Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study
“…Another viable option for SCG waveform analysis is represented by ML techniques which can be exploited for a variety of different tasks from waveform annotation and comparison to SCG template generation or waveform-based classification tasks [ 43 , 44 , 59 , 60 , 65 , 66 , 67 , 68 ]. ML techniques are useful and mainly employed for waveform matching tasks, which may pave the way for automated diagnosis of HF based on the SCG morphology changes.…”
Section: Precordial Vibrations Recording Using Accelerometersmentioning
confidence: 99%
“…For what concerns sensor positioning, for precordial vibration monitoring, accelerometers are commonly attached to the patient’s chest at different placement locations on the sternum or in its proximity. The most investigated points include the sternum [ 45 , 47 , 66 ], the lower end of the sternum (i.e., xiphoid process) [ 41 , 46 , 52 , 55 ], the fourth intercostal space (IC4) near the left lower sternal border [ 43 , 44 ], and the four valvular auscultation sites (mitral, tricuspid, aortic, and pulmonary) [ 51 , 65 ]. Sensor placement locations that have been investigated in the literature are reported in Figure 3 a, together with the measurement sites explored for the other sensor types (i.e., gyroscopes and FBGs) that we illustrate in more detail in the next sections.…”
Section: Precordial Vibrations Recording Using Accelerometersmentioning
Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects.
“…Another viable option for SCG waveform analysis is represented by ML techniques which can be exploited for a variety of different tasks from waveform annotation and comparison to SCG template generation or waveform-based classification tasks [ 43 , 44 , 59 , 60 , 65 , 66 , 67 , 68 ]. ML techniques are useful and mainly employed for waveform matching tasks, which may pave the way for automated diagnosis of HF based on the SCG morphology changes.…”
Section: Precordial Vibrations Recording Using Accelerometersmentioning
confidence: 99%
“…For what concerns sensor positioning, for precordial vibration monitoring, accelerometers are commonly attached to the patient’s chest at different placement locations on the sternum or in its proximity. The most investigated points include the sternum [ 45 , 47 , 66 ], the lower end of the sternum (i.e., xiphoid process) [ 41 , 46 , 52 , 55 ], the fourth intercostal space (IC4) near the left lower sternal border [ 43 , 44 ], and the four valvular auscultation sites (mitral, tricuspid, aortic, and pulmonary) [ 51 , 65 ]. Sensor placement locations that have been investigated in the literature are reported in Figure 3 a, together with the measurement sites explored for the other sensor types (i.e., gyroscopes and FBGs) that we illustrate in more detail in the next sections.…”
Section: Precordial Vibrations Recording Using Accelerometersmentioning
Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects.
“…For the proof-of-concept study, data were collected from 20 patients with HF (20% women; median age 55 years) at the University of California-San Francisco during their right heart catheterization procedures. 3 Each patient underwent a right heart catheterization and vasodilator challenge while wearing an earlier iteration of the CardioTag. The signals obtained by the CardioTag simultaneously during the right heart catheterization and vasodilator challenge were used to estimate the changes in PCWP.…”
mentioning
confidence: 99%
“…The model estimated changes in PCWP with reasonable accuracy for the validation set (root-mean-square error = 2.9 mm Hg; R 2 = 0.95), indicating that changes in the CardioTag’s signals can be used to track changes in PCWP in patients with HF ( Figure 1C ). 3 Figure 1 Estimation Results for the Validation Set (A) Correlation analysis for ΔPAM predicted versus ΔPAM actual, (B) Bland-Altman analysis for ΔPAM predicted and ΔPAM actual, (C) correlation analysis for ΔPCWP predicted versus ΔPCWP actual, and (D) Bland-Altman analysis for ΔPCWP predicted and ΔPCWP actual. In the Bland-Altman plots, the black line indicates the mean, and the blue dashed lines indicate mean ± 1.96.…”
mentioning
confidence: 99%
“…Reprinted, with permission, from Shandhi et al. 3 NRMSE = normalized root-mean-square error; PAM = pulmonary artery mean pressure; PCWP = pulmonary capillary wedge pressure; RMSE = root-mean-square error. …”
Wearable heart monitors are crucial for early diagnosis and treatment of heart diseases in non‐clinical settings. However, their long‐term applications require skin‐interfaced materials that are ultrasoft, breathable, antibacterial, and possess robust, enduring on‐skin adherence—features that remain elusive. Here, we have developed multifunctional porous soft composites that meet all these criteria for skin‐interfaced bimodal cardiac monitoring. The composite consists of a bilayer structure featuring phase‐separated porous elastomer and slot‐die‐coated biogel. The porous elastomer ensures ultrasoftness, breathability, ease of handling, and mechanical integrity, while the biogel enables long‐term on‐skin adherence. Additionally, we incorporated ε‐polylysine in the biogel to offer antibacterial properties. Also, the conductive biogel embedded with silver nanowires was developed for use in electrocardiogram sensors to reduce contact impedance and ensure high‐fidelity recordings. Furthermore, we assembled a bimodal wearable cardiac monitoring system that demonstrates high‐fidelity recordings of both cardiac electrical (electrocardiogram) and mechanical (seismocardiogram) signals over a 14‐day testing period.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
