Activating Adsorption Sites of Waste Crayfish Shells via Chemical Decalcification for Efficient Capturing of Nanoplastics.

The property of being stubborn and degradation resistant makes nanoplastic (NP) pollution a long-standing remaining challenge. Here, we apply a designed top-down strategy to leverage the natural hierarchical structure of waste crayfish shells with exposed functional groups for efficient NP capture. The crayfish shell-based organic skeleton with improved flexibility, strength (14.37 to 60.13 MPa), and toughness (24.61 to 278.98 MJ m-3) was prepared by purposefully removing the inorganic components of crayfish shells through a simple two-step acid-alkali treatment. Due to the activated functional groups (e.g., -NH2, -CONH-, and -OH) and ordered architectures with macropores and nanofibers, this porous crayfish shell exhibited effective removal capability of NPs (72.92 mg g-1) by physical interception and hydrogen bond/electrostatic interactions. Moreover, the sustainability and stability of this porous crayfish shell were demonstrated by the maintained high-capture performance after five cycles. Finally, we provided a postprocessing approach that could convert both porous crayfish shell and NPs into a tough flat sheet. Thus, our feasible top-down engineering strategy combined with promising posttreatment is a powerful contender for a recycling approach with broad application scenarios and clear economic advantages for simultaneously addressing both waste biomass and NP pollutants.

Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach.

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires a deep understanding of the data's intrinsic domain. Therefore, this study presents an Advanced Multi-View Deep Feature Learning (AMV-DFL) framework based on machine learning (ML) technology to enhance the detection of relevant EEG signal features for ES. Our method initially applies a fast Fourier transform (FFT) to EEG data for traditional frequency domain feature (TFD-F) extraction and directly incorporates time domain (TD) features from the raw EEG signals, establishing a comprehensive traditional multi-view feature (TMV-F). Deep features are subsequently extracted autonomously from optimal layers of one-dimensional convolutional neural networks (1D CNN), resulting in multi-view deep features (MV-DF) integrating both time and frequency domains. A multi-view forest (MV-F) is an interpretable rule-based advanced ML classifier used to construct a robust, generalized classification. Tree-based SHAP explainable artificial intelligence (T-XAI) is incorporated for interpreting and explaining the underlying rules. Experimental results confirm our method's superiority, surpassing models using TMV-FL and single-view deep features (SV-DF) by 4% and outperforming other state-of-the-art methods by an average of 3% in classification accuracy. The AMV-DFL approach aids clinicians in identifying EEG features indicative of ES, potentially discovering novel biomarkers, and improving diagnostic capabilities in epilepsy management.

A Secure and Interpretable AI for Smart Healthcare System: A Case Study on Epilepsy Diagnosis Using EEG Signals.

The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.

Adhesive Carbon Paste Electrode for Convenient and Long-term Electrocardiogram Monitoring: A Pilot Study.

Long-term electrocardiogram (ECG) monitoring is an important and widely-used technique in the clinic that helps with the diagnosis of possible diseases that cannot be detected in a short time monitoring. However, the clinically used electrode needs conductive gel to reduce the impedance between the skin and the electrodes, which easily causes the possibility of allergy. Moreover, as the conductive gel becomes dry, the signal's quality will decrease accordingly. In this paper, we proposed a novel adhesive Carbon Paste Electrode (CPE) to achieve convenient and long-term ECG monitoring. By comparing the time-domain waveforms, the R-R peak intervals difference, and the Signal-to-Noise Ratio (SNR) of ECG with the traditional conductive gel-based electrode (Gel) in fixed and unfixed conditions, the performance of the proposed CPE was investigated. The results showed that the CPE could achieve similar ECG monitoring both in fixed and unfixed conditions. When on Day 2, the quality acquired by Gel began to decrease while CPE was still stable, which was obvious especially in unfixed condition. The R-R peak intervals showed that on Day 2, the Gel was unreliable with some abnormal points occurring. Besides, the results of SNR and average heart rate (AHR) also confirmed that the CPE could achieve similar results as Gel on Day 1 and outperformed Gel on Day 2. It is believed that the proposed CPE opens a window of high-quality long-term ECG monitoring with more convenience.

Highly Anthropomorphic Finger Design With a Novel Friction Clutch for Achieving Human-Like Reach-and-Grasp Movements.

In the design of prosthetic hand fingers, achieving human-like movement while meeting anthropomorphic demands such as appearance, size, and lightweight is quite challenging. Human finger movement involves two distinct motion characters during natural reach-and-grasp tasks: consistency in the reaching stage and adaptability in the grasping stage. The former one enhances grasp stability and reduces control complexity; the latter one promotes the adaptability of finger to various objects. However, conventional tendon-driven prosthetic finger designs typically incorporate bulky actuation modules or complex tendon routes to reconcile the consistency and adaptability. In contrast, we propose a novel friction clutch consisting of a single tendon and slider, which is simple and compact enough to be configurated within the metacarpal bone. Through tactfully exploiting the friction force to balance the gravity effect on each phalanx during finger motion, this design effectively combines both consistency and adaptability. As a result, the prosthetic finger can maintain consistent motion unaffected by any spatial posture during reaching, execute adaptive motion during grasping, and automatically switch between them, resulting in human-like reach-and-grasp movements. Additionally, the proposed finger achieves a highly anthropomorphic design, weighing only 18.9 g and possessing the same size as an adult's middle finger. Finally, a series of experiments validate the theoretical effectiveness and motion performance of the proposed design. Remarkably, the mechanical principle of the proposed friction clutch is beneficial to achieve highly anthropomorphic design, providing not only a new strategy to prosthetic hand design but also great potential in hand rehabilitation.

A Scalable Video Multicast Scheme Based on User Demand Perception and D2D Communication.

With the widespread application of 5G technology, there has been a significant surge in wireless video service demand and video traffic due to the proliferation of smart terminal devices and multimedia applications. However, the complexity of terminal devices, heterogeneous transmission channels, and the rapid growth of video traffic present new challenges for wireless network-based video applications. Although scalable video coding technology effectively improves video transmission efficiency in complex networks, traditional cellular base stations may struggle to handle video transmissions for all users simultaneously, particularly in large-scale networks. To tackle this issue, we propose a scalable video multicast scheme based on user demand perception and Device-to-Device (D2D) communication, aiming to enhance the D2D multicast network transmission performance of scalable videos in cellular D2D hybrid networks. Firstly, we analyze user interests by considering their video viewing history and factors like video popularity to determine their willingness for video pushing, thereby increasing the number of users receiving multicast clusters. Secondly, we design a cluster head selection algorithm that considers users' channel quality, social parameters, and video quality requirements. Performance results demonstrate that the proposed scheme effectively attracts potential request users to join multicast clusters, increases the number of users in the clusters, and meets diverse user demands for video quality.

Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model∗.

Objective. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in adolescents that can seriously impair a person's attention function, cognitive processes, and learning ability. Currently, clinicians primarily diagnose patients based on the subjective assessments of the Diagnostic and Statistical Manual of Mental Disorders-5, which can lead to delayed diagnosis of ADHD and even misdiagnosis due to low diagnostic efficiency and lack of well-trained diagnostic experts. Deep learning of electroencephalogram (EEG) signals recorded from ADHD patients could provide an objective and accurate method to assist physicians in clinical diagnosis.Approach. This paper proposes the EEG-Transformer deep learning model, which is based on the attention mechanism in the traditional Transformer model, and can perform feature extraction and signal classification processing for the characteristics of EEG signals. A comprehensive comparison was made between the proposed transformer model and three existing convolutional neural network models.Main results. The results showed that the proposed EEG-Transformer model achieved an average accuracy of 95.85% and an average AUC value of 0.9926 with the fastest convergence speed, outperforming the other three models. The function and relationship of each module of the model are studied by ablation experiments. The model with optimal performance was identified by the optimization experiment.Significance. The EEG-Transformer model proposed in this paper can be used as an auxiliary tool for clinical diagnosis of ADHD, and at the same time provides a basic model for transferable learning in the field of EEG signal classification.

A Microservice and Serverless Architecture for Secure IoT System.

In cross-border transactions, the transmission and processing of logistics information directly affect the trading experience and efficiency. The use of Internet of Things (IoT) technology can make this process more intelligent, efficient, and secure. However, most traditional IoT logistics systems are provided by a single logistics company. These independent systems need to withstand high computing loads and network bandwidth when processing large-scale data. Additionally, due to the complex network environment of cross-border transactions, the platform's information security and system security are difficult to guarantee. To address these challenges, this paper designs and implements an intelligent cross-border logistics system platform that combines serverless architecture and microservice technology. This system can uniformly distribute the services of all logistics companies and divide microservices based on actual business needs. It also studies and designs corresponding Application Programming Interface (API) gateways to solve the interface exposure problem of microservices, thereby ensuring the system's security. Furthermore, asymmetric encryption technology is used in the serverless architecture to ensure the security of cross-border logistics data. The experiments show that this research solution validates the advantages of combining serverless architecture and microservices, which can significantly reduce the operating costs and system complexity of the platform in cross-border logistics scenarios. It allows for resource expansion and billing based on application program requirements at runtime. The platform can effectively improve the security of cross-border logistics service processes and meet cross-border transaction needs in terms of data security, throughput, and latency.

High-Precision and Rapid Direct Laser Writing Using a Liquid Two-Photon Polymerization Initiator.

Two-photon polymerization based direct laser writing (DLW) is an emerging micronano 3D fabrication technology wherein two-photon initiators (TPIs) are a key component in photoresists. Upon exposure to a femtosecond laser, TPIs can trigger the polymerization reaction, leading to the solidification of photoresists. In other words, TPIs directly determine the rate of polymerization, physicochemical properties of polymers, and even the photolithography feature size. However, they generally exhibit extremely poor solubility in photoresist systems, severely inhibiting their application in DLW. To break through this bottleneck, we propose a strategy to prepare TPIs as liquids via molecular design. The maximum weight fraction of the as-prepared liquid TPI in photoresist significantly increases to 2.0 wt %, which is several times higher than that of commercial 7-diethylamino-3-thenoylcoumarin (DETC). Meanwhile, this liquid TPI also exhibits an excellent absorption cross section (64 GM), allowing it to absorb femtosecond laser efficiently and generate abundant active species to initiate polymerization. Remarkably, the respective minimum feature sizes of line arrays and suspended lines are 47 and 20 nm, which are comparable to that of the-state-of-the-art electron beam lithography. Besides, the liquid TPI can be utilized to fabricate various high-quality 3D microstructures and manufacture large-area 2D devices at a considerable writing speed (1.045 m s-1). Therefore, the liquid TPI would be one of the promising initiators for micronano fabrication technology and pave the way for future development of DLW.

Using Flexible and Stretchable Surface Electromyography Electrode Array to Evaluate Congenital Muscular Torticollis in Children.

Congenital Muscular Torticollis (CMT) is a neuromuscular disease in children, which leads to exacerbation of postural deformity and neck muscle dysfunction with age. Towards facilitating functional assessment of neuromuscular disease in children, topographic electromyography (EMG) maps enabled by flexible and stretchable surface EMG (sEMG) electrode arrays are used to evaluate the neck myoelectric activities in this study. Customed flexible and stretchable sEMG electrode arrays with 84 electrodes were utilized to record sEMG in all subjects during neck motion tasks. Clinical parameter assessments including the cervical range of motion (ROM), sonograms of the sternocleidomastoid (SCM), and corresponding histological analysis were also performed to evaluate the CMT. The muscle activation patterns of neck myoelectric activities between the CMT patients and the healthy subjects were asymmetric during different neck motion tasks. The CMT patients presented significantly lower values in spatial features of two-dimensional (2D) correlation coefficient, left/right energy ratio, and left/right energy difference (p < 0.001). The 2D correlation coefficient of activation patterns of neck rotation and extension in CMT patients significantly correlated with clinical parameter assessments (p < 0.05). The findings suggest that the spatial features of muscle activation patterns based on the sEMG electrode arrays can be utilized to evaluate the CMT. The flexible and stretchable sEMG electrode array is promising to facilitate the functional evaluation and treatment strategies for children with neuromuscular disease.

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG signals.

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques.

Hairy-Skin-Adaptive Viscoelastic Dry Electrodes for Long-Term Electrophysiological Monitoring.

Long-term epidermal electrophysiological (EP) monitoring is crucial for disease diagnosis and human-machine synergy. The human skin is covered with hair that grows at an average rate of 0.3 mm per day. This impedes a stable contact between the skin and dry epidermal electrodes, resulting in motion artifacts during ultralong-term EP monitoring. Therefore, accurate and high-quality EP signal detection remains challenging. To address this issue, a new solution-the hairy-skin-adaptive viscoelastic dry electrode (VDE) is reported. This innovative technology is capable of bypassing hair and filling into the skin wrinkles, leading to long-lasting and stable interface impedance. The VDE maintains a stable interface impedance for a remarkable period of 48 days and 100 cycles. The VDE is highly effective in shielding against hair disturbances in electrocardiography (ECG) monitoring, even during intense chest expansion, and in electromyography (EMG) monitoring during large strain. Furthermore, the VDE is easily attachable to the skull without requiring any electroencephalogram (EEG) cap or bandage, making it an ideal solution for EEG monitoring. This work represents a substantial breakthrough in the field of EP monitoring, providing a solution for the previously challenging issue of monitoring human EP signals on hairy skin.

Ascertaining the optimal myoelectric signal recording duration for pattern recognition based prostheses control.

Introduction: Electromyogram-based pattern recognition (EMG-PR) has been widely considered an essentially intuitive control method for multifunctional upper limb prostheses. A crucial aspect of the scheme is the EMG signal recording duration (SRD) from which requisite motor tasks are characterized per time, impacting the system's overall performance. For instance, lengthy SRD inevitably introduces fatigue (that alters the muscle contraction patterns of specific limb motions) and may incur high computational costs in building the motion intent decoder, resulting in inadequate prosthetic control and controller delay in practical usage. Conversely, relatively shorter SRD may lead to reduced data collection durations that, among other advantages, allow for more convenient prosthesis recalibration protocols. Therefore, determining the optimal SRD required to characterize limb motion intents adequately that will aid intuitive PR-based control remains an open research question. Method: This study systematically investigated the impact and generalizability of varying lengths of myoelectric SRD on the characterization of multiple classes of finger gestures. The investigation involved characterizing fifteen classes of finger gestures performed by eight normally limb subjects using various groups of EMG SRD including 1, 5, 10, 15, and 20 s. Two different training strategies including Between SRD and Within-SRD were implemented across three popular machine learning classifiers and three time-domain features to investigate the impact of SRD on EMG-PR motion intent decoder. Result: The between-SRD strategy results which is a reflection of the practical scenario showed that an SRD greater than 5 s but less than or equal to 10 s (>5 and < = 10 s) would be required to achieve decent average finger gesture decoding accuracy for all feature-classifier combinations. Notably, lengthier SRD would incur more acquisition and implementation time and vice-versa. In inclusion, the study's findings provide insight and guidance into selecting appropriate SRD that would aid inadequate characterization of multiple classes of limb motion tasks in PR-based control schemes for multifunctional prostheses.

[A neonatal intelligent regulation system based on the combination of mild hypothermia mattress and hyperbaric oxygen chamber: introduction to a patent]. / 基于亚低温床垫与高压氧舱结合的新生儿智能调控系统.

Neonatal hypoxic-ischemic encephalopathy (HIE) is a common disease that affects brain function in neonates. At present, mild hypothermia and hyperbaric oxygen therapy are the main methods for the treatment of neonatal HIE; however, they are independent of each other and cannot be combined for synchronous treatment, without monitoring of brain function-related physiological information. In addition, parameter setting of hyperbaric oxygen chamber and mild hypothermia mattress relies on the experience of the medical practitioner, and the parameters remain unchanged throughout the medical process. This article proposes a new device for the treatment of neonatal HIE, which has the modules of hyperbaric oxygen chamber and mild hypothermic mattress, so that neonates can receive the treatment of hyperbaric oxygen chamber and/or mild hypothermic mattress based on their conditions. Meanwhile, it can realize the real-time monitoring of various physiological information, including amplitude-integrated electroencephalogram, electrocardiogram, and near-infrared spectrum, which can monitor brain function, heart rate, rhythm, myocardial blood supply, hemoglobin concentration in brain tissue, and blood oxygen saturation. In combination with an intelligent control algorithm, the device can intelligently regulate parameters according to the physiological information of neonates and give recommendations for subsequent treatment.

In situ forming oxygen/ROS-responsive niche-like hydrogel enabling gelation-triggered chemotherapy and inhibition of metastasis.

Though the development of the diverse hypoxia-activated prodrugs (HAPs) has made great progresses in the last several decades, current cancer therapy based on HAPs still suffers many obstacles, e.g., poor therapeutic outcome owing to hard deep reaching to hypoxic region, and the occurrence of metastasis due to hypoxia. Inspired by engineered niches, a novel functional chitosan polymer (CS-FTP) is synthesized for construction of a hydrogel-based bio-niche (CS-FTP-gel) in aiming at remodeling tumor hypoxic microenvironment. The CS-FTP polymers are crosslinked to form a niche-like hydrogel via enzyme-mediated oxygen-consumable dimerization after injected into tumor, in which a HAP (i.e., AQ4N) could be physically encapsulated, resulting in enhanced tumor hypoxia to facilitate AQ4N-AQ4 toxic transformation for maximizing efficacy of chemotherapy. Furthermore, Pazopanib (PAZ) conjugated onto the CS backbone via ROS-sensitive linker undergoes a stimuli-responsive release behavior to promote antiangiogenesis for tumor starvation, eventually contributing to the inhibition of lung metastasis and synergistic action with AQ4N-based chemotherapy for an orthotopic 4T1 breast tumor model. This study provides a promising strategy for hypoxia-based chemotherapy and demonstrates an encouraging clinical potential for multifunctional hydrogel applicable for antitumor treatment.

Towards Optimizing the Quality of Long-Term Physiological Signals Monitoring by Using Anhydrous Carbon Paste Electrode.

Long-term physiological signal monitoring is very important for the diagnosis of health conditions that occur randomly and cannot be easily detected by a short period of a hospital visit. However, the conventional wet electrodes suffered from the problem of signal quality degradation due to the gradual dehydration of the conductive gel. An anhydrous carbon paste electrode (CPE) constructed by a composite of carbon black and polydimethylsiloxane was proposed to enable long-term physiological signal monitoring without signal quality degradation as time elapses. The performance was systematically compared with conventional electrodes when measuring long-term physiological signals including electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and auditory brainstem response (ABR). The proposed CPE showed more stable skin-electrode impedance and higher signal qualities as the monitoring time increased up to 48 days, with signal-to-noise ratios (SNRs) of 16.43 ± 10.39 dB higher for ECG and 24.30 ± 7.79 dB higher for EMG when compared with wet electrodes. The CPE method could also obtain more consistent ABR waveform morphologies and could measure EEG under sweating conditions. It is believed that the proposed CPE could be a potential candidate for durable and robust wearable sensors systems on long-term physiological signal monitoring.

Usefulness of phase gradients of otoacoustic emissions in auditory health screening: An exploration with swept tones.

Otoacoustic emissions (OAEs) are low-level sounds generated by the cochlea and widely used as a noninvasive tool to inspect cochlear impairments. However, only the amplitude information of OAE signals is used in current clinical tests, while the OAE phase containing important information about cochlear functions is commonly discarded, due to the insufficient frequency-resolution of existing OAE tests. In this study, swept tones with time-varying frequencies were used to measure stimulus frequency OAEs (SFOAEs) in human subjects, so that high-resolution phase spectra that are not available in existing OAE tests could be obtained and analyzed. The results showed that the phase of swept-tone SFOAEs demonstrated steep gradients as the frequency increased in human subjects with normal hearing. The steep phase gradients were sensitive to auditory functional abnormality caused by cochlear damage and stimulus artifacts introduced by system distortions. At low stimulus levels, the group delays derived from the phase gradients decreased from around 8.5 to 3 ms as the frequency increased from 1 to 10 kHz for subjects with normal hearing, and the pattern of group-delay versus frequency function showed significant difference for subjects with hearing loss. By using the swept-tone technology, the study suggests that the OAE phase gradients could provide highly sensitive information about the cochlear functions and therefore should be integrated into the conventional methods to improve the reliability of auditory health screening.

Towards Evaluating Pitch-Related Phonation Function in Speech Communication Using High-Density Surface Electromyography.

Pitch, as a sensation of the sound frequency, is a crucial attribute toward constructing a natural voice for communication. Producing intelligible sounds with normal pitches depend on substantive interdependencies among facial and neck muscles. Clarifying the interrelations between the pitches and the corresponding muscular activities would be helpful for evaluating the pitch-related phonating functions, which would play a significant role both in training pronunciation and in assessing dysphonia. In this study, the speech signals and the high-density surface electromyography (HD sEMG) signals were synchronously acquired when phonating [a:], [i:], and [ә:] vowels with increasing pitches, respectively. The HD sEMG energy maps were constructed based on the root mean square values to visualize spatiotemporal characteristics of facial and neck muscle activities. Normalized median frequency (nMF) and root-mean square (nRMS) were correspondingly extracted from the speech and sEMG recordings to quantitatively investigate the correlations between sound frequencies and myoelectric characteristics. The results showed that the frame-wise energy maps built from sEMG recordings presented that the muscle contraction strength increased monotonously across pitch-rising, with left-right symmetrical distribution for the face/neck. Furthermore, the nRMS increased at a similar rate to the nMF when there were rising pitches, and the two parameters had a significant correlation across different vowel tasks [(a:) (0.88 ± 0.04), (i:) (0.89 ± 0.04), and (ә:) (0.87 ± 0.05)]. These findings suggested the possibility of utilizing muscle contraction patterns as a reference for evaluating pitch-related phonation functions. The proposed method could open a new window for developing a clinical approach for assessing the muscular functions of dysphonia.

Towards high-accuracy classifying attention-deficit/hyperactivity disorders using CNN-LSTM model.

Objective. The neurocognitive attention functions involve the cooperation of multiple brain regions, and the defects in the cooperation will lead to attention-deficit/hyperactivity disorder (ADHD), which is one of the most common neuropsychiatric disorders for children. The current ADHD diagnosis is mainly based on a subjective evaluation that is easily biased by the experience of the clinicians and lacks the support of objective indicators. The purpose of this study is to propose a method that can effectively identify children with ADHD.Approach. In this study, we proposed a CNN-LSTM model to solve the three-class problems of classifying ADHD, attention deficit disorder (ADD) and healthy children, based on a public electroencephalogram (EEG) dataset that includes event-related potential (ERP) EEG signals of 144 children. The convolution visualization and saliency map methods were used to observe the features automatically extracted by the proposed model, which could intuitively explain how the model distinguished different groups.Main results. The results showed that our CNN-LSTM model could achieve an accuracy as high as 98.23% in a five-fold cross-validation method, which was significantly better than the current state-of-the-art CNN models. The features extracted by the proposed model were mainly located in the frontal and central areas, with significant differences in the time period mappings among the three different groups. The P300 and contingent negative variation (CNV) in the frontal lobe had the largest decrease in the healthy control (HC) group, and the ADD group had the smallest decrease. In the central area, only the HC group had a significant negative oscillation of CNV waves.Significance. The results of this study suggest that the CNN-LSTM model can effectively identify children with ADHD and its subtypes. The visualized features automatically extracted by this model could better explain the differences in the ERP response among different groups, which is more convincing than previous studies, and it could be used as more reliable neural biomarkers to help with more accurate diagnosis in the clinics.

Flexible Non-contact Electrodes for Wearable Biosensors System on Electrocardiogram Monitoring in Motion.

Electrocardiogram (ECG) is a critical physiological indicator that contains abundant information about human heart activities. However, it is a kind of weak low-frequency signal, which is easy to be interfered by various noises. Therefore, wearable biosensors (WBS) technique is introduced to overcome this challenge. A flexible non-contact electrode is proposed for wearable biosensors (WBS) system, which is made up of flexible printed circuits materials, and can monitor the ECG signals during exercise for a long time. It uses the principle of capacitive coupling to obtain high-quality signals, and reduces the impact of external noise through active shielding; The results showed that the proposed non-contact electrode was equivalent to a medical wet electrode. The correlation coefficient was as high as 99.70 ± 0.30% when the subject was resting, while it was as high as 97.53 ± 1.80% during exercise. High-quality ECG could still be collected at subjects walking at 7 km/h. This study suggested that the proposed flexible non-contact electrode would be a potential tool for wearable biosensors for medical application on long-term monitoring of patients' health and provide athletes with physiological signal measurements.