Chen Chen

Fudan University · Human Phenome Institute

Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle heterogeneous signals and guarantee favorable sleep staging performance when a single modality is availabl...

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topolo...

EEG, which can provide brain alteration information via recording the electrical activity of neurons in the cerebral cortex, has been widely used in neurophysiology. However, conventional wet electrodes in EEG monitoring typically suffer from inherent limitations, including the requirement of skin pretreatment, the risk of superficial skin infectio...

Neonatal sleep staging is crucial for understanding infant brain development and assessing neurological health. This study explores the optimal electrode configuration to reduce technical complexities and potential risks of causing skin irritation to neonates during data collection. A Multi-Branch Convolutional Neural Network (CNN) is used to categ...

Driver fatigue poses a significant safety risk in road transport, prompting heightened public awareness. Electroencephalography (EEG), a key physiological signal for fatigue characterization, has garnered substantial interest and served as de facto gold standard. Existing EEG-based fatigue detection methods often utilize limited forehead lobe chann...

Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This l...

Gaze estimation based on electrooculograms (EOGs) has been widely explored. However, the inter-subject variability of EOGs still leaves a significant challenge for practical applications. It contributes to performance degradation when handling inter-subject issues. In this paper, an unsupervised transfer learning approach with an adaptive reweighti...

Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle both precisely offline sleep staging, and online sleep stages prediction and calibration is proposed....

Unlabelled: Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson's disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieve...

Electrooculography-based Human-Computer Interaction (EOG-HCI) is an emerging field. Research in this domain aims to capture eye movement patterns by measuring the corneal-retinal potential difference. This enables translating eye movements into commands, facilitating human-computer interaction through eye movements. This paper reviews articles publ...

Tedious parameter settings and poor performances seriously affect the entropy estimation's effectiveness in time series analysis. To solve these limits, we propose a conceptually novel definition, cumulative diversity pattern entropy (CDEn), focusing on eliminating parameter selections and improving quantization accuracy, stability, and robustness....

KCNQ2 epileptic encephalopathy is relatively common in early-onset neonatal epileptic encephalopathy and seizure severity varied widely, categorized as drug-sensitive epilepsy and drug-resistant epilepsy. However, in clinical practice, anti-seizure medicines need to be gradually adjusted based on seizure control which undoubtedly increases the econ...

Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments m...

The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intr...

A standard operating procedure for studying the sleep phenotypes in a large population cohort is proposed. It is intended for academic researchers in investigating the sleep phenotypes in conjunction with the clinical sleep disorders assessment guidelines. The protocol refers to the definitive American Academy of Sleep Medicine (AASM) manual for se...

Most existing neonatal sleep staging approaches applied multiple EEG channels to obtain good performance. However, it potentially increased the computational complexity and led to an increased risk of skin disruption to neonates during data acquisition. In this paper, a multi-scale hierarchical neural network (MS-HNN) with a squeeze and excitation...

Deep learning methods have become an important tool for automatic sleep staging in recent years. However, most of the existing deep learning-based approaches are sharply constrained by the input modalities, where any insertion, substitution, and deletion of input modalities would directly lead to the unusable of the model or a deterioration in the...

Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed s...

Limited electroencephalography (EEG) channel number is useful for neonatal sleep classification, particularly in the Internet of Medical Things (IoMT) field, where compact and lightweight devices are essential to monitoring health effectively. A streamlined and cost-effective IoMT solution can be achieved by utilizing fewer EEG channels, thereby re...

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signa...

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize...

Background Capacitively coupled electrode (CC electrode), as a non-contact and unobtrusive technology for measuring physiological signals, has been widely applied in sleep monitoring scenarios. The most common implementation is capacitive electrocardiogram (cECG) that could provide useful clinical information for assessing cardiac function and dete...

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating...

Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares...

Objective. Mixing/dissociation of sleep stages in narcolepsy adds to the difficulty in automatic sleep staging. Moreover, automatic analytical studies for narcolepsy and multiple sleep latency test (MSLT) have only done automatic sleep staging without leveraging the sleep stage profile for further patient identification. This study aims to establis...

Motor function evaluation plays an important role in post-stroke rehabilitation. However, the traditional evaluation is subjective and laborious, which may bring a heavy burden to both physicians and stroke survivors. Therefore, an automatic and objective rehabilitation evaluation is needed to minimize the burden of physician, so as to achieve a si...

In quantifying the complexity characteristics of neurophysiological signals, the most advanced entropy methods still have some inevitable limitations of poor accuracy, robustness, and reliability. To solve these limits, this study proposes a novel entropy estimator, termed cumulative residual symbolic dispersion entropy (CRSDE). Meanwhile, a corres...

Electrical status epilepticus during sleep (ESES) is an epileptic encephalopathy in children with complex clinical manifestations. It is accompanied by specific electroencephalography (EEG) patterns of continuous spike and slow-waves. Quantifying such EEG patterns is critical to the diagnosis of ESES. While most of the existing automatic ESES quant...

Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches. Whether intra-artifacts, originated from the eye movement, chin muscle firing, or heart beating, etc., in EEG signals would lead to a positive or a negative masking effect on deep learning-based slee...

Due to the specific advantages of functional near-infrared spectroscopy (fNIRS), fNIRS-based brain network research, especially directed causal coupling analysis, is emerging gradually. The transfer entropy (TE) method has proved its superiority and effectiveness in identifying directed information flow in the brain network, which helps us further...

The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done...

The emerging technology and innovation on sensing technology, data computing, and artificial intelligence (AI) has resulted in an accelerated development of smart healthcare. This thematic issue on Sensing and Computing for Smart Healthcare aims to highlight the diverse advances and the latest developments and emergent technologies in healthcare ap...

Due to the lack of enough physical or suck central pattern generator (SCPG) development, premature infants require assistance in improving their sucking skills as one of the first coordinated muscular activities in infants. Hence, we need to quantitatively measure their sucking abilities for future studies on their sucking interventions. Here, we p...

Deep sleep staging networks have reached top performance on large-scale datasets. However, these models perform poorer when training and testing on small sleep cohorts due to data inefficiency. Transferring well-trained models from large-scale datasets (source domain) to small sleep cohorts (target domain) is a promising solution but still remains...

Deep sleep staging networks have reached top performance on large-scale datasets. However, these models perform poorer when training and testing on small sleep cohorts due to data inefficiency. Transferring well-trained models from large-scale datasets (source domain) to small sleep cohorts (target domain) is a promising solution but still remains...

Capacitive ECG (cECG), as a contactless solution for measuring ECG, has been extensively explored in existing works. However, the signal quality obtained by cECG can abruptly degrade due to body movement. Hence, it substantially increases the challenge in signal quality assessment of cECG. In this paper, a novel multi-classifier fusion approach is...

Sleep posture has been proven to be a crucial index for sleep monitoring in the Internet of Medical Things (IoMT). In this paper, an edge-computing system based on a smart mat for sleep posture recognition in IoMT is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. To meet the requirements of embedded sy...

In this paper, an electrooculography (EOG)-based eye movement angle estimation approach, including signal acquisition, pre-processing, outlier removal and modeling, is proposed. The eye movement angle estimation model is a data-driven approach that using a non-linear polynomial method. It offers a simple, analytical, accurate, and cost-effective so...

With the rapid advancement of monitoring without contact electrocardiogram (ECG), dynamic and real-time signal quality assessment (SQA) becoming a practical problem. In this paper, a two-stream structure that combines residual network (ResNet) and bidirectional long short-term memory (Bi-LSTM) for dynamic ECG (dECG) signals quality assessment is pr...

Surface electromyogram (sEMG) based hand gesture recognition for prosthesis or armband is an important application of the human-machine interface. However, the measurement location of sensors greatly influences the hand gesture performance, especially with the inter-day or inter-subject validation protocols. Therefore, we acquired two-day hand gest...

Background Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustnes...

With the increasing application of functional near-infrared spectroscopy (fNIRS) technology, topological brain network analysis has recently been developed and successfully applied in fNIRS-based brain research. However, the current network information is analyzed through the binary network, and it lacks a dynamic estimation. In this study, we prop...

In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two...

When processing sparse-spectrum biomedical signals, traditional time-frequency (TF) analysis methods are faced with the defects of blurry energy concentration and low TF resolution caused by the Heisenberg uncertainty principle. The synchrosqueezing-based methods have demonstrated advanced TF performances in recent studies. However, these methods c...

The transformation procedure of deriving amplitude-integrated (a)EEG from raw EEG has been well described in a purely analog prototype, however the inherent specifications within the prototype are not established or disclosed. In this paper, we aim at providing an accessible and digitalized aEEG algorithm that is evaluated quantitatively and valida...

With the rapidly increasing number of patients with chronic disease, numerous recent studies have put great efforts into achieving long-term health monitoring and patient management. Specifically, chronic diseases including cardiovascular disease, chronic respiratory disease and brain disease can threaten patients’ health conditions over a long per...

Background and Objective : K-complexes, as a significant indicator in sleep staging and sleep protection, are an important micro-event in sleep analysis. Clinically, K-complexes are recognized through the expert visual inspection of electroencephalogram (EEG) during sleep. Since this process is laborious and has high inter-observer variability, dev...

Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a s...

Sleep posture, as a crucial index for sleep quality assessment, has been widely studied in sleep analysis. In this paper, an unobtrusive smart mat system based on a dense flexible sensor array and printed electrodes along with an algorithmic framework for sleep posture recognition is proposed. With the dense flexible sensor array, the system offers...

Investigating cerebral hemodynamic changes during regular sleep cycles and sleep disorders is fundamental to understanding the nature of physiological and pathological mechanisms in the regulation of cerebral oxygenation during sleep. Although sleep neuroimaging methods have been studied and have been well-reviewed, they have limitations in terms o...

Objective In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial vi...

Objective: Classification of sleep-wake states using multichannel electroencephalography (EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) neural network is developed to classify sleep-wake states using multichannel bipolar EEG signals, which takes an input vector of size 108 containing the joint features of 9...

Objective: In this paper, we propose to evaluate the use of a pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial...

Objective: Electrical status epilepticus during sleep (ESES), as electroencephalographic disturbances, is characterized by strong activation of epileptiform activity in the electroencephalogram (EEG) during sleep. Quantitative descriptors of such epileptiform activity can support the diagnose and the prognosis of children with ESES. To quantify th...

In recent times, with the advancement of digital imaging, automatic facial recognition has been intensively studied for adults, while less for neonates. Due to the miniature facial structure and facial attributes, newborn facial recognition remains a challenging area. In this paper, an automatic video-based Neonatal Face Attributes Recognition (NFA...

To characterize the irregularity of the spectrum of a signal, spectral entropy and its variants are widely adopted measures. However, spectral entropy is invariant under the permutation of the power spectrum estimations on a predefined grid. This erases the inherent order structure in the spectrum. To disentangle the order structure and extract mea...

In this paper, a multichannel reconfigurable EEG acquisition system with novel flexible dry electrodes is proposed. The novel electrode is designed to overcome the limitations of conventional wet electrodes such as skin irritation, skin preparation, and conductive gel requirements. It is based on the conductive and stretchable Ag NWs/PDMS composite...

Amplitude-integrated electroencephalography (aEEG) is a simplified method for long-term, continuous, and bedside monitoring of brain activity. While conventional Electroencephalography (EEG) is the gold standard of assessing brain function, aEEG is easy to operate and allows bedside interpretation of brain activity by health care providers without...

In this paper, an unconstrained cardiac monitoring system with a novel dual tripolar concentric ring (D-TCR) geometry-based flexible active ECG electrodes is presented. The D-TCR ECG electrode, which based on the conductive flexible and stretchable Ag NWs/PDMS composite material, is designed to acquire the high-fidelity electrocardiographic potenti...

Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach based on a hierarchical neural network to process multi-channel PSG signals for improving the perfo...

Objective: Currently, the automatic sleep staging methods mainly face two problems: the first problem is that although the algorithms which use electroencephalogram (EEG) signals have nice performance, acquiring EEG signals is complicated and uncomfortable; the second one is that if the methods utilize physiological signals collected by user-frien...

Sleep stage classification is a fundamental but cumbersome task in sleep analysis. To score the sleep stage automatically, this study presents a stage classification method based on a two-stage neural network. The feature learning stage as the first stage can fuse network trained features with traditional hand-crafted features. A recurrent neural n...

In this paper, an unconstrained cardiorespiratory system for monitoring of ECG and respiration during sleep is presented. A novel active dry ECG electrode, which based on the conductive flexible and stretchable Ag NWs/PDMS composite material is designed to acquire the electrocardiographic potentials through the cloth. Meanwhile, a membrane pressure...

Automatic seizure detection has been often treated as a classification problem that aims at determining the label of electroencephalogram (EEG) signals by computer science, as EEG monitoring is a helpful adjunct to the diagnosis of epilepsy. In most existing work, the traditional signal energy of EEG has been applied for classification, since the e...

Objective: To characterize the irregularity of the spectrum of a signal, spectral entropy is a widely adopted measure. However, such a metric is invariant under any permutation of the estimations of the powers of individual frequency components on a predefined grid. This erases the order structure inherent in the spectrum which is also an important...

Background: Stroke is a leading cause of mortality and disability, which can be affected by people's daily living habits. Objective: To investigate the effects of main daily living habits (smoking, drinking, diet, vegetable and fruits consumption, and exercise) on stroke risk in patients and provide the scientific basis for the assessment of the...

Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle m...

In order to obtain the respiratory condition unobtrusively and comfortably, a non-contact method based on the commercial depth camera Realsense SR300 was proposed to extract respiratory information from depth data. In this paper, a respiratory region detecting algorithm which is mainly based on the morphological method was proposed to obtain the re...

Identification of sleep stages is a fundamental step in clinical sleep analysis. Existing automatic sleep staging systems ignore two major issues: 1) Most of existing automatic sleep staging systems are using numerical classification methods without involving medical knowledge. While these kinds of systems are not yet understood and accepted by phy...

Heart sounds deliver vital physiological and pathological evidence about health. Wireless cardiac auscultation offers continuous cardiac monitoring of an individual without 24*7 manual healthcare care services. In this paper, a novel wireless sensing system to monitor and analyze cardiac condition is proposed, which sends the information to the car...

This paper presents a novel system for automatic sleep staging based on evolutionary technique and symbolic intelligence. Proposed system mimics decision making process of clinical sleep staging using Symbolic Fusion and considers personal singularity with an adaptive thresholds setting up system using Evolutionary Algorithm. It proved to be an eff...

Background: Epilepsy is a common chronic neurological disorder of the brain. Clinically, epileptic seizures are usually detected via the continuous monitoring of electroencephalogram (EEG) signals by experienced neurophysiologists. Objective: In order to detect epileptic seizures automatically with a satisfactory precision, a new method is propo...

Sleep staging is a fundamental step in diagnosis and treatment of sleep disorders. In current sleep staging systems, normally a set of thresholds should be set up to determine the boundaries in differentiating different linguistic or symbolic features. However, as far as we know, there are no fully satisfying automatic method to do this task. Thres...

In this paper, a personalized sleep staging system is proposed by combining symbolic intelligence and feedback system control technique. Symbolic Fusion is dedicated to mimic decision-making process of clinical sleep staging. It starts from extraction of digital parameters from raw polysomnography (PSG) signals and goes up-to high level symbolic in...

With the rapid extension of clinical data and knowledge, decision making becomes a complex task for manual sleep staging. In this process, there is a need for integrating and analyzing information from heterogeneous data sources with high accuracy. This paper proposes a novel decision support algorithm—Symbolic Fusion for sleep staging application....

Sleep staging is a time-consuming work and inter-rater reliability variation exists in sleep-stages scorers. There is a need for automatic sleep staging which can facilitate this process and enhance the reliability. This paper presents a new method based on symbolic fusion to realize automatic sleep staging. By combining multiple signals of polysom...

Blind source separation (BSS) is an effective and powerful tool for signal processing and artifact removal in electroencephalographic signals. For real-time applications such as brain–computer interfaces, cognitive neuroscience or clinical neuromonitoring, it is of prime importance that BSS is effectively performed in real time. In order to improv...

Steady-state visually evoked potentials (SSVEP) can be elicited by a large variety of stimuli. To the best of our knowledge, the size and shape effect of stimuli has never been investigated in the literature. We study the relationship between the visual parameters (size and shape) of the stimulation and the resulting brain response. A tentative phy...