Browsing by Author "Yeh, Ming-Hua"

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A fast digital chip implementing a real-time noise-resistant algorithm for estimating blood pressure using a non-invasive, cuffless PPG sensor
(1970-01-01)
Wang, Jeremy H. -S.
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Yeh, Ming-Hua
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Chao, Paul C. -P.
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Tu, Tse-Yi
;
Kao, Yung-Hua
A fast digital chip is designed and fabricated successfully to implement the algorithm for estimating blood pressure (BP) in real time by a non-invasive cuffless photoplethysmogram (PPG) Sensor. The fast response accomplished by this digital computation chip is made possible by one-time calibration only and the Taiwan Semiconductor Manufacturing (TSMC) T18 process to realize the chip in an area of 3.97 mm(2). The experimental result shows that the overall power consumption of the chip is 15.62 mW. Finally, the blood pressure measurement platform has been developed with a user interface (UI) for long-term continuous cuffless BP measurement. The non-invasive blood pressure sensor is applied to the wrist arteries of 44 subjects for sensing the PPG pulsation of the blood vessel, and then estimating BPs (blood pressures). Measurement results shows that the maximum error in the BP measurement is +/- 6 mmHg. Which is less than 8 mmHg, defined by the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standard.
Active Photo-Sensing Array of Thin Film Transistor with Threshold Voltage Compensation
(2012)
Chou, Lu-Sheng
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Chen, Huo Moun
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Chen, Bo-Cheng
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Zhang, Jun-Yi
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Tai, Ya-Hsiang
In this paper, an active photo-sensing amplifier pixel circuit with compensation function of device variation is proposed, which has high aperture ratio since only 3 TFTs are used. The compensation function of the circuit obviously improves the sensing error resulted from device variation from 80% to 6%.
Design and implement the readout circuit of an in-cell high-resolution capacitive touch panel
(2017-01-01)
Ku, Tsung-Yen
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Chao, Paul C. -P.
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Huang, Chin-Hai
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Liu, En-Chih
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Yeh, Ming-Hua
This paper proposes a high resolution capacitive touch panel readout circuit to sense the Variation by self-capacitance method. This AM display of in-cell structure includes an active architecture for improved resolution, that controlling the TFT (Thin-film transistor) to make the accuracy better. In addition, the panel has 60 sensing point per 0.88 inches and the proposed readout circuit used switched capacitor charge amplifier(SCCA) to sensing the charge change and enhances the SNR (Signal-to-noise) by eliminating the baseline. The experimental results show that the proposed readout circuit SNR achieves 38dB and the frame rate is 200Hz.
Design and validation of the readout circuitry for an in-cell active-matrix capacitive (IAMC) touch panel
(2018-11-01)
Ku, Tsung-Yen
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Chao, Paul C. -P.
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Tu, Tse-Yi
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Huang, Chin-Hai
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Liu, En-Chih
This study is dedicated to design an in-cell active-matrix capacitive (IAMC) touch panel and its readout circuitry. This IAMC touch panel is in a structure that embeds all components for touch panel in a standard active matrix display. In this way, the thickness of the display-touch combined module can be significantly reduced, but introducing much noise from display to touch module, leading to higher mis-judgements on touches. To solve the problem, this study proposes a new readout circuit and digital signal processing techniques to tackle well the aforementioned noise for much accurate touch sensing. The readout consists essentially of an analog front-end switched capacitor charge amplifier (SCCA), a 12-bit SAR ADC and a digital signal processing (DSP) unit for calibration and filtering. A field-programmable gate array (FPGA) board is adopted to implement the designed digital signal processing in practice. The performance of the readout is tested for a 5x12, 0.88-inch IAMC touch panel with a scanning frequency of 200Hz. Experimental data show clearly that the sensed capacitance was successfully converted to 200 digits in a full dynamic range of 12-bits and a SNR of 52dB, which facilitates well accurate determination of touches and locations on an IAMC touch panel.
Estimating Blood Pressure via Artificial Neural Networks Based on Measured Photoplethysmography Waveforms
(2018-01-01)
Priyanka, K. N. G.
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Chao, Paul C. -P.
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Tu, Tse-Yi
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Kao, Yung-Hua
;
Yeh, Ming-Hua
A new approach for estimating blood pressure from photoplethysmography (PPG) signals is developed using artificial neural networks (ANNs). Blood Pressure is one of the most important parameters that can provide valuable information of personal healthcare. A reflective photoplethysmography (PPG) sensor module is developed for the cuffless, non-invasive blood pressure (BP) measurement based on PPG at wrist on radial artery. Blood Pressure is in a relation with the pulse duration of the PPG. In this paper, we propose to estimate blood pressure from PPG signal by using artificial neural networks approach. This is the first reported study to consider varied temporal periods of PPG waveforms as features for application of artificial neural networks (ANNs) to estimate blood pressure. We compared our results with those measured using a commercial cuff-based digital blood pressure measuring device and obtained encouraging results of overall SBP and DBP regression (R) as 0.99115.
High Resolution Active Photo-Sensing Circuit Using Dual-Scan Compensation
(2012-01-01)
Chou, Lu-Sheng
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Chen, Hua Mao
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Yang, Cheng-Ta
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Yang, Yung-Hsuan
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Tai, Ya-Hsiang
The simplest 2T1C sensing circuit has high aperture ratio in pixel, while it suffers from a severe error owing to the voltage drop on scan bus. In order to solve this issue, a dual-scanning active amplifier sensing circuit is proposed, which can correct the sensing error from 38.5% to 5.7%.