Chengyu Liu

Chengyu Liu
Southeast University (China) | SEU · School of Instrument Science and Engineering

PhD

About

290
Publications
94,528
Reads
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4,195
Citations
Introduction
Research topics: A. Physiological signal processing 1) Signal quality assessment and features detection 2) Real-time and wearable 3) Machine learning, such as CNN, SVM, GA B. Non-linear and optimization methods 1) Entropy, LZ complexity 2) PSO and Bayes optimization 3) Signal complexity, regularity and coupling C. Device development 1) ECG mobile phone and fetal ECG monitoring device 2) BP measurement 3) Non-invasive methods for detection of AF, HF, hypertension, atherosclerosis and arrhythmia
Additional affiliations
September 2017 - present
Southeast University (China)
Position
  • Professor
September 2015 - July 2017
Emory University
Position
  • Fellow
May 2013 - November 2014
Newcastle University
Position
  • Research Associate
Education
September 2005 - December 2010
Shandong University
Field of study
  • Biomedical Engineering
September 2001 - June 2005
Shandong University
Field of study
  • Biomedical Engineering

Publications

Publications (290)
Article
Full-text available
Entropy provides a valuable tool for quantifying the regularity of physiological time series and provides important insights for understanding the underlying mechanisms of the cardiovascular system. Before any entropy calculation, certain common parameters need to be initialized: embedding dimension m, tolerance threshold r and time series length N...
Article
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In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the litera...
Article
Full-text available
Modeling arterial pressure waveforms holds the potential for identifying physiological changes. There is a clinical need for a simple waveform analysis method with a high accuracy in reproducing the original waveforms. The aim of this study was to determine the accuracy of modeling carotid and radial pulses using Gaussian functions, making no physi...
Article
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Coronary artery disease (CAD) is the most common cause of death globally. To detect CAD noninvasively at an early stage before clinical symptoms occur is still nowadays challenging. Analysis of the variation of heartbeat interval (RRI) opens a new avenue for evaluating the functional change of cardiovascular system which is accepted to occur at the...
Article
Full-text available
The poor quality of wireless electrocardiography (ECG) recordings can lead to misdiagnosis and waste of medical resources. This study presents an interpretation of Lempel–Ziv (LZ) complexity in terms of ECG quality assessment, and verifies its performance on real ECG signals. Firstly, LZ complexities for typical signals, namely high-frequency (HF)...
Article
Full-text available
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...
Article
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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...
Article
Full-text available
Background: Autonomic nervous regulation plays a critical role in end-stage kidney disease (ESKD) patients with cardiovascular complications. However, studies on autonomic regulation in ESKD patients are limited to heart rate variability (HRV) analysis. Skin sympathetic nerve activity (SKNA), which noninvasively reflects the sympathetic nerve activ...
Article
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Paroxysmal atrial fibrillation (PAF) may related to the risk of thromboembolism and is the most common cardiac risk factor of cryptogenic stroke (CS). Due to its paroxysmal characteristics, it is usually diagnosed by continuous long-term ECG. Patients with paroxysmal atrial fibrillation usually have premature beats at the same time which is easy to...
Article
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Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization an...
Article
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Background and aims: Auscultation is a cheap and fundamental technique for detecting cardiovascular disease effectively. Doctors' abilities in auscultation are varied. Sometimes, there may be cases of misdiagnosis, even when auscultation is performed by an experienced doctor. Hence, it is necessary to propose accurate computational tools to assist...
Article
Full-text available
How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We...
Article
The tensor is a generalized data formation, and some tensor-based manipulation techniques are superior to time series or images in data processing. However, an effective measure of spatiotemporal regularity for tensors is still lacking. This study proposes an entropy measure for tensors, tensor approximate entropy (TensorApEn), to evaluate the regu...
Preprint
Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion even early warning for physicians, however, they are mutually-exclusive in terms of robustness, generalization and lo...
Article
Full-text available
In this study, we aimed to develop an automatic atrial fibrillation detection technique for the early prediction of atrial fibrillation, that can be used with wearable devices. An effective deep learning-based technology is proposed to automatically detect atrial fibrillation. First, novel preprocessing algorithms, wavelet transform and sliding win...
Preprint
Full-text available
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...
Article
Full-text available
Atrial fibrillation (AF) is a progressive disease often initially manifested by intermittent episodes spontaneously terminating, and is an insidious disease. The previous work trained the support vector machine (SVM) classifier on multiple RR interval features. The trained AF detector was tested on the 4th China Physiological Signal Challenge (CPSC...
Chapter
Full-text available
It is one of the hot spots in recent years to explore changes in the sleep stage by assessing autonomic nervous activity. In recent years, heart rate asymmetry (HRA) is often used to measure the activity of autonomic nerves. However, the relationship between HRA and sleep stage is not clear. We performed Porta’s index (PI), Guzik’s index (GI), slop...
Article
Full-text available
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...
Preprint
Full-text available
Objective: Heart rate variability (HRV) has been proven to be an important indicator of physiological status for numerous applications. Despite the progress and active developments made in HRV metric research over the last few decades, the representation of the heartbeat sequence upon which HRV is based has received relatively little attention. The...
Article
Depression endangers people's health conditions and affects the social order as a mental disorder. As an efficient diagnosis of depression, automatic depression detection has attracted lots of researcher's interest. This study presents an attention-based Long Short-Term Memory (LSTM) model for depression detection to make full use of the difference...
Conference Paper
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...
Article
Full-text available
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...
Article
Full-text available
To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography featu...
Article
Full-text available
Entropy algorithm is an important nonlinear method for cardiovascular disease detection due to its power in analyzing short-term time series. In previous a study, we proposed a new entropy-based atrial fibrillation (AF) detector, i.e., EntropyAF, which showed a high classification accuracy in identifying AF and non-AF rhythms. As a variation of ent...
Article
Automatic detection of arrhythmia through an electrocardiogram (ECG) is of great significance for the prevention and treatment of cardiovascular diseases. In Convolutional neural network, the ECG signal is converted into multiple feature channels with equal weights through the convolution operation. Multiple feature channels can provide richer and...
Article
Full-text available
Depression is a mental disorder that threatens the health and normal life of people. Hence, it is essential to provide an effective way to detect depression. However, research on depression detection mainly focuses on utilizing different parallel features from audio, video, and text for performance enhancement regardless of making full usage of the...
Article
Full-text available
Fasting has been demonstrated to improve health and slow aging in human and other species; however, its impact on the human body in the confined environment is still unclear. This work studies the effects of long-term fasting and confined environment on the cardiovascular activities of human via a 10-day fasting experiment with two groups of subjec...
Preprint
Full-text available
Atrial fibrillation is one of the most common arrhythmias in clinics, which has a great impact on people's physical and mental health. Electrocardiogram (ECG) based arrhythmia detection is widely used in early atrial fibrillation detection. However, ECG needs to be manually checked in clinical practice, which is time-consuming and labor-consuming....
Article
Full-text available
Arrhythmia is a kind of cardiovascular disease that seriously threats human health. Intelligent analysis of electrocardiogram (ECG) is an effective method for the early prevention and precise treatment to arrhythmia. In clinical ECG waveforms, it is common to see the multi-label phenomenon that one patient would be labelled with multiple types of a...
Article
Objective: ECG-derived respiration (EDR) methods have been developed during the past decades to obtain respiration-relevant information. However, it is still necessary to compare the performance of these methods under uniform conditions for reasonable application. Approach: In this paper, the performance of 10 feature-based EDR methods was evalu...
Article
Full-text available
The analysis of heart rate variability (HRV) plays a dominant role in the study of physiological signal variability. HRV reflects the information of the adjustment of sympathetic and parasympathetic nerves on the cardiovascular system and, thus, is widely used to evaluate the functional status of the cardiovascular system. Ectopic beats may affect...
Article
Full-text available
The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to h...
Article
Full-text available
Automatic sleep stage classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural netw...
Article
This paper develops a time-saving, simple, and comfortable method for detecting Sleep Apnea Syndrome (SAS). Seventy SAS patients and 17 healthy persons were randomly selected in this study, and nine analytical parameters (i.e., [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see tex...
Article
Abstract:Objective: This study aimed to prove that there is a sudden change in human physiology system when switching from one sleep stage to another, and a physical threshold-based SampEn is able to capture this transition in RR interval time series. The rule could also be adapted to sleep disorder patients such as sleep apnea, since the basic sle...
Preprint
Full-text available
This paper proposed a feature selection method combined with multi-time-scales analysis and heart rate variability (HRV) analysis for middle and early diagnosis of congestive heart failure (CHF). In previous studies regarding the diagnosis of CHF, researchers have tended to increase the variety of HRV features by searching for new ones or to use di...
Article
In the field of biomedicine, time irreversibility is used to describe how imbalanced and asymmetric biological signals are. As an important feature of signals, the direction of time is always ignored. To find out the variation regularity of time irreversibility of heart rate variability (HRV) in the initial stage of hypoxic exposure, the present st...
Article
PurposeAs depression has been a major contributor to the global disease burden, objective and effective computer-aided diagnosis has become an urgent problem. This study aims to assess the frontal asymmetry variation of alpha electroencephalography (EEG) in different severity depression patients and to find promising biomarkers for future depressio...
Article
Full-text available
Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely differed when applying on the independent test datasets. This situation is more significant for AF...
Article
Full-text available
Arrhythmias are very common in the healthy populations as well as patients with cardiovascular diseases. Among them, atrial fibrillation (AF) and malignant ventricular arrhythmias are usually associated with some clinical events. Early diagnosis of arrhythmias, particularly AF and ventricular arrhythmias, is very important for the treatment and pro...
Article
In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation,...
Article
Full-text available
Atrial fibrillation (AF) can cause a variety of heart diseases and its detection is insufficient in outside-hospital. We proposed three methods for AF diagnosis in ambulatory settings. The first method is a convolutional neural network (CNN) trained on modified frequency slice wavelet transform (MFSWT) data. The second is a support vector machine (...
Article
Objective: Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. \However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algo...
Article
Full-text available
Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, intro...
Article
Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, intro...
Article
Full-text available
Depression is a leading cause of disability worldwide, and objective biomarkers are required for future computer-aided diagnosis. This study aims to assess the variation of frontal alpha complexity among different severity depression patients and healthy subjects, therefore to explore the depressed neuronal activity and to suggest valid biomarkers....
Article
Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The...