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Wei Chen
Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, China

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Original research article
Published: 12 July 2021 in Frontiers in Neuroscience
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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-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring.

ACS Style

Jiahao Fan; Chenglu Sun; Meng Long; Chen Chen; Wei Chen. EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal. Frontiers in Neuroscience 2021, 15, 573194 .

AMA Style

Jiahao Fan, Chenglu Sun, Meng Long, Chen Chen, Wei Chen. EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal. Frontiers in Neuroscience. 2021; 15 ():573194.

Chicago/Turabian Style

Jiahao Fan; Chenglu Sun; Meng Long; Chen Chen; Wei Chen. 2021. "EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal." Frontiers in Neuroscience 15, no. : 573194.

Journal article
Published: 06 July 2021 in Computers in Biology and Medicine
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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 contain at least three drawbacks: (1) existence of non-reassigned points (NRPs), (2) low noise robustness, and (3) low amplitude accuracy. In this study, the novel TF method, termed multi-synchrosqueezing extracting transform (MSSET), is proposed to address these limitations. The proposed MSSET is divided into three steps. First, multisynchrosqueezing transform (MSST) is performed with specific iterations. Second, a synch-extracting is applied to retain the TF distribution of MSST results that relate most to time-varying information of the raw signal; meanwhile, the other smeared TF energy is discarded. Finally, the MSSET result is obtained by rounding the adjacent results at the frequency plane. Numerical verification results show that the proposed MSSET method can effectively solve the NRPs problem and enhance noise robustness. Furthermore, while retaining superior energy concentration and signal reconstruction capability, the MSSET's amplitude accuracy reaches about 90%, significantly higher than other methods. In the same conditions, the MSSET even consumes less time than MSST and IMSST. It also achieves the best composite performance with the least amplitude accuracy-time cost ratio (ATCR) index. Actual application examples in the Bat signal and the electrocardiograph (ECG) signal also validate the excellent performances of our method. To conclude, our proposed MSSET is superior to state-of-the-art methods and is expected to be widely used in the sparse-spectrum biomedical signal.

ACS Style

Yalin Wang; Wei Zhou; Xian Zhao; Chen Chen; Wei Chen. MSSET: A high-performance time-frequency analysis method for sparse-spectrum biomedical signal. Computers in Biology and Medicine 2021, 135, 104637 .

AMA Style

Yalin Wang, Wei Zhou, Xian Zhao, Chen Chen, Wei Chen. MSSET: A high-performance time-frequency analysis method for sparse-spectrum biomedical signal. Computers in Biology and Medicine. 2021; 135 ():104637.

Chicago/Turabian Style

Yalin Wang; Wei Zhou; Xian Zhao; Chen Chen; Wei Chen. 2021. "MSSET: A high-performance time-frequency analysis method for sparse-spectrum biomedical signal." Computers in Biology and Medicine 135, no. : 104637.

Journal article
Published: 06 July 2021 in Neuroscience Letters
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The fatigue-induced neuromuscular mechanism remains to be fully elucidated. So far, the macroscopic mechanism using global surface electromyogram (sEMG) has been widely investigated. However, the microscopic mechanism using high-level neural information based on motor unit (MU) spike train from the spinal cord lacks attention, especially for the conditions under dynamic contraction task. The synchronization of the MU spike train is generally assumed to be an excellent indicator to represent the activities of spinal nerves. Accordingly, this study employed synchronization of MU spike train decomposed from high-density sEMG (HD-sEMG) to investigate the fatigue condition in muscular contractions within the Biceps Brachii muscle under both isometric and dynamic contraction tasks, giving a complete picture of the microscopic fatigue mechanism. We compared the synchronization of MU in Delta (1–4 Hz), alpha (8–12 Hz), Beta (15–30 Hz), and Gamma (30–60 Hz) frequency bands during the fatigue condition induced by different contractions. Our results showed that MU synchronization increased significantly (p<0.05) in all frequency bands across the two contraction tasks. The results indicate that the microscopic fatigue mechanism of Biceps Brachii muscle does not vary due to different contraction tasks.

ACS Style

Xiangyu Liu; Meiyu Zhou; Yanjuan Geng; Long Meng; Huiying Wan; Haoran Ren; Xinyue Zhang; Chenyun Dai; Wei Chen; Xinming Ye. Changes in synchronization of the motor unit in muscle fatigue condition during the dynamic and isometric contraction in the Biceps Brachii muscle. Neuroscience Letters 2021, 761, 136101 .

AMA Style

Xiangyu Liu, Meiyu Zhou, Yanjuan Geng, Long Meng, Huiying Wan, Haoran Ren, Xinyue Zhang, Chenyun Dai, Wei Chen, Xinming Ye. Changes in synchronization of the motor unit in muscle fatigue condition during the dynamic and isometric contraction in the Biceps Brachii muscle. Neuroscience Letters. 2021; 761 ():136101.

Chicago/Turabian Style

Xiangyu Liu; Meiyu Zhou; Yanjuan Geng; Long Meng; Huiying Wan; Haoran Ren; Xinyue Zhang; Chenyun Dai; Wei Chen; Xinming Ye. 2021. "Changes in synchronization of the motor unit in muscle fatigue condition during the dynamic and isometric contraction in the Biceps Brachii muscle." Neuroscience Letters 761, no. : 136101.

Journal article
Published: 01 July 2021 in Biomedical Signal Processing and Control
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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 proposed a novel dynamic weighted “small-world” topology network and applied it to fNIRS analysis. Firstly, we introduced a novel flat-top (FT) window to estimate sliding-window correlation (SWC), improving the spectrum performances. And then, we applied the least absolute shrinkage and selection operator (LASSO) algorithm to the time-varying correlation matrix and obtained corresponding sparse matrix results. Finally, we established a dynamic weighted topological graph and calculated “small-world” network parameters to analyze the brain network dynamics. Simulation results showed that our proposed FT-based SWC method realized wider bandwidth, better spectrum performance, and more accurate dynamic tracking capability with the minimum mean square errors(MSEs) results in each iterative simulation. The fNIRS results showed that the average node degree (DE) and global efficiency (GE) reached a peak in the middle and late periods of the task. The network parameters showed similar changes in 2-back and 3-back tasks, which differ from the 0-back task. Statistical analysis revealed the DE and GE of the subnetwork decreased as the task became more difficult. These results revealed the typical time-varying characteristics of the brain network dynamics during the working memory process, which were expected to unveil the brain mechanisms underlying the cognitive neural activity in greater depth.

ACS Style

Yalin Wang; Xian Zhao; Wei Zhou; Chen Chen; Wei Chen. Dynamic weighted “small-world” graphical network establishment for fNIRS time-varying brain function analysis. Biomedical Signal Processing and Control 2021, 69, 102902 .

AMA Style

Yalin Wang, Xian Zhao, Wei Zhou, Chen Chen, Wei Chen. Dynamic weighted “small-world” graphical network establishment for fNIRS time-varying brain function analysis. Biomedical Signal Processing and Control. 2021; 69 ():102902.

Chicago/Turabian Style

Yalin Wang; Xian Zhao; Wei Zhou; Chen Chen; Wei Chen. 2021. "Dynamic weighted “small-world” graphical network establishment for fNIRS time-varying brain function analysis." Biomedical Signal Processing and Control 69, no. : 102902.

Journal article
Published: 01 June 2021 in IEEE Journal of Biomedical and Health Informatics
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Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects. The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.

ACS Style

Nannapas Banluesombatkul; Pichayoot Ouppaphan; Pitshaporn Leelaarporn; Payongkit Lakhan; Busarakum Chaitusaney; Nattapong Jaimchariyatam; Ekapol Chuangsuwanich; Wei Chen; Huy Phan; Nat Dilokthanakul; Theerawit Wilaiprasitporn. MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning. IEEE Journal of Biomedical and Health Informatics 2021, 25, 1949 -1963.

AMA Style

Nannapas Banluesombatkul, Pichayoot Ouppaphan, Pitshaporn Leelaarporn, Payongkit Lakhan, Busarakum Chaitusaney, Nattapong Jaimchariyatam, Ekapol Chuangsuwanich, Wei Chen, Huy Phan, Nat Dilokthanakul, Theerawit Wilaiprasitporn. MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning. IEEE Journal of Biomedical and Health Informatics. 2021; 25 (6):1949-1963.

Chicago/Turabian Style

Nannapas Banluesombatkul; Pichayoot Ouppaphan; Pitshaporn Leelaarporn; Payongkit Lakhan; Busarakum Chaitusaney; Nattapong Jaimchariyatam; Ekapol Chuangsuwanich; Wei Chen; Huy Phan; Nat Dilokthanakul; Theerawit Wilaiprasitporn. 2021. "MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning." IEEE Journal of Biomedical and Health Informatics 25, no. 6: 1949-1963.

Journal article
Published: 21 May 2021 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named “Hyser”), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2–5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

ACS Style

Xinyu Jiang; Xiangyu Liu; Jiahao Fan; Xinming Ye; Chenyun Dai; Edward A. Clancy; Metin Akay; Wei Chen. Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2021, 29, 1035 -1046.

AMA Style

Xinyu Jiang, Xiangyu Liu, Jiahao Fan, Xinming Ye, Chenyun Dai, Edward A. Clancy, Metin Akay, Wei Chen. Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2021; 29 (99):1035-1046.

Chicago/Turabian Style

Xinyu Jiang; Xiangyu Liu; Jiahao Fan; Xinming Ye; Chenyun Dai; Edward A. Clancy; Metin Akay; Wei Chen. 2021. "Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29, no. 99: 1035-1046.

Journal article
Published: 18 May 2021 in IEEE Reviews in Biomedical Engineering
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Atherosclerosis screening helps the medical model transform from therapeutic medicine to preventive medicine by assessing degree of atherosclerosis prior to the occurrence of fatal vascular events. Pervasive screening emphasizes atherosclerotic monitoring with easy access, quick process, and advanced computing. In this work, we introduced five cutting-edge pervasive technologies including imaging photoplethysmography (iPPG), laser Doppler, radio frequency (RF), thermal imaging (TI), optical fiber sensing and piezoelectric sensor. IPPG measures physiological parameters by using video images that record the subtle skin color changes consistent with cardiac-synchronous blood volume changes in subcutaneous arteries and capillaries. Laser Doppler obtained the information on blood flow by analyzing the spectral components of backscattered light from the illuminated tissues surface. RF is based on Doppler shift caused by the periodic movement of the chest wall induced by respiration and heartbeat. TI measures vital signs by detecting electromagnetic radiation emitted by blood flow. The working principle of optical fiber sensor is to detect the change of light properties caused by the interaction between the measured physiological parameter and the entering light. Piezoelectric sensors are based on the piezoelectric effect of dielectrics. All these pervasive technologies are noninvasive, mobile, and can detect physiological parameters related to atherosclerosis screening.

ACS Style

Jingjing Luo; Zhengrong Yan; Shijie Guo; Wei Chen. Recent Advances in Atherosclerotic Disease Screening Using Pervasive Healthcare. IEEE Reviews in Biomedical Engineering 2021, PP, 1 -1.

AMA Style

Jingjing Luo, Zhengrong Yan, Shijie Guo, Wei Chen. Recent Advances in Atherosclerotic Disease Screening Using Pervasive Healthcare. IEEE Reviews in Biomedical Engineering. 2021; PP (99):1-1.

Chicago/Turabian Style

Jingjing Luo; Zhengrong Yan; Shijie Guo; Wei Chen. 2021. "Recent Advances in Atherosclerotic Disease Screening Using Pervasive Healthcare." IEEE Reviews in Biomedical Engineering PP, no. 99: 1-1.

Article
Published: 19 April 2021 in Phenomics
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Light modulates human brain function through its effect on circadian rhythms, which are related to several human behavioral and physiological processes. Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical neuroimaging technique used for recording brain activation during task performance. This study aimed to investigate the effects of light on cognitive function, particularly in the prefrontal cortex using fNIRS. The effect of light on cognitive modulation was analyzed using the Stroop task, which was performed on 30 participants under three different light conditions (color temperature 4500 K, 2500 K, and none). The behavioral results indicated that light conditions can easily and effectively modulate the performance of tasks based on the feedback, including the response time and accuracy. fNIRS showed hemodynamic changes in the bilateral dorsolateral prefrontal cortices, and the activated brain regions varied under different light conditions. Moreover, light may be regarded as a safe, effective, inexpensive, and accessible tool for modulating human cognitive function.

ACS Style

Yafei Yuan; Guanghao Li; Haoran Ren; Wei Chen. Effect of Light on Cognitive Function During a Stroop Task Using Functional Near-Infrared Spectroscopy. Phenomics 2021, 1, 54 -61.

AMA Style

Yafei Yuan, Guanghao Li, Haoran Ren, Wei Chen. Effect of Light on Cognitive Function During a Stroop Task Using Functional Near-Infrared Spectroscopy. Phenomics. 2021; 1 (2):54-61.

Chicago/Turabian Style

Yafei Yuan; Guanghao Li; Haoran Ren; Wei Chen. 2021. "Effect of Light on Cognitive Function During a Stroop Task Using Functional Near-Infrared Spectroscopy." Phenomics 1, no. 2: 54-61.

Journal article
Published: 15 April 2021 in IEEE Journal of Biomedical and Health Informatics
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Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates. This work aims to investigate a novel video-based unobtrusive method for neonatal sleep-wake classification by analyzing the behavioral changes in the neonatal facial region. A hybrid model is proposed to monitor the sleep-wake patterns of human neonates. The model combines two algorithms: deep convolutional neural network (DCNN) and support vector machine (SVM), where DCNN works as a trainable feature extractor and SVM as a classifier. Data was collected from nineteen Chinese neonates at the Children's Hospital of Fudan University, Shanghai, China. The classification results are compared with the gold standard of video-electroencephalography scored by pediatric neurologists. Validations indicate that the proposed hybrid DCNN-SVM model achieved reliable performances in classifying neonatal sleep and wake states in RGB video frames (with the face region detected), with an accuracy of 93.8 ± 2.2% and an F1-score 0.93 ± 0.3.

ACS Style

Muhammad Awais; Xi Long; Bin Yin; Saadullah Farooq Abbasi; Saeed Akhbarzadeh; Chunmei Lu; Xinhua Wang; Laishuan Wang; Jiong Zhang; Jeroen Dudink; Wei Chen. A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expression in Video. IEEE Journal of Biomedical and Health Informatics 2021, 25, 1 -1.

AMA Style

Muhammad Awais, Xi Long, Bin Yin, Saadullah Farooq Abbasi, Saeed Akhbarzadeh, Chunmei Lu, Xinhua Wang, Laishuan Wang, Jiong Zhang, Jeroen Dudink, Wei Chen. A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expression in Video. IEEE Journal of Biomedical and Health Informatics. 2021; 25 (5):1-1.

Chicago/Turabian Style

Muhammad Awais; Xi Long; Bin Yin; Saadullah Farooq Abbasi; Saeed Akhbarzadeh; Chunmei Lu; Xinhua Wang; Laishuan Wang; Jiong Zhang; Jeroen Dudink; Wei Chen. 2021. "A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expression in Video." IEEE Journal of Biomedical and Health Informatics 25, no. 5: 1-1.

Review
Published: 12 February 2021 in IEEE Sensors Journal
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This comprehensive review mainly analyzes and summarizes the recently published works on IEEExplore in sensor-driven smart living contexts. We have gathered over 150 research papers, especially in the past five years. We categorize them into four major research directions: activity tracker, affective computing, sleep monitoring, and ingestive behavior. We report each research direction’s summary by following our defined sensor types: biomedical sensors, mechanical sensors, non-contact sensors, and others. Furthermore, the review behaves as one-stop service literature appropriate for novices who intend to research the direction of sensor-driven applications towards smart living. In conclusion, the state-of-the-art works, the publicity available data sources, and the future challenge issues (sensor selection, algorithms, and privacy) are the major contributions of this proposed article.

ACS Style

Pitshaporn Leelaarporn; Patcharapol Wachiraphan; Thitikorn Kaewlee; Tinnakit Udsa; Rattanaphon Chaisaen; Tanut Choksatchawathi; Rawipreeya Laosirirat; Payongkit Lakhan; Phantharach Natnithikarat; Kamonwan Thanontip; Wei Chen; Subhas Chandra Mukhopadhyay; Theerawit Wilaiprasitporn. Sensor-Driven Achieving of Smart Living: A Review. IEEE Sensors Journal 2021, 21, 10369 -10391.

AMA Style

Pitshaporn Leelaarporn, Patcharapol Wachiraphan, Thitikorn Kaewlee, Tinnakit Udsa, Rattanaphon Chaisaen, Tanut Choksatchawathi, Rawipreeya Laosirirat, Payongkit Lakhan, Phantharach Natnithikarat, Kamonwan Thanontip, Wei Chen, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn. Sensor-Driven Achieving of Smart Living: A Review. IEEE Sensors Journal. 2021; 21 (9):10369-10391.

Chicago/Turabian Style

Pitshaporn Leelaarporn; Patcharapol Wachiraphan; Thitikorn Kaewlee; Tinnakit Udsa; Rattanaphon Chaisaen; Tanut Choksatchawathi; Rawipreeya Laosirirat; Payongkit Lakhan; Phantharach Natnithikarat; Kamonwan Thanontip; Wei Chen; Subhas Chandra Mukhopadhyay; Theerawit Wilaiprasitporn. 2021. "Sensor-Driven Achieving of Smart Living: A Review." IEEE Sensors Journal 21, no. 9: 10369-10391.

Journal article
Published: 06 February 2021 in Biomedical Signal Processing and Control
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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 validated in clinical practice. The algorithms, especially the filter design and envelope detection methods, applied in the transformation procedure, are investigated. The effectiveness and feasibility of the filters namely, symmetric and asymmetric filters followed with four different envelope detection methods (low-pass filtering, squaring and low-pass filtering, Hilbert transform, and moving average) are evaluated on a clinical dataset collected at Children's Hospital of Fudan University which involves 30 infants. Compared to the outputs of the commercial available (a)EEG device NicoletOne, the Spearman rank correlations (SRs) of the upper/lower tracings of aEEG using the asymmetric filter and squaring and low-pass filtering-based envelope detection method can reach over 0.97. Meanwhile, the SRs of the upper and the lower margin amplitude of aEEG can achieve 0.98 and 0.97, respectively. Furthermore, the accuracy of the obtained aEEG tracings in identifying the aEEG background activities can achieve 100%. To our knowledge, this is the first work to present a digital procedure to transform the EEG into aEEG by assessing the impact of different filters and envelope detection methods. With the high performance of the proposed approach, this work can promote the standardization of aEEG transformation procedure and the exploration of the sophisticated automatic aEEG interpretation algorithms.

ACS Style

Chen Chen; Yan Xu; Zeyu Wang; Chenglu Sun; Xian Zhao; Jiahao Fan; Hendrik Niemarkt; Peter Andriessen; Laishuan Wang; Wei Chen. A digitized approach for amplitude-integrated electroencephalogram transformation towards a standardized procedure. Biomedical Signal Processing and Control 2021, 66, 102433 .

AMA Style

Chen Chen, Yan Xu, Zeyu Wang, Chenglu Sun, Xian Zhao, Jiahao Fan, Hendrik Niemarkt, Peter Andriessen, Laishuan Wang, Wei Chen. A digitized approach for amplitude-integrated electroencephalogram transformation towards a standardized procedure. Biomedical Signal Processing and Control. 2021; 66 ():102433.

Chicago/Turabian Style

Chen Chen; Yan Xu; Zeyu Wang; Chenglu Sun; Xian Zhao; Jiahao Fan; Hendrik Niemarkt; Peter Andriessen; Laishuan Wang; Wei Chen. 2021. "A digitized approach for amplitude-integrated electroencephalogram transformation towards a standardized procedure." Biomedical Signal Processing and Control 66, no. : 102433.

Journal article
Published: 29 January 2021 in Computer Methods and Programs in Biomedicine
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: 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, developing automated K-complex detection methods can alleviate the burden on clinicians while providing reliable recognition results. However, existing methods face the following issues. First, most work only identifies the K-complexes in stage 2, which requires distinguishing the sleep stages as the prerequisite for further events’ identification. Second, most approaches can only detect the occurrence of events without the ability to predict their location and duration, which are also essential to sleep analysis. : In this work, a novel hybrid expert scheme for K-complex detection is proposed by integrating signal morphology with expert knowledge into the decision-making process. To eliminate artifacts, and to minimize the individual variability in raw sleep EEG signals, the potential K-complex candidates are first screened by combining Teager energy operator (TEO) and personalized thresholds. Then, to distinguish signal shapes from background activity, a novel frame of filtering based on morphological filtering (MF) is devised to differentiate morphological components of K-complex waveforms from EEG series. Finally, K-complex waveforms are identified from the extracted morphological information by judgment rules, which are inspired by expert knowledge of micro-sleep events. : Detection performance is evaluated by its application on the public database MASS-C1 (Montreal archives of sleep studies cohort one) which includes the recordings of 19 healthy adults. The detection performance demonstrates an F-measure of 0.63 with a recall of 0.81 and a precision of 0.53 on average. The duration error between events and detections is 0.10 s. : The presented scheme has detected the occurrence of events. Meanwhile, it has recognized their locations and durations. The favorable results exhibit that the proposed scheme outperforms the state-of-the-art studies and has great potential to help release the burden of experts in sleep EEG analysis.

ACS Style

Xian Zhao; Chen Chen; Wei Zhou; Yalin Wang; Jiahao Fan; Zeyu Wang; Saeed Akbarzadeh; Wei Chen. An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex. Computer Methods and Programs in Biomedicine 2021, 201, 105955 .

AMA Style

Xian Zhao, Chen Chen, Wei Zhou, Yalin Wang, Jiahao Fan, Zeyu Wang, Saeed Akbarzadeh, Wei Chen. An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex. Computer Methods and Programs in Biomedicine. 2021; 201 ():105955.

Chicago/Turabian Style

Xian Zhao; Chen Chen; Wei Zhou; Yalin Wang; Jiahao Fan; Zeyu Wang; Saeed Akbarzadeh; Wei Chen. 2021. "An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex." Computer Methods and Programs in Biomedicine 201, no. : 105955.

Journal article
Published: 28 January 2021 in Sensors
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Hemodynamic activities, as an essential measure of physiological and psychological characteristics, can be used for cardiovascular and cerebrovascular disease detection. Photoplethysmography imaging (iPPG) can be applied for such purposes with non-contact advances, however, most cardiovascular hemodynamics of iPPG systems are developed for laboratory research, which limits the application in pervasive healthcare. In this study, a video-based facial iPPG detecting equipment was devised to provide multi-dimensional spatiotemporal hemodynamic pulsations for applications with high portability and self-monitoring requirements. A series of algorithms have also been developed for physiological indices such as heart rate and breath rate extraction, facial region analysis, and visualization of hemodynamic pulsation distribution. Results showed that the new device can provide a reliable measurement of a rich range of cardiovascular hemodynamics. Combined with the advanced computing techniques, the new non-contact iPPG system provides a promising solution for user-friendly pervasive healthcare.

ACS Style

Jingjing Luo; Junjie Zhen; Peng Zhou; Wei Chen; Yuzhu Guo. An iPPG-Based Device for Pervasive Monitoring of Multi-Dimensional Cardiovascular Hemodynamics. Sensors 2021, 21, 872 .

AMA Style

Jingjing Luo, Junjie Zhen, Peng Zhou, Wei Chen, Yuzhu Guo. An iPPG-Based Device for Pervasive Monitoring of Multi-Dimensional Cardiovascular Hemodynamics. Sensors. 2021; 21 (3):872.

Chicago/Turabian Style

Jingjing Luo; Junjie Zhen; Peng Zhou; Wei Chen; Yuzhu Guo. 2021. "An iPPG-Based Device for Pervasive Monitoring of Multi-Dimensional Cardiovascular Hemodynamics." Sensors 21, no. 3: 872.

Journal article
Published: 26 January 2021 in Sensors
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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 single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.

ACS Style

Long Meng; Anjing Zhang; Chen Chen; Xingwei Wang; Xinyu Jiang; Linkai Tao; Jiahao Fan; Xuejiao Wu; Chenyun Dai; Yiyuan Zhang; Bart Vanrumste; Toshiyo Tamura; Wei Chen. Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People. Sensors 2021, 21, 799 .

AMA Style

Long Meng, Anjing Zhang, Chen Chen, Xingwei Wang, Xinyu Jiang, Linkai Tao, Jiahao Fan, Xuejiao Wu, Chenyun Dai, Yiyuan Zhang, Bart Vanrumste, Toshiyo Tamura, Wei Chen. Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People. Sensors. 2021; 21 (3):799.

Chicago/Turabian Style

Long Meng; Anjing Zhang; Chen Chen; Xingwei Wang; Xinyu Jiang; Linkai Tao; Jiahao Fan; Xuejiao Wu; Chenyun Dai; Yiyuan Zhang; Bart Vanrumste; Toshiyo Tamura; Wei Chen. 2021. "Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People." Sensors 21, no. 3: 799.

Journal article
Published: 22 January 2021 in IEEE Transactions on Biomedical Circuits and Systems
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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 a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared to other methods, it reduces production costs and computational complexity with a smaller area of the mat and improves portability with fewer sensors. To distinguish the sleep posture, the algorithmic framework that includes pre-processing and Deep Residual Networks (ResNet) is developed. With the ResNet, the proposed system can omit the complex hand-crafted feature extraction process and provide compelling performance. The feasibility and reliability of the proposed system were evaluated on seventeen subjects. Experimental results exhibit that the accuracy of the short-term test is up to 95.08% and the overnight sleep study is up to 92.99% for four categories (supine, prone, right, and left) classification, which outperform the most of state-of-the-art studies. With the promising results, the proposed system showed great potential in applications like sleep studies, the prevention of pressure ulcers, etc.

ACS Style

Haikang Diao; Chen Chen; Wei Yuan; Amara Amara; Toshiyo Tamura; Jiahao Fan; Long Meng; Xiangyu Liu; Wei Chen. Deep Residual Networks for Sleep Posture Recognition With Unobtrusive Miniature Scale Smart Mat System. IEEE Transactions on Biomedical Circuits and Systems 2021, 15, 111 -121.

AMA Style

Haikang Diao, Chen Chen, Wei Yuan, Amara Amara, Toshiyo Tamura, Jiahao Fan, Long Meng, Xiangyu Liu, Wei Chen. Deep Residual Networks for Sleep Posture Recognition With Unobtrusive Miniature Scale Smart Mat System. IEEE Transactions on Biomedical Circuits and Systems. 2021; 15 (1):111-121.

Chicago/Turabian Style

Haikang Diao; Chen Chen; Wei Yuan; Amara Amara; Toshiyo Tamura; Jiahao Fan; Long Meng; Xiangyu Liu; Wei Chen. 2021. "Deep Residual Networks for Sleep Posture Recognition With Unobtrusive Miniature Scale Smart Mat System." IEEE Transactions on Biomedical Circuits and Systems 15, no. 1: 111-121.

Review
Published: 21 December 2020 in IEEE Sensors Journal
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The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.

ACS Style

Kawisara Ueafuea; Chiraphat Boonnag; Thapanun Sudhawiyangkul; Pitshaporn Leelaarporn; Ameen Gulistan; Wei Chen; Subhas Chandra Mukhopadhyay; Theerawit Wilaiprasitporn; Supanida Piyayotai. Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review. IEEE Sensors Journal 2020, 21, 7162 -7178.

AMA Style

Kawisara Ueafuea, Chiraphat Boonnag, Thapanun Sudhawiyangkul, Pitshaporn Leelaarporn, Ameen Gulistan, Wei Chen, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn, Supanida Piyayotai. Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review. IEEE Sensors Journal. 2020; 21 (6):7162-7178.

Chicago/Turabian Style

Kawisara Ueafuea; Chiraphat Boonnag; Thapanun Sudhawiyangkul; Pitshaporn Leelaarporn; Ameen Gulistan; Wei Chen; Subhas Chandra Mukhopadhyay; Theerawit Wilaiprasitporn; Supanida Piyayotai. 2020. "Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review." IEEE Sensors Journal 21, no. 6: 7162-7178.

Review
Published: 13 December 2020 in Computers in Biology and Medicine
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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 period of time, thus effecting their daily lives. Vital health parameters, such as heart rate, respiratory rate, SpO2 and blood pressure, are closely associated with patients’ conditions. Wearable devices and unobtrusive sensing technologies can detect such parameters in a convenient way and provide timely predictions on health condition deterioration by tracking these biomedical signals and health parameters. In this paper, we review current advancements in wearable devices and unobtrusive sensing technologies that can provides possible tools and technological supports for chronic disease management. Current challenges and future directions of related techniques are addressed accordingly.

ACS Style

Yao Guo; Xiangyu Liu; Shun Peng; Xinyu Jiang; Ke Xu; Chen Chen; Zeyu Wang; Chenyun Dai; Wei Chen. A review of wearable and unobtrusive sensing technologies for chronic disease management. Computers in Biology and Medicine 2020, 129, 104163 -104163.

AMA Style

Yao Guo, Xiangyu Liu, Shun Peng, Xinyu Jiang, Ke Xu, Chen Chen, Zeyu Wang, Chenyun Dai, Wei Chen. A review of wearable and unobtrusive sensing technologies for chronic disease management. Computers in Biology and Medicine. 2020; 129 ():104163-104163.

Chicago/Turabian Style

Yao Guo; Xiangyu Liu; Shun Peng; Xinyu Jiang; Ke Xu; Chen Chen; Zeyu Wang; Chenyun Dai; Wei Chen. 2020. "A review of wearable and unobtrusive sensing technologies for chronic disease management." Computers in Biology and Medicine 129, no. : 104163-104163.

Research article
Published: 21 November 2020 in Neural Plasticity
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Acting as a brain stimulant, coffee resulted in heightening alertness, keeping arousal, improving executive speed, maintaining vigilance, and promoting memory, which are associated with attention, mood, and cognitive function. Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical method to monitor brain activity by measuring the absorption of the near-infrared light through the intact skull. This study is aimed at acquiring brain activation during executing task performance. The aim is to explore the effect of coffee on cognitive function by the fNIRS neuroimaging method, particularly on the prefrontal cortex regions. The behavioral experimental results on 31 healthy subjects with a Stroop task indicate that coffee can easily and effectively modulate the execute task performance by feedback information of the response time and accuracy rate. The findings of fNIRS showed that apparent hemodynamic changes were detected in the bilateral VLPFC regions and the brain activation regions varied with different coffee conditions.

ACS Style

Yafei Yuan; Guanghao Li; Haoran Ren; Wei Chen. Caffeine Effect on Cognitive Function during a Stroop Task: fNIRS Study. Neural Plasticity 2020, 2020, 1 -8.

AMA Style

Yafei Yuan, Guanghao Li, Haoran Ren, Wei Chen. Caffeine Effect on Cognitive Function during a Stroop Task: fNIRS Study. Neural Plasticity. 2020; 2020 ():1-8.

Chicago/Turabian Style

Yafei Yuan; Guanghao Li; Haoran Ren; Wei Chen. 2020. "Caffeine Effect on Cognitive Function during a Stroop Task: fNIRS Study." Neural Plasticity 2020, no. : 1-8.

Neurology
Published: 19 November 2020 in Frontiers in Neurology
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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 of technique and experimental design. Neurologists are convinced that Near-infrared spectroscopy (NIRS) provides essential information and can be used to assist the assessment of cerebral hemodynamics, and numerous studies regarding sleep have been carried out based on NIRS. Thus, a brief historical overview of the sleep studies using NIRS will be helpful for the biomedical students, academicians, and engineers to better understand NIRS from various perspectives. In this study, the existing literature on sleep studies is reviewed, and an overview of the NIRS applications is synthesized and provided. The paper first reviews the application scenarios, as well as the patterns of fluctuation of NIRS, which includes the investigation in regular sleep and sleep-disordered breathing. Various factors such as different sleep stages, populations, and degrees of severity were considered. Furthermore, the experimental design and signal processing, as well as the regulation mechanisms involved in regular and pathological sleep, are investigated and discussed. The strengths and weaknesses of the existing NIRS applications are addressed and presented, which can direct further NIRS analysis and utilization.

ACS Style

Haoran Ren; Xinyu Jiang; Ke Xu; Chen Chen; Yafei Yuan; Chenyun Dai; Wei Chen. A Review of Cerebral Hemodynamics During Sleep Using Near-Infrared Spectroscopy. Frontiers in Neurology 2020, 11, 1 .

AMA Style

Haoran Ren, Xinyu Jiang, Ke Xu, Chen Chen, Yafei Yuan, Chenyun Dai, Wei Chen. A Review of Cerebral Hemodynamics During Sleep Using Near-Infrared Spectroscopy. Frontiers in Neurology. 2020; 11 ():1.

Chicago/Turabian Style

Haoran Ren; Xinyu Jiang; Ke Xu; Chen Chen; Yafei Yuan; Chenyun Dai; Wei Chen. 2020. "A Review of Cerebral Hemodynamics During Sleep Using Near-Infrared Spectroscopy." Frontiers in Neurology 11, no. : 1.

Research note
Published: 04 November 2020 in BMC Research Notes
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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 video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke® video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required.

ACS Style

Muhammad Awais; Xi Long; Bin Yin; Chen Chen; Saeed Akbarzadeh; Saadullah Farooq Abbasi; Muhammad Irfan; Chunmei Lu; Xinhua Wang; Laishuan Wang; Wei Chen. Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification? BMC Research Notes 2020, 13, 1 -6.

AMA Style

Muhammad Awais, Xi Long, Bin Yin, Chen Chen, Saeed Akbarzadeh, Saadullah Farooq Abbasi, Muhammad Irfan, Chunmei Lu, Xinhua Wang, Laishuan Wang, Wei Chen. Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification? BMC Research Notes. 2020; 13 (1):1-6.

Chicago/Turabian Style

Muhammad Awais; Xi Long; Bin Yin; Chen Chen; Saeed Akbarzadeh; Saadullah Farooq Abbasi; Muhammad Irfan; Chunmei Lu; Xinhua Wang; Laishuan Wang; Wei Chen. 2020. "Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?" BMC Research Notes 13, no. 1: 1-6.