18,605 research outputs found

    Heart Rate Estimation During Physical Exercise Using Wrist-Type Ppg Sensors

    Get PDF
    Accurate heart rate monitoring during intense physical exercise is a challenging problem due to the high levels of motion artifacts (MA) in photoplethysmography (PPG) sensors. PPG is a non-invasive optical sensor that is being used in wearable devices to measure blood flow changes using the property of light reflection and absorption, allowing the extraction of vital signals such as the heart rate (HR). However, the sensor is susceptible to MA which increases during physical activity. This occurs since the frequency range of movement and HR overlaps, difficulting correct HR estimation. For this reason, MA removal has remained an active topic under research. Several approaches have been developed in the recent past and among these, a Kalman filter (KF) based approach showed promising results for an accurate estimation and tracking using PPG sensors. However, this previous tracker was demonstrated for a particular dataset, with manually tuned parameters. Moreover, such trackers do not account for the correct method for fusing data. Such a custom approach might not perform accurately in practical scenarios, where the amount of MA and the heart rate variability (HRV) depend on numerous, unpredictable factors. Thus, an approach to automatically tune the KF based on the Expectation-Maximization (EM) algorithm, with a measurement fusion approach is developed. The applicability of such a method is demonstrated using an open-source PPG database, as well as a developed synthetic generation tool that models PPG and accelerometer (ACC) signals during predetermined physical activities

    Design of a wearable sensor system for neonatal seizure monitoring

    Get PDF

    Design of a wearable sensor system for neonatal seizure monitoring

    Get PDF

    Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

    Get PDF
    Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

    Southwest Research Institute assistance to NASA in biomedical areas of the technology

    Get PDF
    Significant applications of aerospace technology were achieved. These applications include: a miniaturized, noninvasive system to telemeter electrocardiographic signals of heart transplant patients during their recuperative period as graded situations are introduced; and economical vital signs monitor for use in nursing homes and rehabilitation hospitals to indicate the onset of respiratory arrest; an implantable telemetry system to indicate the onset of the rejection phenomenon in animals undergoing cardiac transplants; an exceptionally accurate current proportional temperature controller for pollution studies; an automatic, atraumatic blood pressure measurement device; materials for protecting burned areas in contact with joint bender splints; a detector to signal the passage of animals by a given point during ecology studies; and special cushioning for use with below-knee amputees to protect the integrity of the skin at the stump/prosthesis interface

    Characterizing the Noise Associated with Sensor Placement and Motion Artifacts and Overcoming its Effects for Body-worn Physiological Sensors

    Get PDF
    Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts. In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches

    Motion-resistant pulse oximetry

    Get PDF
    The measurement of vital signs ? such as peripheral capillary oxygen saturation (SpO2) and heart rate (HR) levels ? by a pulse oximeter is studied. The pulse oximeter is a non-invasive device that measures photoplethysmography (PPG) signals and extracts vital signs from them. However, the quality of the PPG signal measured by oximetry sensors is known to deteriorate in the presence of substantial human and sensor movements contributing to the measurement noise. Methods to suppress such noise from PPG signals measured by an oximeter and to calculate the associated vital signs with high accuracy even when the wearer is under substantial motion are presented in this study. The spectral components of the PPG waveform are known to appear at a fundamental frequency that corresponds to the participant\u27s HR and at its harmonics. To match this signal, a time-varying comb filter tuned to the participant\u27s HR is employed. The filter captures the HR components and eliminates most other artifacts. A significant improvement in the accuracy of SpO2 calculated from the comb-filtered PPG signals is observed, when tested on data collected from human participants while they are at rest and while they are exercising. In addition, an architecture that integrates SpO2 levels from multiple PPG channels mounted on different parts of the wearer\u27s arm is presented. The SpO2 levels are integrated using a Kalman filter that uses past measurements and modeling of the SpO2 dynamics to attenuate the effect of the motion artifacts. Again, data collected from human participants while they are at rest and while they are exercising are used. The integrated SpO2 levels are shown to be more accurate and reliable than those calculated from individual channels. Motion-resistant algorithms typically require an additional noise reference signal to produce high quality vital signs such as HR. A framework that employs PPG sensors only ? one in the green and one in the infrared spectrum ? to compute high quality HR levels is developed. Our framework is tested on experimental data collected from human participants while at rest and while running at various speeds. Our PPG-only framework generates HR levels with high accuracy and low computational complexity as compared to leading HR calculation methods in the literature that require the availability of a noise reference signal. The methods for SpO2 and HR calculation presented in this study are desirable since (1) they yield high accuracy in estimating vital signs under substantial level of motion artifacts and (2) they are computationally efficient, (and therefore are capable to be implemented in wearable devices)

    Signal processing techniques for cardiovascular monitoring applications using conventional and video-based photoplethysmography

    Get PDF
    Photoplethysmography (PPG)-based monitoring devices will probably play a decisive role in healthcare environment of the future, which will be preventive, predictive, personalized and participatory. Indeed, this optical technology presents several practical advantages over gold standard methods based on electrocardiography, because PPG wearable devices can be comfortably used for long-term continuous monitoring during daily life activities. Contactless video-based PPG technique, also known as imaging photoplethysmography (iPPG), has also attracted much attention recently. In that case, the cardiac pulse is remotely measured from the subtle skin color changes resulting from the blood circulation, using a simple video camera. PPG/iPPG have a lot of potential for a wide range of cardiovascular applications. Hence, there is a substantial need for signal processing techniques to explore these applications and to improve the reliability of the PPG/iPPG-based parameters. \par A part of the thesis is dedicated to the development of robust processing schemes to estimate heart rate from the PPG/iPPG signals. The proposed approaches were built on adaptive frequency tracking algorithms that were previously developed in our group. These tools, based on adaptive band-pass filters, provide instantaneous frequency estimates of the input signal(s) with a very low time delay, making them suitable for real-time applications. In case of conventional PPG, a prior adaptive noise cancellation step involving the use of accelerometer signals was also necessary to reconstruct clean PPG signals during the regions corrupted by motion artifacts. Regarding iPPG, after comparing different regions of interest on the subject face, we hypothesized that the simultaneous use of different iPPG signal derivation methods (i.e. methods to derive the iPPG time series from the pixel values of the consecutive frames) could be advantageous. Methods to assess signal quality online and to incorporate it into instantaneous frequency estimation were also examined and successfully applied to improve system reliability. \par This thesis also explored different innovative applications involving PPG/iPPG signals. The detection of atrial fibrillation was studied. Novel features derived directly from the PPG waveforms, designed to reflect the morphological changes observed during arrhythmic episodes, were proposed and proven to be successful for atrial fibrillation detection. Arrhythmia detection and robust heart rate estimation approaches were combined in another study aimed at reducing the number of false arrhythmia alarms in the intensive care unit by exploiting signals from independent sources, including PPG. Evaluation on a hidden dataset demonstrated that the number of false alarms was drastically reduced while almost no true alarm was suppressed. Finally, other aspects of the iPPG technology were examined, such as the measurement of pulse rate variability indexes from the iPPG signals and the estimation of respiratory rate from the iPPG interbeat intervals
    • …
    corecore