934 research outputs found
Non-invasive urinary bladder volume estimation with artefact-suppressed bio-impedance measurements
Urine output is a vital parameter to gauge kidney health. Current monitoring
methods include manually written records, invasive urinary catheterization or
ultrasound measurements performed by highly skilled personnel. Catheterization
bears high risks of infection while intermittent ultrasound measures and manual
recording are time consuming and might miss early signs of kidney malfunction.
Bioimpedance (BI) measurements may serve as a non-invasive alternative for
measuring urine volume in vivo. However, limited robustness have prevented its
clinical translation. Here, a deep learning-based algorithm is presented that
processes the local BI of the lower abdomen and suppresses artefacts to measure
the bladder volume quantitatively, non-invasively and without the continuous
need for additional personnel. A tetrapolar BI wearable system called ANUVIS
was used to collect continuous bladder volume data from three healthy subjects
to demonstrate feasibility of operation, while clinical gold standards of
urodynamic (n=6) and uroflowmetry tests (n=8) provided the ground truth.
Optimized location for electrode placement and a model for the change in BI
with changing bladder volume is deduced. The average error for full bladder
volume estimation and for residual volume estimation was -29 +/-87.6 ml, thus,
comparable to commercial portable ultrasound devices (Bland Altman analysis
showed a bias of -5.2 ml with LoA between 119.7 ml to -130.1 ml), while
providing the additional benefit of hands-free, non-invasive, and continuous
bladder volume estimation. The combination of the wearable BI sensor node and
the presented algorithm provides an attractive alternative to current standard
of care with potential benefits in providing insights into kidney function
Low-power Wearable Healthcare Sensors
Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors
Novel Methods for Weak Physiological Parameters Monitoring.
M.S. Thesis. University of Hawaiʻi at Mānoa 2017
Towards Bio-impedance Based Labs: A Review
In this article, some of the main contributions to BI (Bio-Impedance) parameter-based systems for medical, biological and
industrial fields, oriented to develop micro laboratory systems are summarized. These small systems are enabled by the development
of new measurement techniques and systems (labs), based on the impedance as biomarker. The electrical properties of the life mater
allow the straightforward, low cost and usually non-invasive measurement methods to define its status or value, with the possibility
to know its time evolution. This work proposes a review of bio-impedance based methods being employed to develop new LoC
(Lab-on-a-Chips) systems, and some open problems identified as main research challenges, such as, the accuracy limits of
measurements techniques, the role of the microelectrode-biological impedance modeling in measurements and system portability
specifications demanded for many applications.Spanish founded Project: TEC 2013-46242-C3-1-P: Integrated Microsystem for Cell Culture AssaysFEDE
Smart Bioimpedance Spectroscopy Device for Body Composition Estimation
The purpose of this work is to describe a first approach to a smart bioimpedance
spectroscopy device for its application to the estimation of body composition. The proposed
device is capable of carrying out bioimpedance measurements in multiple configurable frequencies,
processing the data to obtain the modulus and the bioimpedance phase in each of the frequencies,
and transmitting the processed information wirelessly. Another novelty of this work is a new
algorithm for the identification of Cole model parameters, which is the basis of body composition
estimation through bioimpedance spectroscopy analysis. Against other proposals, the main
advantages of the proposed method are its robustness against parasitic effects by employing
an extended version of Cole model with phase delay and three dispersions, its simplicity and
low computational load. The results obtained in a validation study with respiratory patients
show the accuracy and feasibility of the proposed technology for bioimpedance measurements.
The precision and validity of the algorithm was also proven in a validation study with peritoneal
dialysis patients. The proposed method was the most accurate compared with other existing
algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided
better approximations to the bioimpedance values than a reference device.Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15/00306Junta de Andalucía PIN-0394-2017Unión Europea "FRAIL
Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits.
Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension
Low Power Circuits for Smart Flexible ECG Sensors
Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research.
A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording.
A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops.
A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W
Towards Accurate Dielectric Property Retrieval of Biological Tissues for Blood Glucose Monitoring
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components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10/1109/TMTT.2014.2365019
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