Publications by authors named "Mendelson Y"

Article Synopsis
  • Deep learning has shown promise in enhancing the diagnosis of acutely decompensated heart failure (ADHF) through the analysis of ECG data, but past efforts have mainly relied on predefined ECG patterns in controlled settings.
  • This study created a new model, ECGX-Net, which combines raw ECG data and bioimpedance data from wearable devices to improve ADHF prediction using deep feature learning techniques.
  • The results indicated that ECGX-Net achieved a high precision in predicting ADHF (94% precision, 79% recall), while a simpler model using DenseNet121 was better for high recall tasks (98% recall, 80% precision), showcasing different strengths in ECG data analysis.
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Objective: Rises in the incidence of pressure ulcers are increasingly prevalent in an aging population. Pressure ulcers are painful, are associated with increased morbidity and mortality, increase the risk for secondary infections and inpatient stay, and adds $26.8 billion annually to the healthcare costs of the USA.

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Pressure ulcers are increasingly prevalent in an aging population. The most commonly used method of pressure ulcer prevention is pressure off-loading achieved by physically turning bedbound patients or by using expensive, single application devices such as wheelchair cushions. Our aim is to approach the problem of pressure ulcer prevention in a new way: a wireless sensor worn by the patient at locations susceptible to pressure injury.

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With an aging population, the incidence and prevalence of wound problems is on the rise. Bedsores (also known as pressure ulcers or decubitus ulcers) are painful, take months to heal, and, for many patients, never do, leading to other health problems. The condition has become so acute that treating bedsores is now a significant burden on the healthcare system.

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Objective: Pulse oximetry, a widely accepted method for non-invasive estimation of arterial oxygen saturation (SpO) and pulse rate (PR), is increasingly being adapted for mobile applications. Previous work in mitigating motion artefact, which corrupts the photoplethysmogram (PPG) used in pulse oximetry, has focused on reducing noise using signal processing algorithms or through sensor design that controlled only one variable at a time. In this work, we have investigated the effect of several variables such as sensor weight, relative motion, placement, and contact force against the skin that can impact motion artefact independently or by interacting with each other.

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Objective: The purpose of this paper is to demonstrate that a new algorithm for estimating arterial oxygen saturation can be effective even with data corrupted by motion artifacts (MAs).

Methods: OxiMA, an algorithm based on the time-frequency components of a photoplethysmogram (PPG), was evaluated using 22-min datasets recorded from 10 subjects during voluntarily-induced hypoxia, with and without subject-induced MAs. A Nellcor OxiMax transmission sensor was used to collect an analog PPG while reference oxygen saturation and pulse rate (PR) were collected simultaneously from an FDA-approved Masimo SET Radical RDS-1 pulse oximeter.

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Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L.

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Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data.

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Photoplethysmographic (PPG) waveforms are used to acquire pulse rate (PR) measurements from pulsatile arterial blood volume. PPG waveforms are highly susceptible to motion artifacts (MA), limiting the implementation of PR measurements in mobile physiological monitoring devices. Previous studies have shown that multichannel photoplethysmograms can successfully acquire diverse signal information during simple, repetitive motion, leading to differences in motion tolerance across channels.

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Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis.

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Motion and noise artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA detection method which can provide a clean vs. corrupted decision on each successive PPG segment.

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We introduce a new method to reconstruct motion and noise artifact (MNA) contaminated photoplethysmogram (PPG) data. A method to detect MNA corrupted data is provided in a companion paper. Our reconstruction algorithm is based on an iterative motion artifact removal (IMAR) approach, which utilizes the singular spectral analysis algorithm to remove MNA artifacts so that the most accurate estimates of uncorrupted heart rates (HRs) and arterial oxygen saturation (SpO2) values recorded by a pulse oximeter can be derived.

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Motion and noise artifacts (MNA) have been a serious obstacle in realizing the potential of Photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a statistical approach based on the computation of kurtosis and Shannon Entropy (SE) for the accurate detection of MNA in PPG data. The MNA detection algorithm was verified on multi-site PPG data collected from both laboratory and clinical settings.

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We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal.

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Accurate and early detection of blood volume loss would greatly improve intraoperative and trauma care. This study has attempted to determine early diagnostic and quantitative markers for blood volume loss by analyzing photoplethysmogram (PPG) data from ear, finger and forehead sites with our high-resolution time-frequency spectral (TFS) technique in spontaneously breathing healthy subjects (n = 11) subjected to lower body negative pressure (LBNP). The instantaneous amplitude modulations present in heart rate (AM HR) and breathing rate (AMBR) band frequencies of PPG signals were calculated from the high-resolution TFS.

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Wearable physiological monitoring using a pulse oximeter would enable field medics to monitor multiple injuries simultaneously, thereby prioritizing medical intervention when resources are limited. However, a primary factor limiting the accuracy of pulse oximetry is poor signal-to-noise ratio since photoplethysmographic (PPG) signals, from which arterial oxygen saturation (SpO2) and heart rate (HR) measurements are derived, are compromised by movement artifacts. This study was undertaken to quantify SpO2 and HR errors induced by certain motion artifacts utilizing accelerometry-based adaptive noise cancellation (ANC).

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Despite steady progress in the miniaturization of pulse oximeters over the years, significant challenges remain since advanced signal processing must be implemented efficiently in real-time by a relatively small size wearable device. The goal of this study was to investigate several potential digital signal processing algorithms for computing arterial oxygen saturation (SpO(2)) and heart rate (HR) in a battery-operated wearable reflectance pulse oximeter that is being developed in our laboratory for use by medics and first responders in the field. We found that a differential measurement approach, combined with a low-pass filter (LPF), yielded the most suitable signal processing technique for estimating SpO(2), while a signal derivative approach produced the most accurate HR measurements.

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Steady progress has been made towards the development of a reliable wearable pulse oximeter to aid first responders in remote monitoring and triage operations. This study was undertaken to assess how varying contact pressures affects the photoplethysmographic (PPG) signal, and arterial oxygen saturation (SpO2) and heart rate (HR) measurement errors during motion artifact inducing activity. The study revealed that contact pressures ranging from 8-12 kPa resulted in the largest PPG amplitude for a reflectance sensor attached to the forehead region above the eye, although the signal-to-noise ratio (SNR) did not improve significantly.

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To save life, casualty care requires that trauma injuries are accurately and expeditiously assessed in the field. This paper describes the initial bench testing of a wireless wearable pulse oximeter developed based on a small forehead mounted sensor. The battery operated device employs a lightweight optical reflectance sensor and incorporates an annular photodetector to reduce power consumption.

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The integration of multiple vital physiological measurements could help combat medics and field commanders to better predict a soldier's health condition and enhance their ability to perform remote triage procedures. In this paper we demonstrate the feasibility of extracting accurate breathing rate information from a photoplethysmographic signal that was recorded by a reflectance pulse oximeter sensor mounted on the forehead and subsequently processed by a simple time domain filtering and frequency domain Fourier analysis.

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The performance of current reflectance pulse oximeters is hindered by poor signal-to-noise ratio. To overcome this problem a new reflectance oximeter has been developed with a sensor which consists of three LEDs and two continuous photodetector rings placed equidistant from the center of the LEDs. In addition, ultra low noise electronics and adaptive algorithm assure improved performance.

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The purpose of this investigation was to identify the type of pH-reference electrode combination that is the most suitable and reliable in clinical applications involving long-term postoperative monitoring of microvascular reconstructive transplants and diagnosis of compartment syndrome. Four types of pH-sensing devices were chosen for the study: a standard glass pH electrode, a polymer-based pH electrode, an ISFET pH sensor, and a fiberoptic pH sensor. Various combinations of electrodes were tested in vitro for typically four days.

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Noninvasive measurement of arterial oxygen saturation (SaO2) by pulse oximetry is widely acknowledged to be one of the most important technological advances in monitoring clinical patients. Pulse oximeters compute SaO2 by measuring differences in the visible and near infrared absorbances of fully oxygenated and deoxygenated arterial blood. Unlike clinical blood gas analyzers, which require a sample of blood from the patient and can provide only intermittent measurement of patient oxygenation, pulse oximeters provide continuous, safe, and instantaneous measurement of blood oxygenation.

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In in-vivo animal experiments, the authors evaluated the feasibility of measuring arterial oxyhemoglobin saturation (SaO2) noninvasively during simulated delivery conditions with a skin-reflectance pulse oximeter sensor attached to the fetal scalp. The optical reflectance sensor consisted of three pairs of red and infrared light-emitting diodes and a concentric array of six identical photodiodes. Two prototype sensor assemblies, incorporating different means of attachment to the scalp, were evaluated.

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