Chenxi Yang

• Doctor of Philosophy
• Professor (Associate) at Southeast University

This paper reports on the combined analysis of seismocardiogram (SCG) and gyrocardiogram (GCG) recordings. An inertial measurement unit (IMU) consisting of a three-axis MEMS accelerometer and a three-axis MEMS gyroscope is used to record heart-induced mechanical vibrations from the chest wall of the subjects. An electrocardiogram (ECG) and an imped...

This work proposes a novel method of pulse transit time measurement. The proximal arterial location data is collected from seismocardiogram (SCG) recordings by placing a MEMS accelerometer on the chest wall. The distal arterial location data is recorded using an acoustic sensor placed inside the ear. The performance of distal location recordings is...

This paper describes an open-access database for seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the United States, as well as Southeast University, Nanji...

Objectives: This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the c...

This paper presents a smartphone-only solution for measuring pulse transit time (PTT). An application based on an Android smartphone is developed to collect seismocardiogram (SCG), gyrocardiogram (GCG), and photoplethysmography (PPG) recordings. The system does not need any other external system for measurements, so the total cost and system comple...

Noncontact capacitive electrocardiogram (cECG) is gaining recognition in cardiovascular disease monitoring for its comfort and non-invasiveness. Compared to the conventional ECG, cECG signal quality is prone to degradation in practical applications due to motion artifacts and power line interference (PLI). This study proposed an optimized signal qu...

The heart sound signal is an important physiological signal of the cardiovascular system, which contains crucial information about human heart activity, especially cardiac mechanical activity. Cardiac auscultation is also the most routine means of clinical cardiovascular disease risk screening. This paper aims to design an efficient model that can...

Non-invasive fetal electrocardiography (fECG) offers crucial information for assessing early diagnosis of fetal distress and morbidity. However, the non-invasive fECG signals probably contain various non-stationary noises, which may generate a bad influence on signal processing. Signal quality assessment plays a crucial role in accurate feature est...

Non-invasive fetal electrocardiography (fECG) has a promising application prospect in offering crucial information for assessing early diagnosis and intervention of fetal distress and morbidity during pregnancy. Nevertheless, the detection and extraction of fetal ECG signals are still challenging since fetal ECG signals are exceedingly weak, and li...

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This paper proposes a novel eye movement monitoring method called continuous...

Cardiovascular diseases are a leading cause of death globally, and atrial fibrillation (AF) is a common arrhythmia that affects many people. Detecting AF in real-time using hardware acceleration can prompt timely medical intervention. Multi-layer perceptron (MLP) has demonstrated the ability to detect AF accurately. However, implementing MLP on Fie...

Frequency-modulated continuous wave radar is capable of constant, real-time detection of human presence and monitoring of cardiopulmonary signals such as respiration and heartbeat. In highly cluttered environments or when the human body moves randomly, noise signals may be relatively large in some range bins, making it crucial to accurately select...

Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction...

This study designed a novel noncontact capacitive electrocardiogram (cECG) system for long-term atrial fibrillation (AF) monitoring. Unlike conventional ECG monitoring, the proposed system can record ECG signals without touching a subject’s skin, avoiding the problem of skin allergy by using wet electrodes. The main contributions of this study incl...

The autonomic nervous system is closely related to cardiovascular diseases (CVDs). Simultaneous non-invasive recording of skin sympathetic nerve activity (SKNA) and electrocardiogram (ECG) is a new method for autonomic nervous system real-time assessment. This study presented a portable monitoring system based on SKNA. A system-level modification b...

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be appli...

PurposeElectrocardiogram (ECG) monitoring is an essential approach for the early diagnosis of cardiovascular diseases. The flexible non-hydrogel electrode that contains electrolyte without water could be a potential substitution of wet electrodes for long-term ECG monitors. However, it is not clear if the electrical characteristics and signal quali...

Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager–Kaiser energy (TKE) operator to preprocess t...

Electrocardiogram (ECG) monitoring is an essential method for medical diagnosis [1], [2]. With the development of technology, better quality physiological signals can be easily recorded [3]. Long-term monitoring of electrocardiographic activity is helpful to monitor potential cardiac dysfunction. The Ag/Agcl electrode with conductive gel can record...

PurposeLong-term electrocardiogram (ECG) monitoring is an essential approach for the early diagnosis of cardiovascular diseases. Flexible dry electrodes that contains electrolyte without water could be a potential substitution of wet electrodes for long-term ECG monitoring. Therefore, this paper developes a long-term, portable ECG patch based on fl...

Noncontact capacitive coupling electrocardiogram (cECG) is considered to have a broad application prospect for its comfort and noninvasive. As an important part of cECG transmission path, the characteristics of dielectric materials significantly affect the signal quality. This article studies the influence on the dielectric properties of coupling c...

Objective: The single-lead handheld atrial fibrillation (AF) detection device is suitable for daily monitoring or early screening of AF in the hospital. However, the signal quality and the reliability of AF detection algorithm still need to be improved. This study proposed a novel AF detection system with a user-friendly interaction and a lightwei...

This paper presents a real-time electrocardiogram (ECG) analysis system that can detect atrial fibrillation (AF) using machine learning algorithms without a cloud server. The system takes advantage of the heterogeneous structure of the Zynq system-on-chip (SoC) to optimize the tasks of local implementation of AF detection. The features extraction i...

This study presents our recent findings on the classification of mean pressure gradient using angular chest movements in aortic stenosis (AS) patients. Currently, the severity of aortic stenosis is measured using ultra-sound echocardiography, which is an expensive technology. The proposed framework motivates the use of low-cost wearable sensors, an...

This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous w...

This paper reports a pilot study of a hybrid radar-camera system that simultaneously monitors the respiration of two subjects. A prototype system was built involving a low-cost impulse-radio ultra-wideband (IR-UWB) radar module and an optical and depth-sensing camera module. The system detects subjects using the camera and utilizes the distance inf...

This paper introduces a low-cost phantom system that simulates fetal movements (FMVs) for the first time. This vibration system can be used for testing wearable inertial sensors which detect FMVs from the abdominal wall. The system consists of a phantom abdomen, a linear stage with a stepper motor, a tactile transducer, and control circuits. The li...

This paper reports our study on the impact of transcatheter aortic valve replacement (TAVR) on the classification of aortic stenosis (AS) patients using cardio-mechanical modalities. Machine learning algorithms such as decision tree, random forest, and neural network were applied to conduct two tasks. Firstly, the pre- and post-TAVR data are evalua...

This article presents the design and measurement results of a novel wideband circularly polarized 2 × 2 slot antenna array loaded with a metasurface layer. The proposed array exploits circularly polarized elements. Each element is sequentially rotated and fed with an appropriate phase difference to ensure the generation of a circularly polarized wa...

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algor...

This paper reports a system for monitoring pulse transit time (PTT). Using an Android smartphone and a customized sensing circuit, the system collects seismo-cardiogram (SCG), gyro-cardiogram (GCG), and photoplethysmogram (PPG) recordings. There is no need for any other external stand-alone systems. The SCG and GCG signals are recorded with the ine...

This work proposes a novel approach for detecting fetal heart rate (FHR) using seismo-cardiogram (SCG) and gyro-cardiogram (GCG) recordings collected from abdominal inertial sensors. A proof-of-concept setup with commercially available sensor nodes is prepared. The FHR components are extracted from the fused cepstrum of recordings of all the sensor...

This work proposes a new framework for measuring sternal cardio-mechanical signals from moving subjects using multiple sensors. An array of inertial measurement units (IMUs) are attached to the chest wall of subjects to measure the seismo-cardiogram (SCG) from accelerometers and the gyro-cardiogram (GCG) from gyroscopes. A digital signal processing...

This paper reports on the use of the rotational component of chest vibrations for the automatic annotation of seismogram (SCG) recordings for the first time. An inertial measurement unit consisting of a three-axis MEMS accelerometer and a three-axis MEMS gyroscope is used for recording chest vibrations. The gyroscope signal acts as a reference for...

This paper presents a new setup and method of measuring pulse transit time (PTT) using seismocardiographic data from a MEMS accelerometer and acoustic recordings from a commercial microphone placed inside the ear. A DSP system is designed and implemented in MATLAB, and experimentally tested on 5 healthy adult subjects at rest. Failure conditions ar...

This paper presents a dual-sensor method of extracting seismocardiographic (SCG) data from moving adult subjects using chest-worn wireless MEMS accelerometers. A digital signal processing (DSP) system including a normalized least means square (NLMS) adaptive filter is designed and tested in MATLAB. Data results from 10 subjects indicate a detection...

This paper presents a novel method of extracting seismocardiographic (SCG) data from moving adult subjects recorded via micro-electromechanical (MEMS) accelerometers. A digital signal processing system based on the normalized least mean square (NLMS) adaptive filter design is developed in MATLAB to process the signals collected from the MEMS sensor...

This paper presents a novel method of extracting seismocardiographic data from moving adult subjects using chest-worn wireless MEMS accelerometers. A DSP system including a normalized LMS adaptive filter is designed and tested in MATLAB. Data results from 11 subjects indicate a detection rate of 97.33% and a high correlation between SCG and ECG sig...