Pub Date : 2024-10-31DOI: 10.1109/TBME.2024.3489799
Arash Golmohammadi;Jan Philipp Payonk;Ursula van Rienen;Revathi Appali
Deep brain stimulation (DBS) is an established treatment for neurodegenerative movement disorders such as Parkinson's disease that mitigates symptoms by overwriting pathological signals from the central nervous system to the motor system. Nearly all computational models of DBS, directly or indirectly, associate clinical improvements with the extent of fiber activation in the vicinity of the stimulating electrode. However, it is not clear how such activation modulates information transmission. Here, we use the exact cable equation for straight or curved axons and show that DBS segregates the signaling pathways into one of the three communicational modes: complete information blockage, uni-, and bi-directional transmission. Furthermore, all these modes respond to the stimulating pulse in an asynchronous but frequency-locked fashion. Asynchrony depends on the geometry of the axon, its placement and orientation, and the stimulation protocol. At the same time, the electrophysiology of the nerve determines frequency-locking. Such a trimodal response challenges the notion of activation as a binary state and studies that correlate it with the DBS outcome. Importantly, our work suggests that a mechanistic understanding of DBS action relies on distinguishing between these three modes of information transmission.
{"title":"A Computational Study on the Activation of Neural Transmission in Deep Brain Stimulation","authors":"Arash Golmohammadi;Jan Philipp Payonk;Ursula van Rienen;Revathi Appali","doi":"10.1109/TBME.2024.3489799","DOIUrl":"10.1109/TBME.2024.3489799","url":null,"abstract":"Deep brain stimulation (DBS) is an established treatment for neurodegenerative movement disorders such as Parkinson's disease that mitigates symptoms by overwriting pathological signals from the central nervous system to the motor system. Nearly all computational models of DBS, directly or indirectly, associate clinical improvements with the extent of fiber activation in the vicinity of the stimulating electrode. However, it is not clear how such activation modulates information transmission. Here, we use the exact cable equation for straight or curved axons and show that DBS segregates the signaling pathways into one of the three communicational modes: complete information blockage, uni-, and bi-directional transmission. Furthermore, all these modes respond to the stimulating pulse in an asynchronous but frequency-locked fashion. Asynchrony depends on the geometry of the axon, its placement and orientation, and the stimulation protocol. At the same time, the electrophysiology of the nerve determines frequency-locking. Such a trimodal response challenges the notion of activation as a binary state and studies that correlate it with the DBS outcome. Importantly, our work suggests that a mechanistic understanding of DBS action relies on distinguishing between these three modes of information transmission.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1132-1147"},"PeriodicalIF":4.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Suspended loads have been shown to improve loaded-walking economy. Establishing a biped walking model with dynamic trunk pitch angles can provide more comprehensive estimates of the human biomechanical response under suspended loads. Methods: We developed the trunk-load- hip dynamics, modified the spring-loaded-inverted-pendulum (SLIP) model, and optimized the loaded-walking pattern for minimal energetic cost. 9 subjects participated in experiments using a powered backpack to validate the model's performance, conducting two trials: Load-Suspended (LS) and Load-Locked (LL). Results: The averaged correlation coefficient of simulated and experimental hip trajectory, vertical and horizontal GRFs, and individual leg mechanical (ILM) powers are 0.96, 0.97, 0.93, and 0.81, respectively. The RMS error between simulated and experimental peaks of vertical GRFs, braking peaks of horizontal GRFs, and energetic costs was under 10%. Simulation also provides observation on the effect of suspended load on dynamic trunk pitch angles and torques, and leg stiffness. Both the simulation and experiment demonstrated the advantages of LS in reducing GRFs and energetic cost. Additionally, the simulation shows the peaks of trunk flexion and extension torque are reduced by 34.77% (p < 0.05) and 37.88% (p < 0.05) in LS. Conclusion: The model effectively estimates hip trajectory, vertical and horizontal GRFs, ILM powers, and energetic cost, and provides observations on trunk behavior under different load conditions. The model also supports the advantages of suspension load. Significance: Appropriate models could comprehensively reveal the mechanism between the mechanical systems and human biomechanics responses, guide the design of carrying load devices, and provide rapid evaluation of its effects.
{"title":"A Bipedal Walking Model Considering Trunk Pitch Angle for Estimating the Influence of Suspension Load on Human Biomechanics","authors":"Qinhao Zhang;Wenbin Chen;Hanwen Zhang;Siyuan Lin;Caihua Xiong","doi":"10.1109/TBME.2024.3487536","DOIUrl":"10.1109/TBME.2024.3487536","url":null,"abstract":"<italic>Objective:</i> Suspended loads have been shown to improve loaded-walking economy. Establishing a biped walking model with dynamic trunk pitch angles can provide more comprehensive estimates of the human biomechanical response under suspended loads. <italic>Methods:</i> We developed the trunk-load- hip dynamics, modified the spring-loaded-inverted-pendulum (SLIP) model, and optimized the loaded-walking pattern for minimal energetic cost. 9 subjects participated in experiments using a powered backpack to validate the model's performance, conducting two trials: Load-Suspended (LS) and Load-Locked (LL). <italic>Results:</i> The averaged correlation coefficient of simulated and experimental hip trajectory, vertical and horizontal GRFs, and individual leg mechanical (ILM) powers are 0.96, 0.97, 0.93, and 0.81, respectively. The RMS error between simulated and experimental peaks of vertical GRFs, braking peaks of horizontal GRFs, and energetic costs was under 10%. Simulation also provides observation on the effect of suspended load on dynamic trunk pitch angles and torques, and leg stiffness. Both the simulation and experiment demonstrated the advantages of LS in reducing GRFs and energetic cost. Additionally, the simulation shows the peaks of trunk flexion and extension torque are reduced by 34.77% (p < 0.05) and 37.88% (p < 0.05) in LS. <italic>Conclusion:</i> The model effectively estimates hip trajectory, vertical and horizontal GRFs, ILM powers, and energetic cost, and provides observations on trunk behavior under different load conditions. The model also supports the advantages of suspension load. <italic>Significance:</i> Appropriate models could comprehensively reveal the mechanism between the mechanical systems and human biomechanics responses, guide the design of carrying load devices, and provide rapid evaluation of its effects.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1097-1107"},"PeriodicalIF":4.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545187","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}
Pub Date : 2024-10-29DOI: 10.1109/TBME.2024.3488014
Yue Yuan;Zhouyan Feng;Zhaoxiang Wang
Objective: High-frequency stimulation (HFS) of electrical pulse sequences has been used in various neuromodulation techniques to treat certain disorders. Here, we test the hypothesis that HFS sequences with purely periodic pulses could directly generate non-uniform firing in directly stimulated neurons. Methods: In vivo experiments were conducted in the rat hippocampal CA1 region. A stimulation electrode was placed on the alveus fibers, and a recording electrode array was inserted into the CA1 region upstream of the stimulation site. Antidromic-HFS (A-HFS) of 100 Hz pulses was applied to the alveus to antidromically activate the soma of pyramidal neurons around the recording site. By minimizing the interferences of population spikes, the evoked unit spikes of individual pyramidal neurons were obtained during A-HFS. Additionally, a computational model of pyramidal neuron was used to simulate the neuronal responses to A-HFS, revealing possible mechanisms underlying the different firing patterns. Results: Of the total 54 pyramidal neurons recorded during 2-min 100 Hz A-HFS, 38 (70%) neurons fired in a cluster pattern with alternating periods of intensive spikes and silence. The remaining 16 (30%) neurons fired in a non-cluster pattern with regular spikes. Modeling simulations showed that under the situation of HFS-induced intermittent block, conduction failure and generation failure of action potentials along the axons resulted in the cluster and non-cluster firing. Conclusion: Sustained axonal A-HFS with periodic pulses can induce non-uniform firing in directly stimulated neurons. Significance: This finding provides new evidence for the nonlinear dynamics of neuronal firing, even under uniform stimulation.
{"title":"Cluster Neuronal Firing Induced by Uniform Pulses of High-Frequency Stimulation on Axons in Rat Hippocampus","authors":"Yue Yuan;Zhouyan Feng;Zhaoxiang Wang","doi":"10.1109/TBME.2024.3488014","DOIUrl":"10.1109/TBME.2024.3488014","url":null,"abstract":"<italic>Objective:</i> High-frequency stimulation (HFS) of electrical pulse sequences has been used in various neuromodulation techniques to treat certain disorders. Here, we test the hypothesis that HFS sequences with purely periodic pulses could directly generate non-uniform firing in directly stimulated neurons. <italic>Methods:</i> In vivo experiments were conducted in the rat hippocampal CA1 region. A stimulation electrode was placed on the alveus fibers, and a recording electrode array was inserted into the CA1 region upstream of the stimulation site. Antidromic-HFS (A-HFS) of 100 Hz pulses was applied to the alveus to antidromically activate the soma of pyramidal neurons around the recording site. By minimizing the interferences of population spikes, the evoked unit spikes of individual pyramidal neurons were obtained during A-HFS. Additionally, a computational model of pyramidal neuron was used to simulate the neuronal responses to A-HFS, revealing possible mechanisms underlying the different firing patterns. <italic>Results:</i> Of the total 54 pyramidal neurons recorded during 2-min 100 Hz A-HFS, 38 (70%) neurons fired in a cluster pattern with alternating periods of intensive spikes and silence. The remaining 16 (30%) neurons fired in a non-cluster pattern with regular spikes. Modeling simulations showed that under the situation of HFS-induced intermittent block, conduction failure and generation failure of action potentials along the axons resulted in the cluster and non-cluster firing. <italic>Conclusion:</i> Sustained axonal A-HFS with periodic pulses can induce non-uniform firing in directly stimulated neurons. <italic>Significance:</i> This finding provides new evidence for the nonlinear dynamics of neuronal firing, even under uniform stimulation.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1108-1120"},"PeriodicalIF":4.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: The Motor Imagery (MI) paradigm has been widely used in brain-computer interface (BCI) for device control and motor rehabilitation. However, the MI paradigm faces challenges such as comprehension difficulty and limited decoding accuracy. Therefore, we propose the Action Observation with Rhythm Imagery (AORI) as a natural paradigm to provide distinct features for high-performance decoding. Methods: Twenty subjects were recruited in the current study to perform the AORI task. Spectral-spatial, temporal and time-frequency analyses were conducted to investigate the AORI-activated brain pattern. Task-discriminant component analysis (TDCA) was utilized to perform multiclass motor decoding. Results: The results demonstrated distinct lateralized ERD in the alpha and beta bands, and clear lateralized steady-state movement-related rhythm (SSMRR) at the movement frequencies and their first harmonics. The activated brain areas included frontal, sensorimotor, posterior parietal, and occipital regions. Notably, the decoding accuracy reached 92.16% ± 7.61% in the four-class scenario. Conclusion and Significance: We proposed the AORI paradigm, revealed the activated motor-related pattern and proved its efficacy for high-performance motor decoding. These findings provide new possibilities for designing a natural and robust BCI for motor control and motor rehabilitation.
{"title":"Action Observation With Rhythm Imagery (AORI): A Novel Paradigm to Activate Motor-Related Pattern for High-Performance Motor Decoding","authors":"Yuxuan Wei;Jianjun Meng;Ruijie Luo;Ximing Mai;Songwei Li;Yuchen Xia;Xiangyang Zhu","doi":"10.1109/TBME.2024.3487133","DOIUrl":"10.1109/TBME.2024.3487133","url":null,"abstract":"<italic>Objective:</i> The Motor Imagery (MI) paradigm has been widely used in brain-computer interface (BCI) for device control and motor rehabilitation. However, the MI paradigm faces challenges such as comprehension difficulty and limited decoding accuracy. Therefore, we propose the Action Observation with Rhythm Imagery (AORI) as a natural paradigm to provide distinct features for high-performance decoding. <italic>Methods:</i> Twenty subjects were recruited in the current study to perform the AORI task. Spectral-spatial, temporal and time-frequency analyses were conducted to investigate the AORI-activated brain pattern. Task-discriminant component analysis (TDCA) was utilized to perform multiclass motor decoding. <italic>Results:</i> The results demonstrated distinct lateralized ERD in the alpha and beta bands, and clear lateralized steady-state movement-related rhythm (SSMRR) at the movement frequencies and their first harmonics. The activated brain areas included frontal, sensorimotor, posterior parietal, and occipital regions. Notably, the decoding accuracy reached 92.16% ± 7.61% in the four-class scenario. <italic>Conclusion and Significance:</i> We proposed the AORI paradigm, revealed the activated motor-related pattern and proved its efficacy for high-performance motor decoding. These findings provide new possibilities for designing a natural and robust BCI for motor control and motor rehabilitation.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1085-1096"},"PeriodicalIF":4.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521804","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}
Pub Date : 2024-10-25DOI: 10.1109/TBME.2024.3483936
Gabriel Leander Wagner vom Berg;Vera Röhr;Daniel Platt;Benjamin Blankertz
Objective: Working with the Riemannian manifold of symmetric positive-definite (SPD) matrices has become popular in electroencephalography (EEG) analysis. Frequently selected for its speed property is the manifold geometry provided by the log-euclidean Riemannian metric. However, the kernels used in the log-euclidean framework are not canonically based on the underlying geometry. Therefore, we introduce a new canonical log-euclidean (CLE) kernel. Methods: We used the log-euclidean metric tensor on the SPD manifold to derive the CLE kernel. We compared it with existing kernels, namely the affine-invariant, log-euclidean, and Gaussian log-euclidean kernel. For comparison, we tested the kernels on two paradigms: classification and dimensionality reduction. Each paradigm was evaluated on five open-access brain-computer interface datasets with motor-imagery tasks across multiple sessions. Performance was measured as balanced classification accuracy using a leave-one-session-out cross-validation. Dimensionality reduction performance was measured using AUClogRNX. Results: The CLE kernel itself is simple and easily turned into code, which is provided in addition to all the analytical solutions to relevant equations in the log-euclidean framework. The CLE kernel significantly outperformed existing log-euclidean kernels in classification tasks and was several times faster than the affine-invariant kernel for most datasets. Conclusion: We found that adhering to the geometrical structure significantly improves the accuracy over two commonly used log-euclidean kernels while keeping the speed advantages of the log-euclidean framework. Significance: The CLE provides a good choice as a kernel in time-critical applications and fills a gap in the kernel methods of the log-euclidean framework.
{"title":"A New Canonical Log-Euclidean Kernel for Symmetric Positive Definite Matrices for EEG Analysis (Oct 2024)","authors":"Gabriel Leander Wagner vom Berg;Vera Röhr;Daniel Platt;Benjamin Blankertz","doi":"10.1109/TBME.2024.3483936","DOIUrl":"https://doi.org/10.1109/TBME.2024.3483936","url":null,"abstract":"<italic>Objective:</i> Working with the Riemannian manifold of symmetric positive-definite (SPD) matrices has become popular in electroencephalography (EEG) analysis. Frequently selected for its speed property is the manifold geometry provided by the log-euclidean Riemannian metric. However, the kernels used in the log-euclidean framework are not canonically based on the underlying geometry. Therefore, we introduce a new canonical log-euclidean (CLE) kernel. <italic>Methods:</i> We used the log-euclidean metric tensor on the SPD manifold to derive the CLE kernel. We compared it with existing kernels, namely the affine-invariant, log-euclidean, and Gaussian log-euclidean kernel. For comparison, we tested the kernels on two paradigms: classification and dimensionality reduction. Each paradigm was evaluated on five open-access brain-computer interface datasets with motor-imagery tasks across multiple sessions. Performance was measured as balanced classification accuracy using a leave-one-session-out cross-validation. Dimensionality reduction performance was measured using AUClogRNX. <italic>Results:</i> The CLE kernel itself is simple and easily turned into code, which is provided in addition to all the analytical solutions to relevant equations in the log-euclidean framework. The CLE kernel significantly outperformed existing log-euclidean kernels in classification tasks and was several times faster than the affine-invariant kernel for most datasets. <italic>Conclusion:</i> We found that adhering to the geometrical structure significantly improves the accuracy over two commonly used log-euclidean kernels while keeping the speed advantages of the log-euclidean framework. <italic>Significance:</i> The CLE provides a good choice as a kernel in time-critical applications and fills a gap in the kernel methods of the log-euclidean framework.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1000-1007"},"PeriodicalIF":4.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10735221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1109/TBME.2024.3486628
Federica Gioia, Alberto Greco, Alejandro Luis Callara, Nicola Vanello, Enzo Pasquale Scilingo, Luca Citi
Objective: Infrared Thermography (IRT) has been used to monitor skin temperature variation in a contactless manner, in both clinical medicine and psychophysiology. Here, we introduce a new methodology to obtain information about autonomic correlates related to perspiration, peripheral vasomotility, and respiration from infrared recordings.
Methods: Our approach involves a model-based decomposition of facial thermograms using Independent Component Analysis (ICA) and an ad-hoc preprocessing procedure. We tested our approach on 30 healthy volunteers whose psychophysiological state was stimulated as part of an experimental protocol.
Results: Within-subject ICA analysis identified three independent components demonstrating correlations with the reference physiological signals. Moreover, a linear combination of independent components effectively predicted each physiological signal, achieving median correlations of 0.9 for electrodermal activity, 0.8 for respiration, and 0.73 for photoplethysmography peaks envelope. In addition, we performed a cross-validated inter-subject analysis, which allows to predict physiological signals from facial thermograms of unseen subjects.
Conclusions/significance: Our findings validate the efficacy of features extracted from both original and thermal-derived signals for differentiating experimental conditions. This outcome emphasizes the sensitivity and promise of our approach, advocating for expanded investigations into thermal imaging within biomedical signal analysis. It underscores its potential for enhancing objective assessments of emotional states.
{"title":"ThermICA: Novel Approach for a Multivariate Analysis of Facial Thermal Responses.","authors":"Federica Gioia, Alberto Greco, Alejandro Luis Callara, Nicola Vanello, Enzo Pasquale Scilingo, Luca Citi","doi":"10.1109/TBME.2024.3486628","DOIUrl":"https://doi.org/10.1109/TBME.2024.3486628","url":null,"abstract":"<p><strong>Objective: </strong>Infrared Thermography (IRT) has been used to monitor skin temperature variation in a contactless manner, in both clinical medicine and psychophysiology. Here, we introduce a new methodology to obtain information about autonomic correlates related to perspiration, peripheral vasomotility, and respiration from infrared recordings.</p><p><strong>Methods: </strong>Our approach involves a model-based decomposition of facial thermograms using Independent Component Analysis (ICA) and an ad-hoc preprocessing procedure. We tested our approach on 30 healthy volunteers whose psychophysiological state was stimulated as part of an experimental protocol.</p><p><strong>Results: </strong>Within-subject ICA analysis identified three independent components demonstrating correlations with the reference physiological signals. Moreover, a linear combination of independent components effectively predicted each physiological signal, achieving median correlations of 0.9 for electrodermal activity, 0.8 for respiration, and 0.73 for photoplethysmography peaks envelope. In addition, we performed a cross-validated inter-subject analysis, which allows to predict physiological signals from facial thermograms of unseen subjects.</p><p><strong>Conclusions/significance: </strong>Our findings validate the efficacy of features extracted from both original and thermal-derived signals for differentiating experimental conditions. This outcome emphasizes the sensitivity and promise of our approach, advocating for expanded investigations into thermal imaging within biomedical signal analysis. It underscores its potential for enhancing objective assessments of emotional states.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499468","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}
Pub Date : 2024-10-25DOI: 10.1109/TBME.2024.3462313
{"title":"IEEE Transactions on Biomedical Engineering Information for Authors","authors":"","doi":"10.1109/TBME.2024.3462313","DOIUrl":"https://doi.org/10.1109/TBME.2024.3462313","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"71 11","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1109/TBME.2024.3486580
Orlane Duport;Virginie Le Rolle;Gustavo Guerrero;Alain Beuchée;Alfredo Hernández
Objective: Preterm infants are particularly exposed to severe cardio-respiratory events, associating apnea with bradycardia and oxygen desaturation. A patient-specific and event-specific model-based approach is proposed in this work to analyze the acute heart rate response to apnea-bradycardia events in preterm newborn. Methods: A novel model integrating the main cardio-respiratory interactions which are specific to the neonatal period is proposed. An evolutionary algorithm is applied to estimate patient-specific model parameters from a database of 37 apnea-bradycardia episodes acquired from 10 preterm newborns. Unsupervised clustering (K-means) was applied to the identified parameters to define phenogroups of cardio-respiratory responses to apnea. Results: A significant correspondence was observed between simulated and experimental heart rate series in all identifications (median RMSE = 8.85 bpm). Three clusters of parameters were found and were associated to three different pathophysiological dynamics related to apnea-bradycardia. Conclusion and significance: The proposed method, based on patient and event-specific model parameter identification, provides a novel approach to characterize bradycardia dynamics in response to apnea, opening the way to the proposal of new personalized diagnosis and treatment possibilities in this particularly sensitive population.
{"title":"Modeling Patient-Specific Apnea-Bradycardia Patterns in Preterm Newborn","authors":"Orlane Duport;Virginie Le Rolle;Gustavo Guerrero;Alain Beuchée;Alfredo Hernández","doi":"10.1109/TBME.2024.3486580","DOIUrl":"10.1109/TBME.2024.3486580","url":null,"abstract":"<italic>Objective:</i> Preterm infants are particularly exposed to severe cardio-respiratory events, associating apnea with bradycardia and oxygen desaturation. A patient-specific and event-specific model-based approach is proposed in this work to analyze the acute heart rate response to apnea-bradycardia events in preterm newborn. <italic>Methods:</i> A novel model integrating the main cardio-respiratory interactions which are specific to the neonatal period is proposed. An evolutionary algorithm is applied to estimate patient-specific model parameters from a database of 37 apnea-bradycardia episodes acquired from 10 preterm newborns. Unsupervised clustering (K-means) was applied to the identified parameters to define phenogroups of cardio-respiratory responses to apnea. <italic>Results:</i> A significant correspondence was observed between simulated and experimental heart rate series in all identifications (median RMSE = 8.85 bpm). Three clusters of parameters were found and were associated to three different pathophysiological dynamics related to apnea-bradycardia. <italic>Conclusion and significance:</i> The proposed method, based on patient and event-specific model parameter identification, provides a novel approach to characterize bradycardia dynamics in response to apnea, opening the way to the proposal of new personalized diagnosis and treatment possibilities in this particularly sensitive population.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 3","pages":"1067-1077"},"PeriodicalIF":4.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499465","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}
Pub Date : 2024-10-25DOI: 10.1109/TBME.2024.3462311
{"title":"IEEE Engineering in Medicine and Biology Society Information","authors":"","doi":"10.1109/TBME.2024.3462311","DOIUrl":"https://doi.org/10.1109/TBME.2024.3462311","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"71 11","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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