Precision Heart Rate Estimation Using a PPG Sensor Patch Equipped with New Algorithms of Pre-Quality Checking and Hankel Decomposition.

A new method for accurately estimating heart rates based on a single photoplethysmography (PPG) signal and accelerations is proposed in this study, considering motion artifacts due to subjects' hand motions and walking. The method comprises two sub-algorithms: pre-quality checking and motion artifact removal (MAR) via Hankel decomposition. PPGs and accelerations were collected using a wearable device equipped with a PPG sensor patch and a 3-axis accelerometer. The motion artifacts caused by hand movements and walking were effectively mitigated by the two aforementioned sub-algorithms. The first sub-algorithm utilized a new quality-assessment criterion to identify highly noise-contaminated PPG signals and exclude them from subsequent processing. The second sub-algorithm employed the Hankel matrix and singular value decomposition (SVD) to effectively identify, decompose, and remove motion artifacts. Experimental data collected during hand-moving and walking were considered for evaluation. The performance of the proposed algorithms was assessed using the datasets from the IEEE Signal Processing Cup 2015. The obtained results demonstrated an average error of merely 0.7345 ± 8.1129 beats per minute (bpm) and a mean absolute error of 1.86 bpm for walking, making it the second most accurate method to date that employs a single PPG and a 3-axis accelerometer. The proposed method also achieved the best accuracy of 3.78 bpm in mean absolute errors among all previously reported studies for hand-moving scenarios.

External temperature sensor assisted a new low power photoplethysmography readout system for accurate measurement of the bio-signs.

This study presents an external temperature sensor assisted a new low power, time-interleave, wide dynamic range, and low DC drift photoplethysmography (PPG) signal acquisition system to obtain the accurate measurement of various bio signs in real-time. The designed chip incorporates a 2-bit control programmable transimpedance amplifier (TIA), a high order filter, a 3:8 programmable gain amplifier (PGA) and 2 × 2 organic light-emitting diode (OLED) driver. Temperature sensor is used herein to compensate the adverse effect of low-skin-temperature on the PPG signal quality. The analog front-end circuit is implemented in the integrated chip with chip area of 2008 µm × 1377 µm and fabricated via TSMC T18 process. With the standard 1.8 V, the experimental result shows that the measured current sensing range is 20 nA-100 uA. The measured dynamic range of the designed readout circuit is 80 dB. The estimated signal to noise ratio is 60 dB@1 uA, and the measured input referred noise is 60.2 pA/Hz½. The total power consumption of the designed chip is 31.32 µW (readout) + 1.62 mW (OLED driver@100% duty cycle). The non-invasive PPG sensor is applied to the wrist artery of the 40 healthy subjects for sensing the pulsation of the blood vessel. The experimental results show that for every 1 °C decrease in mean ambient temperature tends to 0.06 beats/min, 0.125 mmHg and 0.063 mmHg increase in hear rate (HR), systolic (SBP) and diastolic (DBP), respectively. Similarly, for every 1 °C increase in mean ambient temperature tends to 0.13 beats/min, 0.601 mmHg and 0.121 mmHg increase in HR, SBP and DBP, respectively. The measured accuracy and standard error for the HR estimation are 96%, and - 0.022 ± 2.589 beats/minute, respectively. The oxygen stauration (SpO2) measurement results shows that the mean absolute percentage error is less than 5%. The resultant errors for the SBP and DBP measurement are - 0.318 ± 5.19 mmHg and - 0.5 ± 1.91 mmHg, respectively.

Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device.

The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog-digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer-Lambert's law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF.

A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine.

A portable, wireless photoplethysomography (PPG) sensor for assessing arteriovenous fistula (AVF) by using class-weighted support vector machines (SVM) was presented in this study. Nowadays, in hospital, AVF are assessed by ultrasound Doppler machines, which are bulky, expensive, complicated-to-operate, and time-consuming. In this study, new PPG sensors were proposed and developed successfully to provide portable and inexpensive solutions for AVF assessments. To develop the sensor, at first, by combining the dimensionless number analysis and the optical Beer Lambert's law, five input features were derived for the SVM classifier. In the next step, to increase the signal-noise ratio (SNR) of PPG signals, the front-end readout circuitries were designed to fully use the dynamic range of analog-digital converter (ADC) by controlling the circuitries gain and the light intensity of light emitted diode (LED). Digital signal processing algorithms were proposed next to check and fix signal anomalies. Finally, the class-weighted SVM classifiers employed five different kernel functions to assess AVF quality. The assessment results were provided to doctors for diagonosis and detemining ensuing proper treatments. The experimental results showed that the proposed PPG sensors successfully achieved an accuracy of 89.11% in assessing health of AVF and with a type II error of only 9.59%.

Control Design and Digital Implementation of a Fast 2-Degree-of-Freedom Translational Optical Image Stabilizer for Image Sensors in Mobile Camera Phones.

This study presents design, digital implementation and performance validation of a lead-lag controller for a 2-degree-of-freedom (DOF) translational optical image stabilizer (OIS) installed with a digital image sensor in mobile camera phones. Nowadays, OIS is an important feature of modern commercial mobile camera phones, which aims to mechanically reduce the image blur caused by hand shaking while shooting photos. The OIS developed in this study is able to move the imaging lens by actuating its voice coil motors (VCMs) at the required speed to the position that significantly compensates for imaging blurs by hand shaking. The compensation proposed is made possible by first establishing the exact, nonlinear equations of motion (EOMs) for the OIS, which is followed by designing a simple lead-lag controller based on established nonlinear EOMs for simple digital computation via a field-programmable gate array (FPGA) board in order to achieve fast response. Finally, experimental validation is conducted to show the favorable performance of the designed OIS; i.e., it is able to stabilize the lens holder to the desired position within 0.02 s, which is much less than previously reported times of around 0.1 s. Also, the resulting residual vibration is less than 2.2-2.5 µm, which is commensurate to the very small pixel size found in most of commercial image sensors; thus, significantly minimizing image blur caused by hand shaking.

A Fast FPGA Hardware Accelerator for Remote Heart Rate Detection Based on RGB Vision.

A fast hardware accelerator is created by this work via field programmable gate array (FPGA) to estimate heart rate (HR) through the video recorded by a RGB camera based on the technology of remote photoplethysmography (rPPG). The method of rPPG acquires physiological signals of a human body by analyzing the subtle color changes on the surface of the human skin. The hardware implementation of rPPG to estimate HR is proposed herein to aim for a much faster calculation speed than software for a number of applications, like heart failure pre-warning of an in-action athlete and drowsiness detection of a driver. In this accelerator, ICA (Independent Component Analysis) is used to recover the blood volume pulse from the raw signals of remote PPG, and then obtain the heart rate value. The architecture of the hardware circuit is described in Verilog HDL and verified by Quartus II, and also implemented in an Altera DE10-Standard FPGA board, which consists of image capture, heart rate algorithm and image display. A TRDB-D5M camera is utilized for image capture. Two experiments were conducted with image collecting duration of 16 seconds and 8 seconds respectively, and the commercial device Omron HEM-6111 was used as the golden value. The proposed system achieves an accuracy in (ME ± 1.96SD) of -0.76 ± 5.09 and -0.70 ± 8.71 bpm in the short periods of 16-second and 8-second versions, respectively, which outperforms all the reported prior works in combined computation time and accuracy.

Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning.

Blood pressure (BP) is predicted by this effort based on photoplethysmography (PPG) data to provide effective pre-warning of possible preeclampsia of pregnant women. Towards frequent BP measurement, a PPG sensor device is utilized in this study as a solution to offer continuous, cuffless blood pressure monitoring frequently for pregnant women. PPG data were collected using a flexible sensor patch from the wrist arteries of 194 subjects, which included 154 normal individuals and 40 pregnant women. Deep-learning models in 3 stages were built and trained to predict BP. The first stage involves developing a baseline deep-learning BP model using a dataset from common subjects. In the 2nd stage, this model was fine-tuned with data from pregnant women, using a 1-Dimensional Convolutional Neural Network (1D-CNN) with Convolutional Block Attention Module (CBAMs), followed by bi-directional Gated Recurrent Units (GRUs) layers and attention layers. The fine-tuned model results in a mean error (ME) of -1.40 ± 7.15 (standard deviation, SD) for systolic blood pressure (SBP) and -0.44 (ME) ± 5.06 (SD) for diastolic blood pressure (DBP). At the final stage is the personalization for individual pregnant women using transfer learning again, enhancing further the model accuracy to -0.17 (ME) ± 1.45 (SD) for SBP and 0.27 (ME) ± 0.64 (SD) for DBP showing a promising solution for continuous, non-invasive BP monitoring in precision by the proposed 3-stage of modeling, fine-tuning and personalization.

A Dual-Channel PPG Readout System With Motion-Tolerant Adaptability for OLED-OPD Sensors.

An adaptive PPG (Photoplethysmography) readout system for a dual-channel OLED-OPD flexible sensor is designed and developed with motion artifact (<1Hz) and ambient lighting interference successfully compensated without any additional motion sensors. The compensation is made possible by adopting multi-feedbacks and an additional reference OPD channel to cancel effectively DC drifts. In result, the quality of measured PPG is improved to the level such that long-time, continuous quality monitoring of bio-sign such as heart rate (HR) is possible. The readout is designed with an auto-programmable band-pass trans-impedance amplifier (TIA) of a 100dbΩ gain with a continuous-type DC-current cancellation loop. The rest of the readout consists of a 0.5 Hz low-pass filter, an additional second-order band-pass filter (0.1-10Hz), a difference amplifier, a motion reference channel, an analog multiplexer, a programmable gain amplifier (PGA), a digital control and a programmable DAC-PWM based auto-intensity tuned OLED driver. The readout is fabricated in an area of 9 mm2 via the TSMC 180nm process. The experiment result shows that the developed OLED-OPD readout senses well as small as 1nA current, with a measured dynamic range >90dB (1nA to 100 µA) and input-referred noise of 0.26 nA/√H, with power consumption of 460µW. The DC drift is successfully reduced to 1% of its average. The accuracy for heart rate is 96%.

A new dual-frequency liquid crystal lens with ring-and-pie electrodes and a driving scheme to prevent disclination lines and improve recovery time.

A new liquid crystal lens design is proposed to improve the recovery time with a ring-and-pie electrode pattern through a suitable driving scheme and using dual-frequency liquid crystals (DFLC) MLC-2048. Compared with the conventional single hole-type liquid crystal lens, this new structure of the DFLC lens is composed of only two ITO glasses, one of which is designed with the ring-and-pie pattern. For this device, one can control the orientation of liquid crystal directors via a three-stage switching procedure on the particularly-designed ring-and-pie electrode pattern. This aims to eliminate the disclination lines, and using different drive frequencies to reduce the recovery time to be less than 5 seconds. The proposed DFLC lens is shown effective in reducing recovery time, and then serves well as a potential device in places of the conventional lenses with fixed focus lengths and the conventional LC lens with a single circular-hole electrode pattern.

A new low-voltage-driven GRIN liquid crystal lens with multiple ring electrodes in unequal widths.

This work is dedicated to design a novel liquid crystal (LC) lens device with multiple ring electrodes in unequal widths, in order to offer tunability on focusing quality and to lower the level of applied voltage. The number and widths of the multiple ring electrodes are pre-designed and optimized to offer the on-line tunability on individual electrode voltages to render a better refraction index distribution for focusing, as compared to the past hole-type LC lenses. The resulted refractive index distribution is expected to offer similar focusing effects based on the theory of the gradient refraction index (GRIN) lens. The transparent electrodes of this new LC lens are placed at the inner surface of the LC cell to minimize the driving voltages, in results, less than 10 V, for the same level of focusing power and an easy practical operation. A new fabrication process in the wafer level to bury bus lines is developed for generating smooth electrical fields over the lens aperture. In addition, a dielectric layer is coated between electrodes and the LC layer.

Achieving high focusing power for a large-aperture liquid crystal lens with novel hole-and-ring electrodes.

Aiming to equip commercial camera modules, such as the optical imaging systems with a CMOS sensor module in 3 Mega pixels, an ultra thin liquid crystal lens with designed hole-and-ring electrodes is proposed in this study to achieve high focusing power. The LC lens with proposed electrodes improves the central intensity of electric field which leads to better focusing quality. The overall thickness of the LC lens can be as thin as 1.2 mm and the shortest focal length of the 4 mm-aperture lens occurs at 20 cm under an applied voltage of 30 V at 1 KHz. The inner ring electrode requires only 40% of applied voltage of the external hole electrode. The applied voltages for this internal ring and external hole electrodes can simply be realized by a pre-designed parallel resistance pair and a single voltage source. Experiments are conducted for validation and it shows that the designed LC lens owns good image clearness and contrast at the focal plane. The proposed design reduces the thickness of LC lens and is capable of achieving relative higher focusing power than past studies with lower applied voltage.

Dorsolateral subthalamic neuronal activity enhanced by median nerve stimulation characterizes Parkinson's disease during deep brain stimulation with general anesthesia.

OBJECT: Deep brain stimulation (DBS) surgery under general anesthesia is an alternative option for patients with Parkinson's disease (PD). However, few studies are available that report whether neuronal firing can be accurately recorded during this condition. In this study the authors attempted to characterize the neuronal activity of the subthalamic nucleus (STN) and elucidate the influence of general anesthetics on neurons during DBS surgery in patients with PD. The benefit of median nerve stimulation (MNS) for localization of the dorsolateral subterritory of the STN, which is involved in sensorimotor function, was explored. METHODS: Eight patients with PD were anesthetized with desflurane and underwent contralateral MNS at the wrist during microelectrode recording of the STN. The authors analyzed the spiking patterns and power spectral density (PSD) of the background activity along each penetration track and determined the spatial correlation to the target location, estimated mated using standard neurophysiological procedures. RESULTS: The dorsolateral STN spiking pattern showed a more prominent bursting pattern without MNS and more oscillation with MNS. In terms of the neural oscillation of the background activity, beta-band oscillation dominated within the sensorimotor STN and showed significantly more PSD during MNS (p < 0.05). CONCLUSIONS: Neuronal firing within the STN could be accurately identified and differentiated when patients with PD received general anesthetics. Median nerve stimulation can enhance the neural activity in beta-band oscillations, which can be used as an index to ensure optimal electrode placement via successfully tracked dorsolateral STN topography.

Dynamic modeling and experimental verification of a piezoelectric part feeder in a structure with parallel bimorph beams.

The study is aimed to perform dynamic modeling of a part feeder powered by piezoelectric actuation. This part feeder consists mainly of a horizontal platform vibrated by a pair of parallel piezoelectric bimorph beams. Owing to intermittent impacts with the platform, the transported part on the platform is able to march forward from one end to another. Dynamic modeling of the feeder is accomplished by essentially using the Rayleigh-Ritz decomposition method. The process of modeling first incorporates material properties and constitutive equations of the piezoelectric materials, and then captures the complex dynamics of the parallel-beam piezo-feeder by three low-order assumed-modes in the transverse direction of the vibrating beams. Applying Lagrange's equations on the kinetic and strain energies formulated in terms of generalized coordinates associated with the first three modes, the system dynamics is then represented by three coupled discrete equations of motion. Based on these equations, motions of the platform can be obtained. With platform motion in hand, the intermittent impacts between the parts and the platform are modeled, rendering the marching speed of the part. Numerical simulations are conducted along with the experiments. The closeness found between the theoretical predicted transporting speed of the part and the experimental counterparts verify the effectiveness of the models established.