Medical Radar Signal Dataset for Non-Contact Respiration and Heart Rate Measurement.
Ontology highlight
ABSTRACT:
SUBMITTER: Edanami K
PROVIDER: S-EPMC8688557 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Vital sign monitoring in neonates requires adhesive electrodes, which often damage fragile newborn skin. Because impulse radio ultrawideband (IR-UWB) radar has been reported to recognize chest movement without contact in adult humans, IR-UWB may be used to measure respiratory rates (RRs) in a non-contact fashion. We investigated the feasibility of radar sensors for respiration monitoring in neonates without any respiratory support to compare the accuracy and reliability of radar measurements with those of conventional impedance pneumography measurements. In the neonatal intensive care unit, RRs were measured using radar (RRRd) and impedance pneumography (RRIP) simultaneously. The neonatal voluntary movements were measured using the radar sensor and categorized into three levels (low [M0], intermediate [M1] and high [M2]). RRRd highly agreed with RRIP (r = 0.90; intraclass correlation coefficient [ICC] = 0.846 [0.835-0.856]). For the M0 movement, there was good agreement between RRRd and RRIP (ICC = 0.893; mean bias -0.15 [limits of agreement (LOA) -9.6 to 10.0]). However, the agreement was slightly lower for the M1 (ICC = 0.833; mean bias = 0.95 [LOA -11.4 to 13.3]) and M2 (ICC = 0.749; mean bias = 3.04 [LOA -9.30 to 15.4]) movements than for the M0 movement. In conclusion, IR-UWB radar can provide accurate and reliable estimates of RR in neonates in a non-contact fashion. The performance of radar measurements could be affected by neonate movement.
| S-EPMC6599793 | biostudies-literature
Project description:We discovered that impulse-radio ultra-wideband (IR-UWB) radar could recognize cardiac motions in a non-contact fashion. Therefore, we measured the heart rate (HR) and rhythms using an IR-UWB radar sensor and evaluated the validity and reliability of the measurements in comparison to electrocardiography. The heart beats were measured in 6 healthy volunteers (18 samples) with normal sinus rhythm (NSR) and 16 patients (36 samples) with atrial fibrillation (AF) using both an IR-UWB radar sensor and electrocardiography simultaneously. The participants hold their breath for 20?seconds during the data acquisition. In subjects with NSR, there was excellent agreement of HR (intraclass correlation coefficient [ICC] 0.856), average R-R interval (ICC 0.997) and individual R-R intervals between the two methods (ICC 0.803). In subjects with AF, HR (ICC 0.871) and average R-R interval (ICC 0.925) from the radar sensor also agreed well with those from electrocardiography, though there was a small disagreement in the individual R-R intervals between the two methods (ICC 0.697). The rhythms computed by the signal-processing algorithm showed good agreement between the two methods (Cohen's Kappa 0.922). The IR-UWB radar sensor is precise and accurate for assessing HR and rhythms in a non-contact fashion.
| S-EPMC6115337 | biostudies-literature
Project description:Currently, many imaging photoplethysmography (IPPG) researches have reported non-contact measurements of physiological parameters, such as heart rate (HR), respiratory rate (RR), etc. However, it is accepted that only HR measurement has been mature for applications, and other estimations are relatively incapable for reliable applications. Thus, it is worth keeping on persistent studies. Besides, there are some issues commonly involved in these approaches need to be explored further. For example, motion artifact attenuation, an intractable problem, which is being attempted to be resolved by sophisticated video tracking and detection algorithms.This paper proposed a blind source separation-based method that could synchronously measure RR and HR in non-contact way. A dual region of interest on facial video image was selected to yield 6-channels Red/Green/Blue signals. By applying Second-Order Blind Identification algorithm to those signals generated above, we obtained 6-channels outputs that contain blood volume pulse (BVP) and respiratory motion artifact. We defined this motion artifact as respiratory signal (RS). For the automatic selections of the RS and BVP among these outputs, we devised a kurtosis-based identification strategy, which guarantees the dynamic RR and HR monitoring available.The experimental results indicated that, the estimation by the proposed method has an impressive performance compared with the measurement of the commercial medical sensors.The proposed method achieved dynamic measurement of RR and HR, and the extension and revision of it may have the potentials for more physiological signs detection, such as heart rate variability, eye blinking, nose wrinkling, yawn, as well as other muscular movements. Thus, it might provide a promising approach for IPPG-based applications such as emotion computation and fatigue detection, etc.
| S-EPMC5439118 | biostudies-literature
Project description:This study aimed to apply radar technology to a large quadruped animal. We first developed a non-contact respiration measurement system using millimeter-wave array radar for a horse in standing position. Specifically, we measured the respiration of a stationary domestic horse in stables. Simultaneously, we measured the respiration rate using infrared thermography and developed a method for analyzing the radar information while verifying the rate of agreement. Our results suggested that the radar technology detected breathing and accurately measured the respiration of a horse, despite variation in the breathing frequency. To the best of our knowledge, this is the first study to apply a non-contact respiration measurement system using millimeter-wave array radar has been applied to large animals in an upright position, thereby demonstrating its potential application in animal husbandry and welfare.
| S-EPMC9586033 | biostudies-literature
Project description:Conventional respiration measurement requires a separate device and/or can cause discomfort, so it is difficult to perform routinely, even for patients with respiratory diseases. The development of contactless respiration measurement technology would reduce discomfort and help detect and prevent fatal diseases. Therefore, we propose a respiration measurement method using a learning-based region-of-interest detector and a clustering-based respiration pixel estimation technique. The proposed method consists of a model for classifying whether a pixel conveys respiration information based on its variance and a method for classifying pixels with clear breathing components using the symmetry of the respiration signals. The proposed method was evaluated with the data of 14 men and women acquired in an actual environment, and it was confirmed that the average error was within approximately 0.1 bpm. In addition, a Bland-Altman analysis confirmed that the measurement result had no error bias, and regression analysis confirmed that the correlation of the results with the reference is high. The proposed method, designed to be inexpensive, fast, and robust to noise, is potentially suitable for practical use in clinical scenarios.
| S-EPMC8157066 | biostudies-literature
Project description:Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis.
| S-EPMC6536597 | biostudies-literature
Project description:Heart rate (HR) is an important parameter in the study of the developmental physiology of chicken embryos and a crucial indicator of dead or live embryo grading in artificial incubation processes. A non-invasive HR measurement technique is required for long-term and routine HR assessment with minimal influence on embryo development. Accordingly, in this study, a non-invasive HR measurement technique of chicken embryos using a smartphone is demonstrated. The detection method of the proposed device is based on the photoplethysmography principle in which a smartphone camera is used for video recording, and the chicken embryonic HR is obtained from the recorded video images using a custom Android application. We used a smartphone to measure the embryonic HR of 60 native chicken eggs and found that it can measure the chicken embryonic HR from day 4 to day 20. The proposed smartphone HR device will be beneficial for scientific research and industrial applications. With internet connectivity, users can utilize their smartphone to measure the HR, display, share, and store the results.
| S-EPMC6891743 | biostudies-literature
Project description:This paper describes a dataset acquired from 41 volunteers performing 16 Activities of daily livings (ADLs) and 8 Falls repeated 5 times. This data was collected using a custom wrist-worn end device. The dataset has data collected from Inertial measurement unit (IMU) and heart-rate sensors. The end device is built using Qualcomm Snapdragon 820c System on Chip (SoC) interfaced to the sensors via Interconnect Integrated Circuit (I2C) protocol. The data was sampled for every activity at a rate of 20 Hz for the motion sensors and at a rate of 1 Hz for the heart-rate sensor. The motion sensor comprised of a triaxial accelerometer, triaxial gyroscope, triaxial magnetometer and a linear accelerometer. The heart-rate sensor was medical grade and all sensors were calibrated for the wrist -worn position. The dataset is available on this website https://shamanx86.github.io/fall_detection_data/ and https://doi.org/10.5281/zenodo.10013090.
| S-EPMC10709028 | biostudies-literature
Project description:Physical fatigue can be assessed using heart rate variability (HRV). We measured HRV at rest and in a fatigued state using impulse-radio ultra wideband (IR-UWB) radar in a noncontact fashion and compared the measurements with those obtained using electrocardiography (ECG) to assess the reliability and validity of the radar measurements. HRV was measured in 15 subjects using radar and ECG simultaneously before (rest for 10 min before exercise) and after a 20-min exercise session (fatigue level 1 for 0–9 min; fatigue level 2 for 10–19 min; recovery for ≥ 20 min after exercise). HRV was analysed in the frequency domain, including the low-frequency component (LF), high-frequency component (HF) and LF/HF ratio. The LF/HF ratio measured using radar highly agreed with that measured using ECG during rest (ICC = 0.807), fatigue-1 (ICC = 0.712), fatigue-2 (ICC = 0.741) and recovery (ICC = 0.764) in analyses using intraclass correlation coefficients (ICCs). The change pattern in the LH/HF ratios during the experiment was similar between radar and ECG. The subject’s body fat percentage was linearly associated with the time to recovery from physical fatigue (R2 = 0.96, p < 0.001). Our results demonstrated that fatigue and rest states can be distinguished accurately based on HRV measurements using IR-UWB radar in a noncontact fashion.
| S-EPMC9392064 | biostudies-literature
Project description:While full-night polysomnography is the gold standard for the diagnosis of obstructive sleep apnea, its limitations include a high cost and first-night effects. This study developed an algorithm for the detection of respiratory events based on impulse-radio ultra-wideband radar and verified its feasibility for the diagnosis of obstructive sleep apnea. A total of 94 subjects were enrolled in this study (23 controls and 24, 14, and 33 with mild, moderate, and severe obstructive sleep apnea, respectively). Abnormal breathing detected by impulse-radio ultra-wideband radar was defined as a drop in the peak radar signal by ?30% from that in the pre-event baseline. We compared the abnormal breathing index obtained from impulse-radio ultra-wideband radar and apnea-hypopnea index (AHI) measured from polysomnography. There was an excellent agreement between the Abnormal Breathing Index and AHI (intraclass correlation coefficient?=?0.927). The overall agreements of the impulse-radio ultra-wideband radar were 0.93 for Model 1 (AHI???5), 0.91 for Model 2 (AHI???15), and 1 for Model 3 (AHI???30). Impulse-radio ultra-wideband radar accurately detected respiratory events (apneas and hypopneas) during sleep without subject contact. Therefore, impulse-radio ultra-wideband radar may be used as a screening tool for obstructive sleep apnea.
| S-EPMC7093464 | biostudies-literature