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Qiang Fang

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9 papers

2 author rows

JBHI Journal 2025 Journal Article

A Novel Approach for Aphasia Evaluation Based on ROI-Based Features From Structural Magnetic Resonance Image

Ying Dan
Aiqun Cai
Jiaxin Ma
Yuming Zhong
Seedahmed S. Mahmoud
Qiang Fang

Aphasia, affecting one-third of stroke survivors, impairs language comprehension and speech production, leading to challenges in daily interactions, social isolation, and economic losses. Assessing aphasia is crucial for effective rehabilitation and recovery in patients. However, the conventional behavioral-based evaluation, reliant on speech pathologists, is susceptible to individual variability, resulting in high labor costs, time-consuming processes, and low robustness. To address these limitations, this study introduces a novel evaluation method based on medical image processing and artificial intelligence. Magnetic resonance imaging (MRI) provides exceptional spatial resolution while mitigating the impact of individual variability. The image processing techniques were employed to extract pathological features, specifically region-of-interest (ROI)-based features. Subsequently, the evaluation models were trained using ROI-based features which initially identify the occurrence of aphasia and then categorize the type of aphasia, aiding clinicians in tailoring treatment to various therapeutic approaches and intensities. The evaluation models also predict the severity and generate scores for four types of language function: spontaneous speech, auditory comprehension, naming, and repetition. Both aphasia occurrence detection and aphasia type classification attain impressive accuracy rates of 100. 00 $\pm$ 0. 00% and 85. 00 $\pm$ 13. 23%, respectively. The severity prediction yields the lowest root mean square error (RMSE) of 17. 03 $\pm$ 2. 75, while the assessment of four language functions achieves the best RMSE of 1. 27 $\pm$ 0. 82. Utilising the advantages of a medical imaging-based automation approach, the proposed aphasia evaluation method provides a comprehensive procedure and generates rather accurate results. Hence it could assist the aphasia rehabilitation and substantially reduce clinicians' workload.

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EAAI Journal 2025 Journal Article

Region-based weighting-and-enhancement network with adaptive class weighting loss for postoperative inguinal hernia prediction

Jiawei Zhang
Lisheng Wu
Qiang Fang
Weidong Yu
Zhengyu Hu
Fengyun Zhang
Cheng Yang
Xiaoqing Zhang

Postoperative inguinal hernia (PIH) is a common complication after radical prostatectomy, subsequently leading to multiple potential risks (e. g. , cardiovascular and cerebrovascular accidents) and increased surgical costs due to re-surgical reparation. Magnetic resonance imaging (MRI) examination is a widely used procedure before radical prostatectomy, which can investigate the muscle structures of the abdominal wall (MSAW). Recently, clinical studies have indicated that clinical parameters (e. g. , thickness and width of the external oblique muscle) of MSAW are strongly related to PIH. However, automated MRI-based PIH prediction based on deep neural networks has not been studied previously. Motivated by these observations, we propose a novel region-based weighting-and-enhancement network to predict PIH before radical prostatectomy based on MRI images automatically. Specifically, we employ the well-designed Region Weighting-and-Enhancement module to capture informative context representations through region weighting and regional context enhancement, by fully leveraging the potential of clinical MSAW priori. Additionally, this paper designs an effective adaptive class weighting loss to emphasize or suppress the samples with varying levels of significance to further boost the PIH prediction performance. The extensive experiments on a clinical MRI-PIH dataset and one publicly available MRI dataset manifest the superiority of our proposed methods over state-of-the-art deep neural networks and advanced loss methods.

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JBHI Journal 2024 Journal Article

Dynamic Reconfiguration of Brain Functional Network in Stroke

Kaichao Wu
Beth Jelfs
Katrina Neville
Seedahmed S. Mahmoud
Wenzhen He
Qiang Fang

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer temporal network method, a dynamic modular structure was recognized based on a time-resolved function network. The dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, revealing the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It's also noted that previous studies using static methods could not reveal this severity-dependent alteration in network interaction. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain neuronal images could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The present method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction.

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YNIMG Journal 2024 Journal Article

Meso-scale reorganization of local–global brain networks under mild sedation of propofol anesthesia

Kangli Dong
Lu Zhang
Yuming Zhong
Tao Xu
Yue Zhao
Siya Chen
Seedahmed S. Mahmoud
Qiang Fang

The fragmentation of the functional brain network has been identified through the functional connectivity (FC) analysis in studies investigating anesthesia-induced loss of consciousness (LOC). However, it remains unclear whether mild sedation of anesthesia can cause similar effects. This paper aims to explore the changes in local-global brain network topology during mild anesthesia, to better understand the macroscopic neural mechanism underlying anesthesia sedation. We analyzed high-density EEG from 20 participants undergoing mild and moderate sedation of propofol anesthesia. By employing a local-global brain parcellation in EEG source analysis, we established binary functional brain networks for each participant. Furthermore, we investigated the global-scale properties of brain networks by estimating global efficiency and modularity, and examined the changes in meso-scale properties of brain networks by quantifying the distribution of high-degree and high-betweenness hubs and their corresponding rich-club coefficients. It is evident from the results that the mild sedation of anesthesia does not cause a significant change in the global-scale properties of brain networks. However, network components centered on SomMot L show a significant decrease, while those centered on Default L, Vis L and Limbic L exhibit a significant increase during the transition from wakefulness to mild sedation (p<0.05). Compared to the baseline state, mild sedation almost doubled the number of high-degree hubs in Vis L, DorsAttn L, Limbic L, Cont L, and reduced by half the number of high-degree hubs in SomMot R, DorsAttn R, SalVentAttn R. Further, mild sedation almost doubled the number of high-betweenness hubs in Vis L, Vis R, Limbic R, Cont R, and reduced by half the number of high-betweenness hubs in SomMot L, SalVentAttn L, Default L, and SomMot R. Our results indicate that mild anesthesia cannot affect the global integration and segregation of brain networks, but influence meso-scale function for integrating different resting-state systems involved in various segregation processes. Our findings suggest that the meso-scale brain network reorganization, situated between global integration and local segregation, could reflect the autonomic compensation of the brain for drug effects. As a direct response and adjustment of the brain network system to drug administration, this spontaneous reorganization of the brain network aims at maintaining consciousness in the case of sedation.

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IROS Conference 2024 Conference Paper

Similarity Distance-Based Label Assignment for Tiny Object Detection

Shuohao Shi
Qiang Fang
Xin Xu
Tong Zhao

Tiny object detection is becoming one of the most challenging tasks in computer vision because of the limited object size and lack of information. The label assignment strategy is a key factor affecting the accuracy of object detection. Although there are some effective label assignment strategies for tiny objects, most of them focus on reducing the sensitivity to the bounding boxes to increase the number of positive samples and have some fixed hyperparameters need to set. However, more positive samples may not necessarily lead to better detection results, in fact, excessive positive samples may lead to more false positives. In this paper, we introduce a simple but effective strategy named the Similarity Distance (SimD) to evaluate the similarity between bounding boxes. This proposed strategy not only considers both location and shape similarity but also learns hyperparameters adaptively, ensuring that it can adapt to different datasets and various object sizes in a dataset. Our approach can be simply applied in common anchor-based detectors in place of the IoU for label assignment and Non Maximum Suppression (NMS). Extensive experiments on four mainstream tiny object detection datasets demonstrate superior performance of our method, especially, 1. 8 AP points and 4. 1 AP points of very tiny higher than the state-of-the-art competitors on AI-TOD. Code is available at: https://github.com/cszzshi/simd.

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JBHI Journal 2020 Journal Article

An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients

Seedahmed S. Mahmoud
Akshay Kumar
Yiting Tang
Youcun Li
Xudong Gu
Jianming Fu
Qiang Fang

Speech assessment is an important part of the rehabilitation process for patients with aphasia (PWA). Mandarin speech lucidity features such as articulation, fluency, and tone influence the meaning of the spoken utterance and overall speech clarity. Automatic assessment of these features is important for an efficient assessment of the aphasic speech. Hence, in this paper, a standardized automatic speech lucidity assessment method for Mandarin-speaking aphasic patients using a machine learning based technique is presented. The proposed assessment method adopts the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE) standard as a guideline. Quadrature based high-resolution time-frequency images with a convolutional neural network (CNN) are utilized to develop a method that can map the relationship between the severity level of aphasic patients’ speech and the three speech lucidity features. The results show a linear relationship with statistically significant correlations between the normalized true-class output activations (TCOA) of the CNN model and patients’ articulation, fluency, and tone scores, i. e. , 0. 71 ( p p p < 0. 001), respectively. The linearity of the proposed Mandarin aphasic speech assessment method and its significant correlation with the speech severity levels show the efficacy of the method in predicting the severity of impaired Mandarin speech. The outcome of this research envisages assisting speech-language pathologists in Mandarin-speech impairment assessment and promoting early support discharge; hence could alleviate the stress that the healthcare system is currently experiencing in China nationwide. The framework of the proposed Mandarin aphasic speech assessment method can be readily extended to other languages.

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JBHI Journal 2019 Journal Article

A Novel Multistandard Compliant Hand Function Assessment Method Using an Infrared Imaging Device

Qiang Fang
Seedahmed S. Mahmoud
Xudong Gu
Jianming Fu

Many post-stroke patients suffer varying degrees of hand function and fine motor skills impairment. Both passive and active hand rehabilitation training are beneficial in improving the strength and dexterity of the hands. However, hand rehabilitation programs should be prescribed based on an accurate assessment of hand function. In this paper, we propose a novel method for hand function assessment, which can accurately measure multiple joint angles of a hand simultaneously using a portable infrared based imaging device. Different from traditional assessment methods that are often based on a clinician's subjective observations and ordinal charts, this method provides an accurate, fast, and objective evaluation using infrared imaging sensors. Performance evaluation and benchmarking for the proposed measurement system were carried out using the correlation coefficient (CC) method, the root mean squared error, and the percentage residual difference method (PRD). A clinical trial involving 25 participants resulted in a higher correlation with CC of 0. 9672 and PRD of 8. 8%, which indicated that the developed assessment framework is compliant with multiple assessment standards such as Swanson impairment evaluation and Fugl–Meyer assessment. The new hand function assessment method can be used to replace traditional methods for fine hand function modeling and assessment in rehabilitation medicine and can also play an important role in precision post-stroke function analysis.

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JBHI Journal 2016 Journal Article

A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation

Zhe Zhang
Luca Liparulo
Massimo Panella
Xudong Gu
Qiang Fang

Autonomous poststroke rehabilitation systems which can be deployed outside hospital with no or reduced supervision have attracted increasing amount of research attentions due to the high expenditure associated with the current inpatient stroke rehabilitation systems. To realize an autonomous systems, a reliable patient monitoring technique which can automatically record and classify patient's motion during training sessions is essential. In order to minimize the cost and operational complexity, the combination of nonvisual-based inertia sensing devices and pattern recognition algorithms are often considered more suitable in such applications. However, the high motion irregularity due to stroke patients' body function impairment has significantly increased the classification difficulty. A novel fuzzy kernel motion classifier specifically designed for stroke patient's rehabilitation training motion classification is presented in this paper. The proposed classifier utilizes geometrically unconstrained fuzzy membership functions to address the motion class overlapping issue, and thus, it can achieve highly accurate motion classification even with poorly performed motion samples. In order to validate the performance of the classifier, experiments have been conducted using real motion data sampled from stroke patients with a wide range of impairment level and the results have demonstrated that the proposed classifier is superior in terms of error rate compared to other popular algorithms.

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JBHI Journal 2015 Journal Article

Implementation of a Wireless ECG Acquisition SoC for IEEE 802.15.4 (ZigBee) Applications

Liang-Hung Wang
Tsung-Yen Chen
Kuang-Hao Lin
Qiang Fang
Shuenn-Yuh Lee

This paper presents a wireless biosignal acquisition system-on-a-chip (WBSA-SoC) specialized for electrocardiogram (ECG) monitoring. The proposed system consists of three subsystems, namely, 1) the ECG acquisition node, 2) the protocol for standard IEEE 802. 15. 4 ZigBee system, and 3) the RF transmitter circuits. The ZigBee protocol is adopted for wireless communication to achieve high integration, applicability, and portability. A fully integrated CMOS RF front end containing a quadrature voltage-controlled oscillator and a 2. 4-GHz low-IF (i. e. , zero-IF) transmitter is employed to transmit ECG signals through wireless communication. The low-power WBSA-SoC is implemented by the TSMC 0. 18-μm standard CMOS process. An ARM-based displayer with FPGA demodulation and an RF receiver with analog-to-digital mixed-mode circuits are constructed as verification platform to demonstrate the wireless ECG acquisition system. Measurement results on the human body show that the proposed SoC can effectively acquire ECG signals.

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