Multi-Point Near-Field RF Sensing of Blood Pressures and Heartbeat Dynamics

Abstract

Systolic and diastolic blood pressure estimation using arm-cuff monitors is one of the most common cardiovascular evaluation criteria in healthcare today, however these measures lack critical heartbeat and pressure dynamics. Pulse-transit time can be used as an alternative for arm-cuff monitors, but gives only one parameter for two pressure quantities. Ultrasound, computed tomography scan, and magnetic resonance imaging can retrieve geometrical features of the heart, but cannot directly estimate vascular pressures. Here we show a novel radio-frequency heartbeat sensor based on multi-point near-field observation. By comparing to synchronized electrocardiogram and auscultation, the multi-point sensor can assess motion and pressure in different parts of the heart following the Wiggers diagram. By applying the Hilbert-Huang frequencytime transform, the central blood pressure can be derived from the vascular vibration characteristics as continuous transients, including during the pulmonary cycle which is previously inaccessible from branchial measurements. Our scheme can be further extended to a full multiple-input-multiple-output (MIMO) channel arrangement, producing rich content for diagnostic and biometric applications. We employ dynamic time warping analyses to illustrate the sensor's wealth of diversified information across different channels and persons. This new multi-point sensor, which can be worn conveniently over clothing, enables unprecedented monitoring capabilities of detailed central blood pressure transients and heartbeat dynamics under the given measurement instructions for at-home and clinical cardiovascular diagnostics.

Machine Learning and IoT Applied to Cardiovascular Diseases Identification Through Heart Sounds: A Literature Review

“…Table 5 presents the list of selected articles, containing a numerical identification, reference, authors' countries, databases, year of publication, and a short description of the research work. India IEEE Xplore Noise detection on sign Ayari et al [54] Tunisia/USA IEEE Xplore Mathematical component analysis algorithm for separation of cardiac sounds from pulmonary sounds Udawatta et al [55] Sri Lanka IEEE Xplore Digital stethoscope to amplify signal Malek et al [56] Malaysia IEEE Xplore Digital stethoscope in Arduino, ZigBee and signal processing by MatLab Singh and Singh [36] India IEEE Xplore Convolutional Neural Networks Das et al [57] India IEEE Xplore Algorithm to remove signal noise, regardless of sensor quality Gjoreski et al [58] Slovenia/Macedônia IEEE Xplore Machine Learning Pereira et al [59] Portugal/Brasil IEEE Xplore Machine Learning Banerjee et al [60] India IEEE Xplore Convolutional Neural Networks Suhn et al [61] Germany IEEE Xplore Carotid auscultation equipment Gautam and kumar [62] India IEEE Xplore Multilayer Multilayer Perceptron Artificial Neural Network Zhang et al [63] Singapore IEEE Xplore Heart rate estimation algorithm Doshi et al [64] India IEEE Xplore Neural Network Prasad et al [65] Switzerland IEEE Xplore Processing in the time domain employing a low-pass filter Rao et al [66] Switzerland IEEE Xplore Neural Network Hui et al [67] USA IEEE Xplore Investigates transient movement and heartbeat Humayun et al [25] Bangladesh/USA IEEE Xplore Use of convolutional neural network to detect abnormality of cardiac sound with stethoscope Shuvo et al [68] Bangladesh/Saudi Arabia/Yemen IEEE Xplore Convolutional Neural Network for automatic detection of different classes of cardiovascular diseases, direct by phonocardiography signal Tiwari et al [27] India/Saudi Arabia IEEE Xplore Hybrid model, with signal processing using the constant Q transform and Convolutional Neural Network Du et al [69] China JMIR Big Data and Machine Learning Chowdhury et al [26] Qatar/Malaysia PubMed Central Processing and classification using MATLAB Leng et al [30] Singapore PubMed Central Machine Learning Techniques Elgendi et al [70] Canada/India PubMed Central Developed a Wavelet-based algorithm SwarupandMakaryus [71] USA PubMed Central Use of digital stethoscope and mobile computing Raza et al…”

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…Table 5 presents the list of selected articles, containing a numerical identification, reference, authors' countries, databases, year of publication, and a short description of the research work. India IEEE Xplore Noise detection on sign Ayari et al [54] Tunisia/USA IEEE Xplore Mathematical component analysis algorithm for separation of cardiac sounds from pulmonary sounds Udawatta et al [55] Sri Lanka IEEE Xplore Digital stethoscope to amplify signal Malek et al [56] Malaysia IEEE Xplore Digital stethoscope in Arduino, ZigBee and signal processing by MatLab Singh and Singh [36] India IEEE Xplore Convolutional Neural Networks Das et al [57] India IEEE Xplore Algorithm to remove signal noise, regardless of sensor quality Gjoreski et al [58] Slovenia/Macedônia IEEE Xplore Machine Learning Pereira et al [59] Portugal/Brasil IEEE Xplore Machine Learning Banerjee et al [60] India IEEE Xplore Convolutional Neural Networks Suhn et al [61] Germany IEEE Xplore Carotid auscultation equipment Gautam and kumar [62] India IEEE Xplore Multilayer Multilayer Perceptron Artificial Neural Network Zhang et al [63] Singapore IEEE Xplore Heart rate estimation algorithm Doshi et al [64] India IEEE Xplore Neural Network Prasad et al [65] Switzerland IEEE Xplore Processing in the time domain employing a low-pass filter Rao et al [66] Switzerland IEEE Xplore Neural Network Hui et al [67] USA IEEE Xplore Investigates transient movement and heartbeat Humayun et al [25] Bangladesh/USA IEEE Xplore Use of convolutional neural network to detect abnormality of cardiac sound with stethoscope Shuvo et al [68] Bangladesh/Saudi Arabia/Yemen IEEE Xplore Convolutional Neural Network for automatic detection of different classes of cardiovascular diseases, direct by phonocardiography signal Tiwari et al [27] India/Saudi Arabia IEEE Xplore Hybrid model, with signal processing using the constant Q transform and Convolutional Neural Network Du et al [69] China JMIR Big Data and Machine Learning Chowdhury et al [26] Qatar/Malaysia PubMed Central Processing and classification using MATLAB Leng et al [30] Singapore PubMed Central Machine Learning Techniques Elgendi et al [70] Canada/India PubMed Central Developed a Wavelet-based algorithm SwarupandMakaryus [71] USA PubMed Central Use of digital stethoscope and mobile computing Raza et al…”

Sensing of Life Activities at the Human-Microwave Frontier

“…An alternative microwave sensing approach was near-field coherent sensing, which can retrieve the heart sound through layers of clothing using ultrahigh frequency (UHF) band (300MHz -3 GHz) signals [81]. This enables multi-point near-field assessment of motion and pressure at different parts of the heart [82]. Furthermore, the Hilbert-Huang frequency-time transform can be used to derive the central blood pressure from the vascular vibration characteristics as continuous transients.…”