signal under a poor environment, high CMRR and low-noise IA is required.

Material and Method

When an amplitude modulated signal is down-converted, a low-pass filter is required to filter out the high-frequency signal that results from mixing.

Material and Method

The PGA (Programmable-gain amplifier) also performs band pass filtering.

Lower cut-off frequency is determined by c1,c2. Higher cut-off frequency is determined by R and MOS

capacitors.

Material and Method

The volume change of the abdomen due to inhalation and exhalation causes impedance change of the body, which can be monitored by the BI technique.

Material and Method

In order to save power, automatic current control (ACC) is applied to the respiration sensor as shown in Fig.

Result

(a) wireless PWV sensor using body channel communication.

(b) respiration sensor with ACC.

Result

(a) Measurement setup of the PWV sensor. (b) electrodes for respiration measurement.

Result

Figure shows the waveform of the measured ECG and BI.

Result

The measured PWV is compared with that of the PPG-ECG method.

Result

Fig. (a) shows the measured gain curve of the IA. The gain is 20 dB and 10 dB in ECG and BI modes.

Fig. (b) shows the CMRR of the IA, which is 82 dB and 76 dB

in their respective modes.

Result

The measured output spectrum of the proposed BCC with 100 Hz sinusoidal input is shown in Figure.

Result

The subject was requested to hold his respiration for about 10 seconds during the test.

Result

The initial current is 80 uA and the final desired current is 240 uA with step increments of 10 uA. The time to find the optimum current level is 24 ms.

Result

Conclusion

The PWV is calculated by measuring the time difference between the signals from an ECG sensor on the chest and a BI sensor on the wrist.

While the proposed work has demonstrated the feasibility of an all-electrical sensor for non-electrical signals which opens opportunities for low-power, low-cost, and small-area sensors,some challenges remain if it is to be welcomed by people at large.

The foremost issue is the motion artifact. As BI is affected by muscles, the proposed PWV sensor is very sensitive to motion.

Future directions for motion compensation may include cancellation using inertia sensors or placing additional electrodes so that motion signals can be extracted and cancelled using an adaptive filter.

In its current status, we believe that the proposed system is best suited for sleep apnea monitoring,where the subject goes through limited motion during sleeping.

Conclusion

Thanks You for Your Attention