Biocybernetics and Biomedical Engineering最新文献_第4页

Can cerebral autoregulation be monitored with non-linear correlation indices? A proof-of-concept study 非线性相关指标能否监测大脑自调节？概念验证研究

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.07.005

Karol Sawicki , Arkadiusz Syta , Pawel Dzienis , Lukasz Tymiński , Bartlomiej Ambrożkiewicz , Andrzej Koszewnik , Magdalena Maria Sawicka , Tomasz Łysoń , Zofia Czosnyka , Marek Czosnyka

Objective

Cerebral autoregulation (CA) is the mechanism by which cerebral vessels self-regulate blood flow to maintain adequate brain perfusion. Although CA monitoring is recommended in neurocritical care as a means to improve therapy, there is currently no consensus on the most effective computational technique. The aim of this study was to evaluate the feasibility of measuring CA using recurrence quantification analysis (RQA)—a non-linear method for analysing complex systems—as an alternative to traditional, linear correlation-based indices.

Methods

A retrospective analysis was conducted on a database of head-injured patients treated in a neurocritical care unit who developed spontaneous intracranial pressure (ICP) elevations known as plateau waves. Signals from arterial blood pressure, ICP, and cerebral blood flow velocity were segmented into stable phases of elevated ICP (associated with CA deterioration) and the preceding baseline. Non-linear RQA correlations were calculated for both periods, and the results were statistically compared. For reference, well-established linear CA indices were assessed in the same manner.

Results

The non-linear correlations followed a similar pattern to the linear CA indices and successfully differentiated the plateau wave phase with a comparable, high level of statistical significance (p < 0.0001), while demonstrating higher values and reduced data dispersion.

Conclusion

Non-linear RQA correlation is feasible for CA assessment. To the best of the authors’ knowledge, this is the first evaluation of RQA feasibility in CA research. In the pursuit of a reliable index suitable for widespread implementation in neurocritical care, a non-linear approach may offer a promising alternative to traditional linear correlation-based indices.

目的脑自调节（cerebral autoregulation， CA）是脑血管自我调节血流以维持脑灌注的机制。虽然CA监测被推荐用于神经危重症监护，作为改善治疗的一种手段，但目前对于最有效的计算技术还没有达成共识。本研究的目的是评估使用递归量化分析（RQA）（一种分析复杂系统的非线性方法）作为传统的基于线性相关指标的替代方法来测量CA的可行性。方法回顾性分析在神经危重症监护室治疗的出现自发性颅内压升高（高原波）的颅脑损伤患者的数据库。来自动脉血压、ICP和脑血流速度的信号被划分为ICP升高（与CA恶化相关）的稳定阶段和之前的基线。计算两个时期的非线性RQA相关性，并对结果进行统计学比较。作为参考，已建立的线性CA指数以相同的方式进行评估。结果非线性相关性遵循与线性CA指数相似的模式，并成功区分了高原波相位，具有相当的高水平统计学意义(p <；0.0001)，同时显示出更高的值和减少的数据分散。结论非线性RQA相关性评价CA是可行的。据作者所知，这是CA研究中首次对RQA可行性进行评估。在追求一个可靠的指标，适合在神经危重症护理的广泛实施，非线性的方法可能提供一个有希望的替代传统的线性相关为基础的指标。

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引用次数: 0

Movement and force dynamics in bimanual cooperative tasks in chronic stroke and healthy individuals 慢性脑卒中和健康人双手合作任务的运动和力动力学

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.07.002

J. Podobnik, M. Munih, M. Mihelj

Stroke rehabilitation often involves the use of haptic robots to improve motor control and bimanual coordination. This study examines how damping affects movement and force dynamics in bimanual tasks performed by healthy participants and participants with chronic stroke using a haptic robotic system equipped with force sensors for each hand. Participants completed tasks at three damping levels:

0 \frac{N}{m / s}

(no damping),

20 \frac{N}{m / s}

(low damping), and

40 \frac{N}{m / s}

(moderate damping). Key parameters for trajectory of movement, velocity, manipulation force, and internal force were analyzed to assess movement stability and control. The results revealed that damping

20 \frac{N}{m / s}

effectively stabilized movements in persons with stroke, reducing velocity deviations and making their performance more comparable to healthy participants, without introducing excessive resistance. In contrast, damping

40 \frac{N}{m / s}

acted as resistance training. Participants with stroke exhibited consistently higher internal forces than healthy participants, reflecting compensatory strategies and inefficient motor control. These findings demonstrate that low damping (

20 \frac{N}{m / s}

) offers an optimal balance between movement stabilization and resistance, highlighting its potential as a rehabilitation strategy, while moderate damping (

40 \frac{N}{m / s}

) may be reserved for resistance training.

中风康复通常涉及使用触觉机器人来改善运动控制和双手协调。本研究考察了阻尼如何影响健康参与者和慢性中风参与者在每只手配备力传感器的触觉机器人系统中进行的双手任务的运动和力动力学。参与者在三种阻尼水平下完成任务：0Nm/s（无阻尼）、20Nm/s（低阻尼）和40Nm/s（中等阻尼）。分析了运动轨迹、速度、操纵力和内力等关键参数，以评估运动稳定性和控制性。结果显示，20纳米/秒的阻尼有效地稳定了中风患者的运动，减少了速度偏差，使他们的表现更接近健康参与者，而不会产生过多的阻力。以40Nm/s阻尼作为阻力训练。卒中参与者表现出比健康参与者更高的内力，反映了代偿策略和低效的运动控制。这些研究结果表明，低阻尼（20Nm/s）在运动稳定和阻力之间提供了最佳平衡，突出了其作为康复策略的潜力，而中等阻尼（40Nm/s）可能保留用于阻力训练。

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引用次数: 0

Toward the integration of mixed reality and brain-computer interfaces based on code-modulated visual evoked potentials 基于码调制视觉诱发电位的混合现实与脑机接口集成研究

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.06.003

Selene Moreno-Calderón , Víctor Martínez-Cagigal , Ana Martín-Fernández , Eduardo Santamaría-Vázquez , Roberto Hornero

Background and objective: Brain-computer interface (BCI) systems can assist individuals with severe motor disabilities by enabling communication through their brain signals using spellers, which allow selecting commands from a set of options. For this technology, accuracy, speed and user comfort are essential. Code-modulated visual evoked potentials (c-VEPs) have demonstrated promising performance in BCI control. Integrating BCI systems with mixed reality (MR) could provide portability and autonomy. However, to the best of our knowledge, no existing studies have explored the feasibility of combining MR with c-VEP-based BCIs. This study aims to: (1) evaluate the performance of integrating MR with c-VEP-based BCIs and (2) study the visual fatigue induced by c-VEPs compared to traditional screen. Methods: Twenty participants used a 36-character speller to select words in both MR and traditional screen conditions. Metrics like accuracy and information transfer rate (ITR) were measured. Usability and eyestrain were evaluated through questionnaires. Results: The integration of MR with c-VEPs achieved an accuracy of 96.71 % and an ITR of 27.55 bits/min, compared to 95.98 % accuracy and 27.10 bits/min for the conventional screen condition. The questionnaires revealed minimal levels of visual fatigue in both conditions and high usability. No significant differences were observed between conditions in terms of performance or visual fatigue. Conclusions: The c-VEP-based speller with MR-BCI technology proved feasible, achieving performance levels similar to the conventional setup, with high accuracy in both conditions. The study also found comparable visual fatigue between MR and traditional screens, supporting the practicality of MR integration in BCI systems.

背景和目的：脑机接口（BCI）系统可以帮助患有严重运动障碍的人，通过使用拼写器使他们的大脑信号能够交流，从而允许从一组选项中选择命令。对于这项技术，准确性、速度和用户舒适度是必不可少的。编码调制视觉诱发电位（c-VEPs）在脑机接口控制中表现出良好的性能。将BCI系统与混合现实（MR）集成可以提供可移植性和自主性。然而，据我们所知，目前还没有研究探讨MR与基于c- vep的脑机接口联合的可行性。本研究旨在：(1)评价MR与基于c- vep的脑机接口集成的性能；(2)研究与传统屏幕相比，c- vep引起的视觉疲劳。方法：20名参与者使用36个字符的拼写器在MR和传统屏幕条件下选择单词。测量了准确性和信息传输率（ITR）等指标。通过问卷对可用性和眼疲劳进行评价。结果：MR与c-VEPs的整合准确率为96.71%，ITR为27.55 bits/min，而常规筛选条件下准确率为95.98%，ITR为27.10 bits/min。调查问卷显示，在两种情况下，视觉疲劳的程度都很低，可用性也很高。在表现或视觉疲劳方面，没有观察到明显的差异。结论：基于c- vep的拼写器与MR-BCI技术被证明是可行的，在两种情况下都具有较高的准确率，达到了与传统设置相似的性能水平。该研究还发现MR和传统屏幕的视觉疲劳程度相当，这支持了MR集成在脑机接口系统中的实用性。

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引用次数: 0

A potential approach to detecting of gastrointestinal slow waves based on optically pumped magnetometers array 一种基于光泵磁强计阵列的胃肠道慢波检测方法

IF 6.6 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.08.001

Yixiang Zhang , Xinlei He , Haoyue Li , Linke Zhang , Bing Yan , Yuming Peng , Qinggang Ge , Ming Ding , Yuxin Leng

Gastrointestinal slow wave (SW) can be used to evaluate human gastrointestinal function. Compared with electrogastrogram and electroenterogram, SW magnetic signals measurement can obtain higher-quality results. Optically pumped magnetometers (OPMs) based on the spin exchange relaxation-free method have comparable weak magnetic detection capabilities to superconducting quantum interference devices, and does not require liquid helium. Therefore, this study developed a gastrointestinal SW signals measurement system using OPM sensors. Our system measured rat intestinal SW signals with a frequency of approximately 34.24 cpm, and simultaneously extracted human gastric SW signals of approximately 3 cpm and intestinal SW signals of 8–12 cpm for the first time. It was also observed that the postprandial gastrointestinal SW signals were significantly enhanced compared with the fasting state. The results exhibited consistent power and time–frequency characteristics with previous reports. In summary, the gastrointestinal SW signals measurement system based on OPM provides a new technical approach for detecting gastrointestinal SWs and diagnosing related diseases.

胃肠慢波（SW）可用于评价人体胃肠功能。与胃电和肠电相比，SW磁信号测量可以获得更高质量的结果。基于自旋交换无弛豫方法的光泵磁强计（OPMs）具有与超导量子干涉装置相当的弱磁探测能力，并且不需要液氦。因此，本研究开发了一种利用OPM传感器的胃肠道SW信号测量系统。我们的系统测量了大鼠肠道SW信号，频率约为34.24 cpm，并首次同时提取了人类胃SW信号，频率约为3 cpm，肠道SW信号为8-12 cpm。与禁食状态相比，餐后胃肠道SW信号明显增强。结果显示出与先前报道一致的功率和时频特性。综上所述，基于OPM的胃肠道SW信号测量系统为检测胃肠道SW和诊断相关疾病提供了新的技术途径。

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引用次数: 0

Artificial intelligence models for wound infection recognition and their comparison with human results 伤口感染识别的人工智能模型及其与人类结果的比较

IF 6.6 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.08.003

Piotr Foltynski , Karolina Kruszewska , Arkadiusz Krakowiecki , Bozena Czarkowska-Paczek , Piotr Ladyzynski

Recognizing an infected wound based solely on a photograph can be a challenge and the aim of this work was to develop a machine learning model that would enable that. We selected 899 wound photographs taken at PODOS Wound Care Clinic (Warsaw, Poland). There were 445 photographs showing uninfected wounds, whereas 454 photographs showed infected wounds with positive microbiological test and antibiotic treatment. A test set was created by randomly selecting 82 photographs representing 42 uninfected and 40 infected wounds. From the remaining photographs, 154 were randomly selected for the validation set, and the remaining 663 formed the training set. Initially we used five pretrained YOLO models from generation 8 and five from generation 11. The 8th generation models performed better than 11th generation models and were then compared with the results of 6 experts and 6 nursing students. The post-hoc analysis revealed that AI models outperformed both specialists and students in terms of mean averaged precision (mAP), accuracy and F1 score, while the results of specialists and students did not differ significantly. For specialists, the medians of mAP, F1 score, and accuracy were 74.1 %, 76.4 %, and 74.4 %, respectively. For Students the medians were 68.4 %, 59.4 %, and 67.7 %, respectively; and for AI models the medians were 92.7 %, 92.9 %, and 92.7 %, respectively. The highest accuracy of 95.1 % of YOLOv8n model was significantly higher than the best specialist’s result of 84.1 %. These results suggest that artificial intelligence can significantly help caregivers recognize wound infection, so they can take appropriate action more quickly.

仅仅根据照片来识别感染的伤口可能是一个挑战，这项工作的目的是开发一种机器学习模型来实现这一目标。我们选择了899张在PODOS伤口护理诊所（波兰华沙）拍摄的伤口照片。有445张照片显示未感染的伤口，而454张照片显示微生物检测呈阳性并接受抗生素治疗的感染伤口。通过随机选择代表42个未感染伤口和40个感染伤口的82张照片，创建了一个测试集。从剩下的照片中随机抽取154张作为验证集，剩下的663张构成训练集。最初，我们使用了来自第8代和第11代的5个预训练YOLO模型。第8代模型优于第11代模型，并与6名专家和6名护生的结果进行比较。事后分析显示，人工智能模型在平均平均精度（mAP）、准确性和F1分数方面都优于专家和学生，而专家和学生的结果没有显著差异。专科医师的mAP、F1评分和准确率中位数分别为74.1%、76.4%和74.4%。学生的中位数分别为68.4%、59.4%和67.7%；人工智能模型的中位数分别为92.7%、92.9%和92.7%。YOLOv8n模型的最高准确率为95.1%，显著高于最佳专家的84.1%。这些结果表明，人工智能可以显著帮助护理人员识别伤口感染，因此他们可以更快地采取适当的行动。

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引用次数: 0

Relevance of harmonic content findings of hand motor (dys)functionalities in Parkinson’s disease revealed by means of a sensory glove 通过感觉手套揭示帕金森病手运动（日）功能的谐波含量相关性

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.07.004

Luca Pietrosanti , Martina Patera , Antonio Suppa , Giovanni Costantini , Nicola Arangino , Franco Giannini , Giovanni Saggio

Hand functions are vital for performing daily activities, ensuring independence, and maintaining quality of life. In Parkinson’s disease (PD), impaired hand function affects fine motor skills, dexterity, and coordination, leading to difficulties in self-care, communication, and work-related tasks. As such, correct hand function assessment in PD is among the crucial aspects in evaluating motor impairment, in guiding treatment and tracking disease progression. Here, we report objective results obtained in assessing hand (dys)functionalities using an on-the-shelves fingerless sensory glove, named MANUS Quantum Metaglove, capable of sensing the variations of an electromagnetic field (EMF) sourced on the dorsal part of the hand and revealed by EMF coils at the fingers tips. A total of 65 people (35 PD patients and 30 healthy subjects for reference) were asked to perform standard motor tasks, and both most affected and least affected hands were assessed for opening-closing, grasping and pronation-supination movements. Differing from the generally adopted spatiotemporal analysis, taking a cue from non-linear theory adopted in electronics, we focused on spectral characteristics of the measured signals, specifically examining harmonic content and related harmonic distortions. As a result, we report how the adopted sensory glove, ensemble with spectral analysis, can be able to consistently assess hand motor (in)abilities in PD subjects and healthy subjects. In fact according to our results, PD patients significatively performed with hand motion signals affected by harmonic distortions, which revealed that the greater the complexity of the motor task, the greater the spread of the signal across harmonic frequencies, whilst healthy subjects perform with signals mostly around the fundamental frequency, as a marker of movement smoothness.

手部功能对于进行日常活动、确保独立性和维持生活质量至关重要。在帕金森氏症（PD）中，手部功能受损会影响精细运动技能、灵活性和协调性，导致自我照顾、沟通和工作相关任务的困难。因此，PD患者正确的手功能评估是评估运动障碍、指导治疗和跟踪疾病进展的关键方面之一。在这里，我们报告了使用架子上的无指传感手套（名为MANUS Quantum Metaglove）评估手部（天）功能所获得的客观结果，该手套能够感知源自手背的电磁场（EMF）的变化，并通过指尖的EMF线圈显示。共65人（35名PD患者和30名健康受试者作为参考）被要求执行标准的运动任务，并评估最受影响和最不受影响的手的开合、抓握和旋前运动。与通常采用的时空分析不同，我们借鉴了电子学中采用的非线性理论，重点研究了测量信号的频谱特征，特别是谐波含量和相关的谐波畸变。因此，我们报告了所采用的感觉手套如何与频谱分析相结合，能够一致地评估PD受试者和健康受试者的手部运动能力。事实上，根据我们的研究结果，PD患者的手部运动信号明显受到谐波失真的影响，这表明运动任务越复杂，信号在谐波频率上的传播越大，而健康受试者的信号大多在基频附近，作为运动平滑度的标志。

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引用次数: 0

Dysphonia discovering using a Goertzel algorithm implementation for vocal signals analysis 语音障碍发现使用Goertzel算法实现的声音信号分析

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.07.001

Patrizia Vizza , Giuseppe Tradigo , Pietro Hiram Guzzi , Pierangelo Veltri

Background and objectives: The identification, study and classification of anomalies in vocal signals are used to support physicians in the diagnosis and monitoring of vocal robe pathologies. Dysphonia is the most common disorder causing difficulties in voice production. Dysphonia refers to any impairment in voice quality, and significantly impacts on the quality of life. Early detection is imperative to prevent severe pathologies or to early detect chronic ones. Voice signal processing techniques, such as Fast Fourier Transform (FFT) and Praat, are noninvasive tools used to study phonatory apparatus diseases. Nevertheless there is room for improving efficacy in vocal signal patterns identification that could be related to vocal robe related pathologies.

Methods: The focus is on the possibility of using Goertzel Algorithm (GA) characteristics to improve state of the art for pattern identification in vocal signals. A tool for early identification of dysphonia based on GA is presented. An optimized version of GA, able to detect voice frequency anomalies has been implemented.

Results: The proposed tool has been tested with vocal signal datasets containing both normophonic and pathological subjects. The results are reported in terms of different implementation strategies and techniques. Experimental tests were performed comparing GA based and FFT based signal analysis tools in terms of: (i) efficiency and (ii) capacity of features identification. Performance parameters report: (i) an efficiency in terms of processing time improved by 37 % (i.e. 16.78 ms for FFT vs 12.26 ms for GA) and memory requirements reduced by 74 %; (ii) GA enabled the identification of healthy and pathological conditions better than FFT with a significance level below 0.05.

Conclusions: Results of using GA-based method on vocal signal processing, compared with existing methods, demonstrate the reliability of the proposed method in early identification of dysphonia and in clinical monitoring of patients post treatment.

背景与目的：声音信号异常的识别、研究和分类用于支持医生对声带病理的诊断和监测。发音困难是最常见的导致发声困难的障碍。语音障碍是指语音质量的任何损害，并显著影响生活质量。早期发现对于预防严重病变或早期发现慢性病变至关重要。语音信号处理技术，如快速傅里叶变换（FFT）和Praat，是用于研究发声器官疾病的非侵入性工具。尽管如此，在声音信号模式识别方面仍有改进的空间，这可能与声带相关的病理有关。方法：重点是使用Goertzel算法（GA）特征来改进语音信号模式识别的技术状态的可能性。提出了一种基于遗传算法的语音障碍早期识别工具。一个优化版本的遗传算法，能够检测语音频率异常已经实现。结果：提出的工具已经测试了声音信号数据集，包括正常音和病理受试者。根据不同的实现策略和技术报告了结果。实验测试比较了基于遗传算法和基于FFT的信号分析工具在以下方面：(i)效率和（ii）特征识别能力。性能参数报告：(i)处理时间方面的效率提高了37%（即FFT为16.78 ms， GA为12.26 ms），内存需求降低了74%；（ii）与FFT相比，GA能更好地识别健康和病理状况，且显著性水平低于0.05。结论：与现有方法相比，基于ga的语音信号处理方法在语音障碍早期识别和治疗后患者临床监测方面具有较高的可靠性。

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引用次数: 0

Evaluating the generalization of machine learning models for predicting 14-day mortality in traumatic brain injury patients 评估预测外伤性脑损伤患者14天死亡率的机器学习模型的泛化

IF 6.6 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.08.002

Fabio Arthur Soares Araújo , Robson Luis Oliveira de Amorim , Marly Guimarães Fernandes Costa , Henrique Oliveira Martins , Cicero Ferreira Fernandes Costa Filho

Traumatic Brain Injury (TBI) remains a leading cause of morbidity and mortality worldwide, with significant disparities in outcomes influenced by regional healthcare access and infrastructure. This study evaluates the performance and generalizability of machine learning models for predicting 14-day mortality in TBI patients using datasets from two distinct Brazilian regions: São Paulo, an urban center, and Manaus, an isolated urban center with unique logistical challenges. To our knowledge, this research represents the first cross-validation of predictive models across two datasets within the same country, underscoring the critical need for localized approaches in TBI research. Our findings indicate that while convolutional neural network (CNN)-based models achieved high performance, with an area under the curve (AUC) of 0.90 in São Paulo and 0.93 in Manaus, the best model from São Paulo exhibited a strikingly low AUC when applied to the Manaus dataset. The incorporation of context-specific features, such as pandemic-related variables and time from trauma to admission, significantly enhanced model accuracy, with the Manaus model reaching an impressive AUC of 0.98. Notably, the study highlights key regional differences in predictors of mortality, with hypoxia and hypotension being more critical in Manaus, emphasizing the importance of tailoring predictive models to local contexts. These regionally important variables identified in the ML prediction model may inform quality-improvement priorities and further research in these settings. Our results indicate that CNN-based models have the potential to enhance mortality predictions for patients with traumatic brain injury (TBI).

创伤性脑损伤（TBI）仍然是世界范围内发病率和死亡率的主要原因，其结果受区域医疗保健可及性和基础设施的影响存在显著差异。本研究使用来自巴西两个不同地区的数据集，评估了机器学习模型预测TBI患者14天死亡率的性能和通用性：城市中心圣保罗和具有独特物流挑战的孤立城市中心玛瑙斯。据我们所知，这项研究代表了同一国家内两个数据集预测模型的首次交叉验证，强调了在TBI研究中本地化方法的迫切需要。我们的研究结果表明，虽然基于卷积神经网络（CNN）的模型取得了很高的性能，在圣保罗和马瑙斯的曲线下面积（AUC）分别为0.90和0.93，但当应用于马瑙斯数据集时，来自圣保罗的最佳模型显示出非常低的AUC。纳入特定情境的特征，如流行病相关变量和从创伤到入院的时间，显著提高了模型的准确性，Manaus模型的AUC达到了令人印象深刻的0.98。值得注意的是，该研究强调了死亡率预测因素的关键区域差异，在马瑙斯，缺氧和低血压更为重要，强调了根据当地情况量身定制预测模型的重要性。在机器学习预测模型中确定的这些区域重要变量可以为这些设置中的质量改进优先级和进一步研究提供信息。我们的研究结果表明，基于cnn的模型有可能提高对创伤性脑损伤（TBI）患者的死亡率预测。

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引用次数: 0

Deep spatio-temporal features optimised fusion with coordinate attention mechanism for EEG lower limb pre-movement intention decoding 深度时空特征优化融合与协调注意机制在脑电下肢运动前意向解码中的应用

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.06.004

Runlin Dong , Xiaodong Zhang , Zhengzheng Zhou , Wenyu Zha , Aibin Zhu

Background and Objective

Decoding pre-movement intention is crucial in developing a brain-computer interface (BCI) for neuro-rehabilitation robotic systems. However, the weak amplitude and non-smooth characteristics of EEG signals lead to the inability of existing methods to achieve the accuracy for proper applications. This study proposed a novel pre-movement intention decoding network framework to improve accuracy by extracting and optimizing the deep spatio-temporal features of EEG signals.

Methods

A deep spatio-temporal neural network structure was constructed based on the brain intention generation mechanism and its movement expression. The collected multi-channel EEG data were reorganized into brain topographic distributions, after the initial extraction of the features and optimization using the coordinate attention mechanism, a 3-layer dense block with two bi-directional gated recirculation units was designed to effectively extract the deep spatial and temporal features, further decoding the pre-movement intention efficiently.

Results

The experimental results showed an average accuracy of 95.51 ± 1.79 % for healthy subjects and 90.48 ± 2.90 % for stroke survivors in decoding pre-movement intention. All evaluation indexes are excellent. Pseudo-online testing showed the average TPR was 95.45 ± 3.80 % and 90.71 ± 7.77 % for healthy subjects and stroke survivors, respectively, and the latency was −1965 ± 48 ms and −1974 ± 36 ms. The results of the ablation and comparative analysis showed that the proposed framework is justified and its decoding capability outperforms other state-of-the-art algorithms.

Conclusion

The method proposed in this study has high decoding accuracy and good online performance in pre-movement intention decoding based on EEG signals, which lays the foundation for further neuro-rehabilitation robotic systems.

背景与目的运动前意图的解码是开发神经康复机器人系统脑机接口的关键。然而，由于脑电信号的微弱幅度和非平滑特性，现有的方法无法达到正确应用的精度。本研究提出了一种新的运动前意图解码网络框架，通过提取和优化脑电信号的深层时空特征来提高解码准确率。方法基于大脑意图产生机制及其运动表达，构建深度时空神经网络结构。将采集到的多通道脑电数据重组为脑地形分布，经过特征的初始提取和坐标注意机制的优化，设计了具有两个双向门控循环单元的3层密集块，有效提取深层时空特征，进一步高效解码动作前意图。结果健康受试者和脑卒中幸存者对动作前意向的平均解码准确率分别为95.51±1.79%和90.48±2.90%。各项评价指标均为优秀。伪在线测试显示，健康受试者和脑卒中幸存者的平均TPR分别为95.45±3.80%和90.71±7.77%，潜伏期分别为- 1965±48 ms和- 1974±36 ms。实验结果和对比分析表明，所提出的框架是合理的，其解码能力优于其他最先进的算法。结论该方法在基于脑电信号的运动前意图解码中具有较高的解码精度和良好的在线性能，为进一步的神经康复机器人系统奠定了基础。

{"title":"Deep spatio-temporal features optimised fusion with coordinate attention mechanism for EEG lower limb pre-movement intention decoding","authors":"Runlin Dong , Xiaodong Zhang , Zhengzheng Zhou , Wenyu Zha , Aibin Zhu","doi":"10.1016/j.bbe.2025.06.004","DOIUrl":"10.1016/j.bbe.2025.06.004","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Decoding pre-movement intention is crucial in developing a brain-computer interface (BCI) for neuro-rehabilitation robotic systems. However, the weak amplitude and non-smooth characteristics of EEG signals lead to the inability of existing methods to achieve the accuracy for proper applications. This study proposed a novel pre-movement intention decoding network framework to improve accuracy by extracting and optimizing the deep spatio-temporal features of EEG signals.</div></div><div><h3>Methods</h3><div>A deep spatio-temporal neural network structure was constructed based on the brain intention generation mechanism and its movement expression. The collected multi-channel EEG data were reorganized into brain topographic distributions, after the initial extraction of the features and optimization using the coordinate attention mechanism, a 3-layer dense block with two bi-directional gated recirculation units was designed to effectively extract the deep spatial and temporal features, further decoding the pre-movement intention efficiently.</div></div><div><h3>Results</h3><div>The experimental results showed an average accuracy of 95.51 ± 1.79 % for healthy subjects and 90.48 ± 2.90 % for stroke survivors in decoding pre-movement intention. All evaluation indexes are excellent. Pseudo-online testing showed the average TPR was 95.45 ± 3.80 % and 90.71 ± 7.77 % for healthy subjects and stroke survivors, respectively, and the latency was −1965 ± 48 ms and −1974 ± 36 ms. The results of the ablation and comparative analysis showed that the proposed framework is justified and its decoding capability outperforms other state-of-the-art algorithms.</div></div><div><h3>Conclusion</h3><div>The method proposed in this study has high decoding accuracy and good online performance in pre-movement intention decoding based on EEG signals, which lays the foundation for further neuro-rehabilitation robotic systems.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 3","pages":"Pages 515-527"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel device for proprioceptive acuity measurement: Validity and reliability analysis in young and older adults 一种新的本体感觉敏锐度测量装置：对年轻人和老年人的效度和信度分析

IF 6.6 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-07-01 DOI: 10.1016/j.bbe.2025.08.005

Sayedmohsen Mortazavi Najafabadi , Dariusz Grzelczyk , Mohammed N. Ashtiani

Age, neurodegenerative diseases, diabetes, and sports injuries can all impair proprioception, i.e. a crucial sensory feedback system for balance control and gait. The purpose of this study was to assess the validity and reliability of a recently constructed device for measuring proprioceptive function. Forty-seven participants, comprising 26 younger healthy adults (20–40 years) and 21 older adults (> 65 years), were evaluated. The ankle’s sense of motion (SoM) sensitivity and sense of position (SoP, active/passive) acuity were measured by the device. The Intraclass Correlation Coefficient (ICC) and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) were used as indicators of reliability and validity. The results showed excellent reliability for SoM sensitivity in dorsiflexion (ICC = 0.985 for younger, 0.98 for older) and plantarflexion (ICC = 0.972 for younger, 0.982 for older). High reliability was also observed in passive SoP acuity (ICC = 0.825 – 0.989). However, the reliability of the active SoP acuity method was poor to moderate. Strong discriminative validity was demonstrated by the AUC-ROC values, with SoM sensitivity distinguishing between younger and older participants with an accuracy of over 91 %. Bland-Altman analysis revealed tighter agreement for SoM sensitivity (18 to 40 % of the device precision) than passive SoP acuity (70 to 90 % of the device precision), as well as minimal systematic bias (−0.03 to −0.01 degrees) to show interday test–retest reliability. According to these results, the device is valid for evaluating proprioceptive function, particularly SoM sensitivity, and it may be useful in clinical and research settings.

年龄、神经退行性疾病、糖尿病和运动损伤都会损害本体感觉，即平衡控制和步态的关键感觉反馈系统。本研究的目的是评估最近建造的测量本体感觉功能的装置的有效性和可靠性。对47名参与者进行了评估，其中包括26名年轻健康成年人（20-40岁）和21名老年人（65岁）。测量踝关节运动感（SoM）灵敏度和位置感（SoP，主动/被动）敏锐度。采用类内相关系数（Intraclass Correlation Coefficient， ICC）和受试者工作特征曲线下面积（Area Under Receiver Operating Characteristic Curve, AUC-ROC）作为信度和效度指标。结果显示，SoM对背屈（年轻人ICC = 0.985，老年人ICC = 0.98）和跖屈（年轻人ICC = 0.972，老年人ICC = 0.982）的敏感性具有良好的可靠性。在被动SoP敏锐度上也观察到较高的信度（ICC = 0.825 ~ 0.989）。然而，活性SoP敏锐度法的可靠性较差至中等。AUC-ROC值证明了强的判别效度，SoM敏感性区分年轻和老年参与者，准确率超过91%。Bland-Altman分析显示，SoM灵敏度（18%至40%的设备精度）比被动SoP灵敏度（70%至90%的设备精度）更为一致，并且最小的系统偏差（- 0.03至- 0.01度）显示了日间测试-重测可靠性。根据这些结果，该装置可以有效地评估本体感觉功能，特别是SoM敏感性，并且可能在临床和研究环境中有用。

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引用次数: 0