High Precision Ping-pong Auto-zeroed Lock-in Fluorescence Photometry Sensor.

This paper presents a high-precision CMOS fluorescence photometry sensor using a novel lock-in amplification scheme based on switched-biasing and ping-pong auto-zeroing techniques. The CMOS sensor includes two photodiodes and a lock-in amplifier (LIA) operating at 1 kHz. The LIA comprises a differential low-noise amplifier using a novel switched-biasing ping-pong auto-zeroed scheme, an automatic phase aligner, a programmable gain amplifier, a band-pass filter, a mixer, and an output low-pass filter. The design is fabricated in 0.18-µm CMOS process, and the measurement shows that the LIA can retrieve noisy input signals with a dynamic reserve of 42 dB, while consuming only 0.7 mW from a 1.8 V supply voltage. The measured results show that the LIA can detect a wide range of incident light power from 8 nW to 24 µW. The proposed design is encapsulated in a 3D-printed housing allowing for real-time in vitro biomarker detection. This ambulatory platform uses an LED and a fiber optic to convey the excitation light to the sample and retrieve the fluorescence signal. Experiments with a beads solution diluted in PBS demonstrate that the sensor has a sensitivity of 1:100 k. Experimental results obtained in vitro with NIH3T3 mouse cells tagged with membrane dye show the ability of the prototype to detect different densities of cell culture. The portable prototype, which includes optical filters and a small 30 mm × 36 mm × 30 mm printed circuit board enclosed inside the 3D-printed housing, consumes 36.7 mW and weighs 120 g.

Deep Learning Empowered Fresnel-based Lensless Fluorescence Microscopy.

Miniaturized fluorescence microscopy has revolutionized the way neuroscientists study the brain in-vivo. Recent developments in computational lensless imaging promise a next generation of miniaturized microscopes in lensless fluorescence microscopy. We developed a microscope prototype using an optimized Fresnel amplitude mask. While many lensless imaging modalities have reported excellent performance using Deep Learning (DL) approaches, DL application in fluorescence imaging has been left untouched. We generated a computational dataset based on experimental system calibration to evaluate DL capabilities on biological cell morphologies. We show that our DL-assisted microscope can provide high-quality imaging with a structural similarity index of 89%. The least absolute error was decreased by 63% using the DL-assisted method compared with the classical models. The state-of-the-art performance of this prototype enhances the expected potential of amplitude masks in lensless microscopy applications, which are critical for robust in-vivo flat microscopy with engineered image sensors.Clinical Relevance- This study aids in advancing miniaturized fluorescence microscopy, which greatly impacts long-term brain circuit and disease studies in freely moving animal models.

A Low-Power High Input Range PPG Readout Amplifier with a Current Buffer Input.

This paper presents ultra-low power photoplethysmography (PPG) readout circuits. The proposed system architecture uses a current buffer between the photodiode (PD) and the transimpedance amplifier (TIA) to isolate the large parasitic capacitance of the PD leading to improves the power consumption of the TIA. A class AB topology is exploited at the output of the amplifier, which allows for increased drive capability without the use of auxiliary circuits. The maximum input current range of the TIA is 160 µA, so the large DC current of the input signal does not saturate the circuit. In the LED driver circuit, by varying the duty cycle of a pulse wave modulation (PWM) signal, the ON and OFF times of the circuits. The amplifier and LED driver are manufactured in the 130 nm TSMC CMOS process. The power consumption of the circuits with a duty cycle of 1% is 3.28 µW (at VDD = 1.2V).Clinical Relevance- Vital signs are becoming a very important research topic due to the recent prevalence of COVID-19 and other respiratory diseases. This research aims to develop and interface circuits to monitor vital signs including blood pressure, heart rate, and respiratory rate to study respiratory disease, drug safety, and efficacy.

A fully-flexible and thermally adjustable implantable neural probe with a U-turn polyester microchannel.

This work presents a fully flexible implantable neural probe fabricated with Polydimethylsiloxane (PDMS) and including a thermally-tunable stiffness microchannel filled with Polyester. The probe includes an optimized microfluidics mixer for drug delivery. Polyester, which is solid at room temperature and has a low melting point close to body temperature, is used to decrease the stiffness of the probe after insertion, after getting in contact with tissues. We designed a U-turn microchannel inside the PDMS neural probe and filled it up with melted polyester. The microchannel has a cross-section of 30 µm × 5 µm and a length of 14.7 mm. The following probe dimensions were chosen after extensive simulation: thickness = 20 µm, width = 300 µm, and length = 7 mm. These values yield a buckling force above 1 mN, which is sufficient for proper insertion into the brain tissues. Simulation results show that the microfluidics mixer with a cross-section of 90 µm × 5 µm and a length of 7 mm has optimum performance for the desired flow rate and quantity of drug to deliver. The pressure drop inside the microfluidic channel is less than 0.43 kPa, which is appropriate for PDMS-PDMS bonding, whereas the Reynolds number is near 1.91k in the laminar regime. No leakage or bubble occurred during the experimental validation, which suggests an appropriate pressure and a laminar flow in the channel.

EMaGer: A Wearable Full-Circumference HD-EMG Sensor and Data Augmentation Method for Robust Hand Gesture Recognition.

This work presents EMaGer, a new 360° 64-channel high-density electromyography (HD-EMG) bracelet combined with an original data augmentation method for improved robustness in gesture recognition. By leveraging homogeneous electrode density and powerful deep learning techniques, the sensor is capable of rotation invariance around the arm axis, thus increasing gesture recognition robustness to electrode movement and inter-session evaluation. The system is made of a 4x16 electrode array covering the full circumference of the limb, and uses a sampling frequency of 1 kHz and a 16-bit resolution. The sensor's uniform and adjustable geometry paired with an array barrel shifting data augmentation (ABSDA) technique allows a convolutional neural network to maintain a 76.98% inter-session classification accuracy for a 6 gestures dataset, from a baseline intra-session accuracy of 93.75%. High inter-session classification accuracy decreases the training burden for users of EMG control systems such as myoelectric prostheses by minimizing calibration requirements. The same methods applied with different state-of-the-art sensors are demonstrated to be less effective. Thus, this work evidences the importance of co-designing the EMG sensor system with the gesture inference algorithms to leverage synergistic properties and solve state-of-the-art challenges.Clinical relevance- This paper establishes a method that alleviates clinical manipulations in setting up and calibrating myoelectric prosthetic devices.

Novel Wearable HD-EMG Sensor With Shift-Robust Gesture Recognition Using Deep Learning.

In this work, we present a hardware-software solution to improve the robustness of hand gesture recognition to confounding factors in myoelectric control. The solution includes a novel, full-circumference, flexible, 64-channel high-density electromyography (HD-EMG) sensor called EMaGer. The stretchable, wearable sensor adapts to different forearm sizes while maintaining uniform electrode density around the limb. Leveraging this uniformity, we propose novel array barrel-shifting data augmentation (ABSDA) approach used with a convolutional neural network (CNN), and an anti-aliased CNN (AA-CNN), that provides shift invariance around the limb for improved classification robustness to electrode movement, forearm orientation, and inter-session variability. Signals are sampled from a 4×16 HD-EMG array of electrodes at a frequency of 1 kHz and 16-bit resolution. Using data from 12 non-amputated participants, the approach is tested in response to sensor rotation, forearm rotation, and inter-session scenarios. The proposed ABSDA-CNN method improves inter-session accuracy by 25.67% on average across users for 6 gesture classes compared to conventional CNN classification. A comparison with other devices shows that this benefit is enabled by the unique design of the EMaGer array. The AA-CNN yields improvements of up to 63.05% accuracy over non-augmented methods when tested with electrode displacements ranging from -45 ° to +45 ° around the limb. Overall, this article demonstrates the benefits of co-designing sensor systems, processing methods, and inference algorithms to leverage synergistic and interdependent properties to solve state-of-the-art problems.

Improving flat fluorescence microscopy in scattering tissue through deep learning strategies.

Intravital microscopy in small animals growingly contributes to the visualization of short- and long-term mammalian biological processes. Miniaturized fluorescence microscopy has revolutionized the observation of live animals' neural circuits. The technology's ability to further miniaturize to improve freely moving experimental settings is limited by its standard lens-based layout. Typical miniature microscope designs contain a stack of heavy and bulky optical components adjusted at relatively long distances. Computational lensless microscopy can overcome this limitation by replacing the lenses with a simple thin mask. Among other critical applications, Flat Fluorescence Microscope (FFM) holds promise to allow for real-time brain circuits imaging in freely moving animals, but recent research reports show that the quality needs to be improved, compared with imaging in clear tissue, for instance. Although promising results were reported with mask-based fluorescence microscopes in clear tissues, the impact of light scattering in biological tissue remains a major challenge. The outstanding performance of deep learning (DL) networks in computational flat cameras and imaging through scattering media studies motivates the development of deep learning models for FFMs. Our holistic ray-tracing and Monte Carlo FFM computational model assisted us in evaluating deep scattering medium imaging with DL techniques. We demonstrate that physics-based DL models combined with the classical reconstruction technique of the alternating direction method of multipliers (ADMM) perform a fast and robust image reconstruction, particularly in the scattering medium. The structural similarity indexes of the reconstructed images in scattering media recordings were increased by up to 20% compared with the prevalent iterative models. We also introduce and discuss the challenges of DL approaches for FFMs under physics-informed supervised and unsupervised learning.

Portable impedance-sensing device for microorganism characterization in the field.

A variety of biosensors have been proposed to quickly detect and measure the properties of individual microorganisms among heterogeneous populations, but challenges related to cost, portability, stability, sensitivity, and power consumption limit their applicability. This study proposes a portable microfluidic device based on impedance flow-cytometry and electrical impedance spectroscopy that can detect and quantify the size of microparticles larger than 45 µm, such as algae and microplastics. The system is low cost ($300), portable (5 cm [Formula: see text] 5 cm), low-power (1.2 W), and easily fabricated utilizing a 3D-printer and industrial printed circuit board technology. The main novelty we demonstrate is the use of square wave excitation signal for impedance measurements with quadrature phase-sensitive detectors. A linked algorithm removes the errors associated to higher order harmonics. After validating the performance of the device for complex impedance models, we used it to detect and differentiate between polyethylene microbeads of sizes between 63 and 83 µm, and buccal cells between 45 and 70 µm. A precision of 3% is reported for the measured impedance and a minimum size of 45 µm is reported for the particle characterization.

Recent Advances in CMOS Electrochemical Biosensor Design for Microbial Monitoring: Review and Design Methodology.

Rapid, high-sensitivity, and real-time characterization of microorganisms plays a significant role in several areas, including clinical diagnosis, human healthcare, early detection of outbreaks, and the protection of living beings. Integrating microbiology and electrical engineering promises the development of low-cost, miniaturized, autonomous, and high-sensitivity sensors to quantify and characterize bacterial strains at various concentrations. Electrochemical-based biosensors are receiving particular attention in microbiological applications among the different biosensing devices. Several approaches have been adopted to design and fabricate cutting-edge, miniaturized, and portable electrochemical biosensors to track and monitor bacterial cultures in real time. These techniques differ in their sensing interface circuits and microelectrode fabrication. The goals of this review are (1) to summarize the current state of CMOS sensing circuit designs in label-free electrochemical biosensors for bacteria monitoring and (2) to discuss the material and size of the electrodes used in electrochemical biosensors in microbiological applications. In this paper, we reviewed the latest and most advanced CMOS integrated interface circuits that have recently been used in electrochemical biosensors to identify and characterize bacteria species, such as impedance spectroscopy, capacitive, amperometry, and voltammetry, etc. In addition to the interface circuit design, other crucial factors, such as the material and scale of the electrodes, must be considered to increase the sensitivity of electrochemical biosensors. Surveying the literature in this field improves our knowledge about the impact of electrode designs and materials on sensing precision and will help future designers adapt, design, and fabricate appropriate electrode configurations based on their application. Thus, we summarized the conventional microelectrode designs and materials mainly employed in microbial sensors, including interdigitated electrodes (IDEs), microelectrode arrays (MEAs), paper, and carbon-based electrodes, etc.

Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied.

The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions-often under the effect of drugs-it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant p < 0.001 differences between the datasets in diastolic BP and in 20 out of 31 features when adjusting for heart rate differences. The eight features showing the highest rank correlation ρ   >   0.40 to systolic BP in PPG-BP all displayed muted correlation levels ρ   <   0.10 in MIMIC. Regression tests showed twice higher baseline predictive power with PPG-BP than with MIMIC. Cross-dataset regression displayed a practically complete loss of predictive power for all models. The differences between the MIMIC and PPG-BP dataset exposed in this study suggest that BP estimation models based on the MIMIC dataset have reduced predictive power on the general population.

First-In-Human Study in Cancer Patients Establishing the Feasibility of Oxygen Measurements in Tumors Using Electron Paramagnetic Resonance With the OxyChip.

OBJECTIVE: The overall objective of this clinical study was to validate an implantable oxygen sensor, called the 'OxyChip', as a clinically feasible technology that would allow individualized tumor-oxygen assessments in cancer patients prior to and during hypoxia-modification interventions such as hyperoxygen breathing. METHODS: Patients with any solid tumor at ≤3-cm depth from the skin-surface scheduled to undergo surgical resection (with or without neoadjuvant therapy) were considered eligible for the study. The OxyChip was implanted in the tumor and subsequently removed during standard-of-care surgery. Partial pressure of oxygen (pO2) at the implant location was assessed using electron paramagnetic resonance (EPR) oximetry. RESULTS: Twenty-three cancer patients underwent OxyChip implantation in their tumors. Six patients received neoadjuvant therapy while the OxyChip was implanted. Median implant duration was 30 days (range 4-128 days). Forty-five successful oxygen measurements were made in 15 patients. Baseline pO2 values were variable with overall median 15.7 mmHg (range 0.6-73.1 mmHg); 33% of the values were below 10 mmHg. After hyperoxygenation, the overall median pO2 was 31.8 mmHg (range 1.5-144.6 mmHg). In 83% of the measurements, there was a statistically significant (p ≤ 0.05) response to hyperoxygenation. CONCLUSIONS: Measurement of baseline pO2 and response to hyperoxygenation using EPR oximetry with the OxyChip is clinically feasible in a variety of tumor types. Tumor oxygen at baseline differed significantly among patients. Although most tumors responded to a hyperoxygenation intervention, some were non-responders. These data demonstrated the need for individualized assessment of tumor oxygenation in the context of planned hyperoxygenation interventions to optimize clinical outcomes.

A Wireless Electro-Optic Platform for Multimodal Electrophysiology and Optogenetics in Freely Moving Rodents.

This paper presents the design and the utilization of a wireless electro-optic platform to perform simultaneous multimodal electrophysiological recordings and optogenetic stimulation in freely moving rodents. The developed system can capture neural action potentials (AP), local field potentials (LFP) and electromyography (EMG) signals with up to 32 channels in parallel while providing four optical stimulation channels. The platform is using commercial off-the-shelf components (COTS) and a low-power digital field-programmable gate array (FPGA), to perform digital signal processing to digitally separate in real time the AP, LFP and EMG while performing signal detection and compression for mitigating wireless bandwidth and power consumption limitations. The different signal modalities collected on the 32 channels are time-multiplexed into a single data stream to decrease power consumption and optimize resource utilization. The data reduction strategy is based on signal processing and real-time data compression. Digital filtering, signal detection, and wavelet data compression are used inside the platform to separate the different electrophysiological signal modalities, namely the local field potentials (1-500 Hz), EMG (30-500 Hz), and the action potentials (300-5,000 Hz) and perform data reduction before transmitting the data. The platform achieves a measured data reduction ratio of 7.77 (for a firing rate of 50 AP/second) and weights 4.7 g with a 100-mAh battery, an on/off switch and a protective plastic enclosure. To validate the performance of the platform, we measured distinct electrophysiology signals and performed optogenetics stimulation in vivo in freely moving rondents. We recorded AP and LFP signals with the platform using a 16-microelectrode array implanted in the primary motor cortex of a Long Evans rat, both in anesthetized and freely moving conditions. EMG responses to optogenetic Channelrhodopsin-2 induced activation of motor cortex via optical fiber were also recorded in freely moving rodents.

Low-Cutoff Frequency Reduction in Neural Amplifiers: Analysis and Implementation in CMOS 65 nm.

Scaling down technology demotes the parameters of AC-coupled neural amplifiers, such as increasing the low-cutoff frequency due to the short-channel effects. To improve the low-cutoff frequency, one solution is to increase the feedback capacitors' value. This solution is not desirable, as the input capacitors have to be increased to maintain the same gain, which increases the area and decreases the input impedance of the neural amplifier. We analytically analyze the small-signal behavior of the neural amplifier and prove that the main reason for the increase of the low-cutoff frequency in advanced CMOS technologies is the reduction of the input resistance of the operational transconductance amplifier (OTA). We also show that the reduction of the input resistance of the OTA is due to the increase in the gate oxide leakage in the input transistors. In this paper, we explore this fact and propose two solutions to reduce the low-cutoff frequency without increasing the value of the feedback capacitor. The first solution is performed by only simulation and is called cross-coupled positive feedback that uses pseudoresistors to provide a negative resistance to increase the input resistance of the OTA. As an advantage, only standard CMOS transistors are used in this method. Simulation results show that a low-cutoff frequency of 1.5 Hz is achieved while the midband gain is 30.4 dB at 1 V. In addition, the power consumption is 0.6 µW. In the second method, we utilize thick-oxide MOS transistors in the input differential pair of the OTA. We designed and fabricated the second method in the 65 nm TSMC CMOS process. Measured results are obtained by in vitro recordings on slices of mouse brainstem. The measurement results show that the bandwidth is between 2 Hz and 5.6 kHz. The neural amplifier has 34.3 dB voltage gain in midband and consumes 3.63 µW at 1 V power supply. The measurement results show an input-referred noise of 6.1 µV rms and occupy 0.04 mm 2 silicon area.

Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning.

Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition.

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly ( [Formula: see text]) outperforms using fine-tuning as the recalibration technique.

Development of a Dual-Wavelength Isosbestic Wireless Fiber Photometry Platform for Live Animals Studies.

Measuring neural activity from well-defined neural populations deep inside the brain has an important value in neuroscience. Fiber photometry is an important technique for evaluating neuron activity inside the brain. Besides, miniature wireless systems to record neuronal activity in a fully untethered experimental setting have recently become extremely interesting for experimenters. Still, a noise-robust wireless fiber photometry system for this purpose does not exist. Using an isosbestic excitation wavelength for recording with GCaMP6 has recently been suggested to reduce the different types of noises. We present the design of a wireless fiber photometry system at 470 nm for calcium-dependent fluorescence emission of GCaMP6 using a calcium-independent isosbestic excitation wavelength of 410 nm. Synthetic emission fluorescence light was played from a function generator to drive an LED at 530 nm at test the photometry platform. The setup has been fixed at 4.18 mW light power after linearity assessment while the analog circuit has THD of 0.35%. Then, the recorded synthetic neuronal activity was transmitted wirelessly to the base station. Finally, the isosbestic response has been aligned and removed from the calcium-dependent fluorescence signal to have a noiseless neuronal activity.

A 6.78-MHz Robust WPT System with Inductive Link Bandwidth Extended for cm-Sized Implantable Medical Devices.

This paper presents a new technique to design a robust inductive link for Wireless Power Transmission (WPT) to centimeter-sized (cm-sized) Implantable Medical Devices (IMDs). The consequence of this methodology is the bandwidth extension of utilized link to maximize both Power Delivered to Load (PDL) and Power Transfer Efficiency (PTE). Design, circuit implementation, and In-vivo validation experimental results are reported. Different conditions of tests, including three misalignment experiments, are performed with the proposed WPT system to prove the concept of a robust inductive link. The geometry of the Transmitter (Tx) and Receiver (Rx) coils are considered as well as the operating frequency (fp) of the WPT system. The Tx and Rx coils are crafted in a circulated shape with the diameters of 5 and 2.5 cm, respectively. Achieved PTE and PDL are in the range of 0.82%-25.7% and 44.4mW-720mW, respectively. The distance between Tx and Rx coils varies in the range of 1.5 to 4cm.

Multimodal Electrophysiological Signal Measurement using a New Flexible and Conductive Polymer Fiber-electrode.

A new multi-material polymer fiber electrode has been developed for smart clothing applications. The conductive fiber is optimized for bipotential measurements such as surface electromyogram (sEMG) and electrocardiogram (ECG). The main benefit of this fiber is its flexibility and being a dry and non-obtrusive electrode. It can be directly integrated into a garment to make a smart textile for real time biopoten-tial monitoring. A customized wireless electronic system has been developed to acquire electrophysiological signal from the fiber. The receiver base station is connected to a PC host running Matlab. The multi-material polymer fiber electrode recording setting were first optimized in length and inter-electrode distance by recording different sEMG signals. The typical sEMG signal to noise ratio ranges from 19.1 dB to 33.9 dB depending on the geometry. These value are comparable with those obtained with Ag/AgCl electrodes and dry electrode-base commercial system such as Delsys Trigno. The frequency domain analysis obtained from the power spectral density reveals that the new flexible fiber-electrode enables high sEMG signals recording quality while being suitable for integration in smart clothing fabric. A muscle fatigue analysis and ECG recording are also presented in this study. The multi-material polymer fiber electrodes demonstrate a viable solution for sEMG and ECG data acquisition.

Wearable Wireless-Enabled Oscillometric Sphygmomanometer: A Flexible Ambulatory Tool for Blood Pressure Estimation.

This article presents the design of an unobtrusive and wireless-enabled blood pressure (BP) monitoring system that is suitable for ambulatory use. By adopting low-profile electromechanical actuators and a compact printed circuit board design, this lightweight device can be worn directly on the occlusive cuff, therefore eliminating the need of a long and obtrusive tubing interconnect between the device and the cuff, as seen in traditional ambulatory BP monitors (ABPM). Instead of executing the BP estimation algorithm directly on the device, the proposed design rather sends the raw oscillometric signal through a Bluetooth Low Energy link, thus granting any Bluetooth-enabled device to gather and process the signal using a dedicated application. This in turn allows to assess several BP estimation algorithms found in the literature without being limited by the device resources. Three of them were tested with the designed prototype and validated with a reference equipment on 11 subjects. Overall, two of the algorithms revealed a mean absolute difference with the reference equipment of less than 5 mmHg and almost zero bias along with a standard deviation of less than 6 mmHg. Reproducibility results shown a mean difference between successive measurements of less than 3.1 mmHg and a standard deviation of less than 2.4 mmHg. The assembled prototype dimensions are 63.8 × 134.8 × 24.8 mm and features an autonomy of 63.1 hours. Comparison with commercial ABPM devices shown that the proposed design is 18% to 33% smaller volume-wise, 5% to 27% weight-wise and height is reduced by 17% to 25%.

Source-Detector Spectral Pairing-Related Inaccuracies in Pulse Oximetry: Evaluation of the Wavelength Shift.

Pulse oximetry enables oxygen saturation estimation ( S p O 2) non-invasively in real time with few components and modest processing power. With the advent of affordable development kits dedicated to the monitoring of biosignals, capabilities once reserved to hospitals and high-end research laboratories are becoming accessible for rapid prototyping. While one may think that medical-grade equipment differs greatly in quality, surprisingly, we found that the performance requirements are not widely different from available consumer-grade components, especially regarding the photodetection module in pulse oximetry. This study investigates how the use of candidate light sources and photodetectors for the development of a custom S p O 2 monitoring system can lead to inaccuracies when using the standard computational model for oxygen saturation without calibration. Following the optical characterization of selected light sources, we compare the extracted parameters to the key features in their respective datasheet. We then quantify the wavelength shift caused by spectral pairing of light sources in association with photodetectors. Finally, using the widely used approximation, we report the resulting absolute error in S p O 2 estimation and show that it can lead up to 8% of the critical 90-100% saturation window.