Abstract This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously extracted using THz waves based on the established principles in electronics and optics. The first fundamental principle is micro-Doppler motion effect. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave) for breathe rate and heart rate detection. The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). Herein, we introduce the concept of terahertz-wave-plethysmography (TPG), which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle. The TPG principle is justified by scientific deduction, electromagnetic wave simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by the Terahertz Electronics Lab at Arizona State University, Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and standard deviation 1.08 BPM. The results validate the feasibility of TPG for direct pulse monitoring. A comparative study on pulse sensitivity is conducted between TPG and rPPG. The results indicate that the TPG contains more pulsatile information from the forehead BOI than that in the rPPG signals in regular office lighting condition and thus generate better heart rate estimation statistic in the form of empirical cumulative distribution function of HR estimation error. Last but not least, TPG penetrability test for covered skin is demonstrated using two types of garment materials commonly used in daily life.
more »
« less
Using smart speakers to contactlessly monitor heart rhythms
Heart rhythm assessment is indispensable in diagnosis and management of many cardiac conditions and to study heart rate variability in healthy individuals. We present a proof-of-concept system for acquiring individual heart beats using smart speakers in a fully contact-free manner. Our algorithms transform the smart speaker into a short-range active sonar system and measure heart rate and inter-beat intervals (R-R intervals) for both regular and irregular rhythms. The smart speaker emits inaudible 18–22 kHz sound and receives echoes reflected from the human body that encode sub-mm displacements due to heart beats. We conducted a clinical study with both healthy participants and hospitalized cardiac patients with diverse structural and arrhythmic cardiac abnormalities including atrial fibrillation, flutter and congestive heart failure. Compared to electrocardiogram (ECG) data, our system computed R-R intervals for healthy participants with a median error of 28 ms over 12,280 heart beats and a correlation coefficient of 0.929. For hospitalized cardiac patients, the median error was 30 ms over 5639 heart beats with a correlation coefficient of 0.901. The increasing adoption of smart speakers in hospitals and homes may provide a means to realize the potential of our non-contact cardiac rhythm monitoring system for monitoring of contagious or quarantined patients, skin sensitive patients and in telemedicine settings.
more » « less- Award ID(s):
- 1812559
- NSF-PAR ID:
- 10216714
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Biology
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2399-3642
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Cardiovascular diseases are the leading cause of death globally. Noninvasive, accurate, and continuous cardiovascular monitoring can enable the preemptive detection of heart diseases and timely intervention to prevent serious cardiac complications. However, unobtrusive, ambulatory, and comprehensive cardiac monitoring is still a challenge as conventional electronics are rigid, heavy, or consume too much power for long‐term measurement. This work presents a thin (200 µm), stretchable (20%), lightweight (2.5 g), wireless, and low‐power (<3 mW) cardiac monitoring device that conforms to the human chest like a temporary tattoo sticker, correspondingly known as an e‐tattoo. This chest e‐tattoo features dual‐mode electro‐mechanical sensing—bio‐electric cardiac signals via electrocardiography and mechanical cardiac rhythm via seismocardiography. A unique peripheral synchronization strategy between the two sensors enables the measurement of systolic time intervals like the pre‐ejection period and the left ventricular ejection time with high accuracy (error = −0.44 ± 8.74 ms) while consuming very low power. The e‐tattoo is validated against clinically approved gold‐standard instruments on five human subjects. The good wearability and low power consumption of this e‐tattoo permit 24‐h continuous ambulatory monitoring.
-
Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).more » « less
-
more » « less
Purpose To develop and evaluate a cardiac phase‐resolved myocardial T1mapping sequence.
Methods The proposed method for temporally resolved parametric assessment of Z‐magnetization recovery (TOPAZ) is based on contiguous fast low‐angle shot imaging readout after magnetization inversion from the pulsed steady state. Thereby, segmented k‐space data are acquired over multiple heartbeats, before reaching steady state. This results in sampling of the inversion‐recovery curve for each heart phase at multiple points separated by an R‐R interval. Joint T1and
estimation is performed for reconstruction of cardiac phase‐resolved T1and maps. Sequence parameters are optimized using numerical simulations. Phantom and in vivo imaging are performed to compare the proposed sequence to a spin‐echo reference and saturation pulse prepared heart rate–independent inversion‐recovery (SAPPHIRE) T1mapping sequence in terms of accuracy and precision. Results In phantom, TOPAZ T1values with integrated
correction are in good agreement with spin‐echo T1values (normalized root mean square error = 4.2%) and consistent across the cardiac cycle (coefficient of variation = 1.4 ± 0.78%) and different heart rates (coefficient of variation = 1.2 ± 1.9%). In vivo imaging shows no significant difference in TOPAZ T1times between the cardiac phases (analysis of variance: P = 0.14, coefficient of variation = 3.2 ± 0.8%), but underestimation compared with SAPPHIRE (T1time ± precision: 1431 ± 56 ms versus 1569 ± 65 ms). In vivo precision is comparable to SAPPHIRE T1mapping until middiastole (P > 0.07), but deteriorates in the later phases.Conclusions The proposed sequence allows cardiac phase‐resolved T1mapping with integrated
assessment at a temporal resolution of 40 ms. Magn Reson Med 79:2087–2100, 2018. © 2017 International Society for Magnetic Resonance in Medicine. -
more » « less
Abstract Pre‐ejection period (PEP), an indicator of sympathetic nervous system activity, is useful in psychophysiology and cardiovascular studies. Accurate PEP measurement is challenging and relies on robust identification of the timing of aortic valve opening, marked as the B point on impedance cardiogram (ICG) signals. The ICG sensitivity to noise and its waveform's morphological variability makes automated B point detection difficult, requiring inefficient and cumbersome expert visual annotation. In this article, we propose a machine learning‐based automated algorithm to detect the aortic valve opening for PEP measurement, which is robust against noise and ICG morphological variations. We analyzed over 60 hr of synchronized ECG and ICG records from 189 subjects. A total of 3657 averaged beats were formed using our recently developed ICG noise removal algorithm. Features such as the averaged ICG waveform, its first and second derivatives, as well as high‐level morphological and critical hemodynamic parameters were extracted and fed into the regression algorithms to estimate the B point location. The morphological features were extracted from our proposed “variable” physiologically valid search‐window related to diverse B point shapes. A subject‐wise nested cross‐validation procedure was performed for parameter tuning and model assessment. After examining multiple regression models, Adaboost was selected, which demonstrated superior performance and higher robustness to five state‐of‐the‐art algorithms that were evaluated in terms of low mean absolute error of 3.5 ms, low median absolute error of 0.0 ms, high correlation with experts' estimates (Pearson coefficient = 0.9), and low standard deviation of errors of 9.2 ms. For reproducibility, an open‐source toolbox is provided.
