Pub Date : 2024-09-06DOI: 10.1088/1361-6579/ad74d5
Buket Sonbas Cobb, Stephen J Kolb, Seward B Rutkove
Objective.To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.Approach. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression.Main Results.Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment approaches available at the time. LDA scores were also able to track progression effectively, although a multifrequency reactance-based measure also performed very well in this context.Significance.EIM enhanced with ML promises to be effective for providing effective diagnosis and tracking children and adults with SMA treated with currently available therapies. The normative trends identified here may also inform future applications of the technology in very young children. The basic analyses applied here could also likely be applied to other neuromuscular disorders characterized by muscle atrophy.
目的:
评估电阻抗肌电图(EIM)与机器学习相结合检测小儿脊髓性肌萎缩症(SMA)和疾病进展的效果
方法:
作为 NeuroNEXT SMA 生物标记物研究的一部分,我们对 26 名 SMA 婴儿和 27 名健康婴儿进行了登记和 EIM 评估。我们对这些数据采用了多种现代、有监督的机器学习方法,首先寻求区分健康和 SMA 肌肉,然后使用最佳方法跟踪 SMA 的进展。这与当时使用单频或多频评估方法达到的最高 66% 的准确率形成了鲜明对比。尽管基于多频反应的测量方法在这方面也表现出色,但 LDA 分数也能有效跟踪病情进展。这里确定的标准值和趋势对该技术的其他儿科应用也很有价值。这里应用的基本分析方法也可能适用于其他以肌肉萎缩为特征的神经肌肉疾病。
{"title":"Machine learning-enhanced electrical impedance myography to diagnose and track spinal muscular atrophy progression.","authors":"Buket Sonbas Cobb, Stephen J Kolb, Seward B Rutkove","doi":"10.1088/1361-6579/ad74d5","DOIUrl":"10.1088/1361-6579/ad74d5","url":null,"abstract":"<p><p><i>Objective.</i>To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.<i>Approach</i>. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression.<i>Main Results.</i>Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment approaches available at the time. LDA scores were also able to track progression effectively, although a multifrequency reactance-based measure also performed very well in this context.<i>Significance.</i>EIM enhanced with ML promises to be effective for providing effective diagnosis and tracking children and adults with SMA treated with currently available therapies. The normative trends identified here may also inform future applications of the technology in very young children. The basic analyses applied here could also likely be applied to other neuromuscular disorders characterized by muscle atrophy.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142093680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1088/1361-6579/ad7777
Jose Javier Reyes-Lagos, Eric Alonso Abarca-Castro, Claudia Ivette Ledesma-Ramírez, Adriana Cristina Pliego-Carrillo, Araceli Espinosa-Guerrero, Guadalupe Dorantes Méndez
Objective: This study aims to use Recurrence Quantification Analysis (RQA) of uterine vectormyometriogram (VMG) created from the slow wave (SW) and high wave (HW) bands of electrohysterogram (EHG) signals and assess the directionality of the EHG activity (horizontal or X, vertical or Y) in normal-weight (NW) and overweight (OW) women during the first stage of labor.
Approach: The study involved 41 parturient women (NW=21 and OW=20) during the first stage of labor, all of whom were attended at the Gynecology and Obstetrics Hospital of the Maternal and Child Institute of the State of Mexico (IMIEM) in Toluca, Mexico. Twenty-minute EHG signals were analyzed in horizontal and vertical directions. Linear and nonlinear indices such as dominant frequency (Dom), Sample Entropy (SampEn), and RQA measures of VMG were computed for SW and HW bands.
Main results: Significant differences in SampEn and Dom were observed in the SW band between NW and OW in both X and Y directions, indicating more regular dynamics of electrical uterine activity and a higher dominant frequency in normal-weight parturient women compared to overweight women. Additionally, the RQA indices calculated from the VMG of SW were consistent and revealed that NW women exhibit more regular dynamics compared to OW women.
Significance: The study demonstrates that RQA of VMG signals and EHG directionality differentiate uterine activity between NW and OW women during the first stage of labor. These findings suggest that the uterine vector may become more periodic, predictable, and stable in normal-weight women compared to overweight women. This highlights the importance of tailored clinical strategies for managing labor in overweight women to improve maternal and infant outcomes.
.
{"title":"Recurrence quantification analysis of uterine vectormyometriogram reveals differences between normal weight and overweight parturient women.","authors":"Jose Javier Reyes-Lagos, Eric Alonso Abarca-Castro, Claudia Ivette Ledesma-Ramírez, Adriana Cristina Pliego-Carrillo, Araceli Espinosa-Guerrero, Guadalupe Dorantes Méndez","doi":"10.1088/1361-6579/ad7777","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7777","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to use Recurrence Quantification Analysis (RQA) of uterine vectormyometriogram (VMG) created from the slow wave (SW) and high wave (HW) bands of electrohysterogram (EHG) signals and assess the directionality of the EHG activity (horizontal or X, vertical or Y) in normal-weight (NW) and overweight (OW) women during the first stage of labor.</p><p><strong>Approach: </strong>The study involved 41 parturient women (NW=21 and OW=20) during the first stage of labor, all of whom were attended at the Gynecology and Obstetrics Hospital of the Maternal and Child Institute of the State of Mexico (IMIEM) in Toluca, Mexico. Twenty-minute EHG signals were analyzed in horizontal and vertical directions. Linear and nonlinear indices such as dominant frequency (Dom), Sample Entropy (SampEn), and RQA measures of VMG were computed for SW and HW bands.</p><p><strong>Main results: </strong>Significant differences in SampEn and Dom were observed in the SW band between NW and OW in both X and Y directions, indicating more regular dynamics of electrical uterine activity and a higher dominant frequency in normal-weight parturient women compared to overweight women. Additionally, the RQA indices calculated from the VMG of SW were consistent and revealed that NW women exhibit more regular dynamics compared to OW women.</p><p><strong>Significance: </strong>The study demonstrates that RQA of VMG signals and EHG directionality differentiate uterine activity between NW and OW women during the first stage of labor. These findings suggest that the uterine vector may become more periodic, predictable, and stable in normal-weight women compared to overweight women. This highlights the importance of tailored clinical strategies for managing labor in overweight women to improve maternal and infant outcomes. 
.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.
Approach: Employing the unsupervised manifold learning algorithm, Dynamic Diffusion Map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.
Main results: Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.
Significance: The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.
{"title":"Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.","authors":"Yu Chieh Ho, Te-Sheng Lin, Shen-Chih Wang, Cheng-Hsi Chang, Yu-Ting Lin","doi":"10.1088/1361-6579/ad7779","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7779","url":null,"abstract":"<p><strong>Objective: </strong>We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing. 
Approach: Employing the unsupervised manifold learning algorithm, Dynamic Diffusion Map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database. 
Main results: Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis. 
Significance: The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1088/1361-6579/ad7778
Agnieszka Uryga, Mikołaj Najda, Ignacy Berent, Cyprian Mataczyński, Piotr Urbański, Magdalena Kasprowicz, Teodor Buchner
Objective
The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS).
Approach
The analyzed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5-minute sessions of controlled breathing at three different frequencies: 6, 10, and 15 breaths/min. CO2 concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including Phase-Rectified Signal Averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn), as well as time and frequency domains (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV).
Main Results
Higher breathing rates resulted in a significant decrease in end-tidal CO2 concentration (p < 0.001), accompanied by increases in both ABP (p<0.001) and heart rate (p<0.001). A strong, linear decline in AC and DC (p<0.001 for both) was observed with increasing respiratory rate. All entropy metrics increased with breathing frequency (p<0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (p<0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (p = 0.038 and p = 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies.
Significance
Alterations in CO2 levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations.
.
{"title":"The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability.","authors":"Agnieszka Uryga, Mikołaj Najda, Ignacy Berent, Cyprian Mataczyński, Piotr Urbański, Magdalena Kasprowicz, Teodor Buchner","doi":"10.1088/1361-6579/ad7778","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7778","url":null,"abstract":"<p><p>Objective
The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS).

Approach
The analyzed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5-minute sessions of controlled breathing at three different frequencies: 6, 10, and 15 breaths/min. CO2 concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including Phase-Rectified Signal Averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn), as well as time and frequency domains (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV). 

Main Results
Higher breathing rates resulted in a significant decrease in end-tidal CO2 concentration (p < 0.001), accompanied by increases in both ABP (p<0.001) and heart rate (p<0.001). A strong, linear decline in AC and DC (p<0.001 for both) was observed with increasing respiratory rate. All entropy metrics increased with breathing frequency (p<0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (p<0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (p = 0.038 and p = 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies.

Significance
Alterations in CO2 levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations.
.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Accurate prediction of unmearsured muscle excitations can reduce the required wearable surface electromyography (sEMG) sensors, which is a critical factor in the study of physiological measurement. Synergy extrapolation uses synergy excitations as building blocks to reconstruct muscle excitations. However, the practical application of synergy extrapolation is still limited as the extrapolation process utilizes unmeasured muscle excitations it seeks to reconstruct. This paper aims to propose and derive methods to provide an avenue for the practical application of synergy extrapolation with non-negative matrix factorization (NMF) methods.
Approach: Specifically, a tunable Gaussian-Laplacian mixture distribution NMF (GLD-NMF) method and related multiplicative update rules are derived to yield appropriate synergy excitations for extrapolation. Furthermore, a template-based extrapolation structure (TBES) is proposed to extrapolate unmeasured muscle excitations based on synergy weighting matrix templates totally extracted from measured sEMG datasets, improving the extrapolation performance. Moreover, we applied the proposed GLD-NMF method and TBES to selected muscle excitations acquired from a series of single-leg stance (SLS) tests, walking tests and upper limb reaching tests.
Main results: Experimental results show that the proposed GLD-NMF and TBES could extrapolate unmeasured muscle excitations accurately. Moreover, introducing synergy weighting matrix templates could decrease the number of sEMG sensors in a series of experiments. In addition, verification results demonstrate the feasibility of applying synergy extrapolation with NMF methods.
Significance: With the TBES method, synergy extrapolation could play a significant role in reducing data dimensions of sEMG sensors, which will improve the portability of sEMG sensors-based systems and promotes applications of sEMG signals in human-machine interfaces scenarios.
{"title":"Template-based synergy extrapolation analysis for prediction of muscle excitations.","authors":"Kaitai Li, Daming Wang, Zuobing Chen, Dazhi Guo, Shuyi Pan, Hui Liu, Congcong Zhou, Xuesong Ye","doi":"10.1088/1361-6579/ad7776","DOIUrl":"https://doi.org/10.1088/1361-6579/ad7776","url":null,"abstract":"<p><strong>Objective: </strong>Accurate prediction of unmearsured muscle excitations can reduce the required wearable surface electromyography (sEMG) sensors, which is a critical factor in the study of physiological measurement. Synergy extrapolation uses synergy excitations as building blocks to reconstruct muscle excitations. However, the practical application of synergy extrapolation is still limited as the extrapolation process utilizes unmeasured muscle excitations it seeks to reconstruct. This paper aims to propose and derive methods to provide an avenue for the practical application of synergy extrapolation with non-negative matrix factorization (NMF) methods.</p><p><strong>Approach: </strong>Specifically, a tunable Gaussian-Laplacian mixture distribution NMF (GLD-NMF) method and related multiplicative update rules are derived to yield appropriate synergy excitations for extrapolation. Furthermore, a template-based extrapolation structure (TBES) is proposed to extrapolate unmeasured muscle excitations based on synergy weighting matrix templates totally extracted from measured sEMG datasets, improving the extrapolation performance. Moreover, we applied the proposed GLD-NMF method and TBES to selected muscle excitations acquired from a series of single-leg stance (SLS) tests, walking tests and upper limb reaching tests.</p><p><strong>Main results: </strong>Experimental results show that the proposed GLD-NMF and TBES could extrapolate unmeasured muscle excitations accurately. Moreover, introducing synergy weighting matrix templates could decrease the number of sEMG sensors in a series of experiments. In addition, verification results demonstrate the feasibility of applying synergy extrapolation with NMF methods.</p><p><strong>Significance: </strong>With the TBES method, synergy extrapolation could play a significant role in reducing data dimensions of sEMG sensors, which will improve the portability of sEMG sensors-based systems and promotes applications of sEMG signals in human-machine interfaces scenarios.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1088/1361-6579/ad7348
William B Hammert, Ryo Kataoka, Yujiro Yamada, Jun Seob Song, Anna Kang, Robert W Spitz, Jeremy P Loenneke
Progressive overload describes the gradual increase of stress placed on the body during exercise training, and is often quantified (i.e. in resistance training studies) through increases in total training volume (i.e. sets × repetitions × load) from the first to final week of the exercise training intervention. Within the literature, it has become increasingly common for authors to discuss skeletal muscle growth adaptations in the context of increases in total training volume (i.e. the magnitude progression in total training volume). The present manuscript discusses a physiological rationale for progressive overload and then explains why, in our opinion, quantifying the progression of total training volume within research investigations tells very little about muscle growth adaptations to resistance training. Our opinion is based on the following research findings: (1) a noncausal connection between increases in total training volume (i.e. progressively overloading the resistance exercise stimulus) and increases in skeletal muscle size; (2) similar changes in total training volume may not always produce similar increases in muscle size; and (3) the ability to exercise more and consequently amass larger increases in total training volume may not inherently produce more skeletal muscle growth. The methodology of quantifying changes in total training volume may therefore provide a means through which researchers can mathematically determine the total amount of external 'work' performed within a resistance training study. It may not, however, always explain muscle growth adaptations.
渐进性超负荷是指在运动训练过程中身体所承受的压力逐渐增加,通常通过从运动训练干预的第一周到最后一周总训练量(即组数 x 重复次数 x 负荷)的增加来量化(例如,在阻力训练研究中)。在文献中,越来越多的作者结合总训练量的增加(即总训练量的幅度递增)来讨论骨骼肌的生长适应性。本手稿讨论了渐进超负荷的生理学原理,然后解释了为什么我们认为在研究调查中量化总训练量的增长对阻力训练的肌肉生长适应性意义不大。我们的观点基于以下研究结果:(1)总训练量的增加(即逐渐超负荷的阻力训练刺激)与骨骼肌体积的增加之间存在非因果关系;(2)类似的总训练量变化不一定总是会产生类似的肌肉体积增加;以及(3)能够进行更多的锻炼并因此积累更多的总训练量增加可能并不会在本质上产生更多的骨骼肌生长。因此,量化总训练量变化的方法可以为研究人员提供一种手段,通过这种方法,研究人员可以用数学方法确定阻力训练研究中进行的外部 "工作 "总量。但是,这种方法并不能解释肌肉生长的适应性。
{"title":"Progression of total training volume in resistance training studies and its application to skeletal muscle growth.","authors":"William B Hammert, Ryo Kataoka, Yujiro Yamada, Jun Seob Song, Anna Kang, Robert W Spitz, Jeremy P Loenneke","doi":"10.1088/1361-6579/ad7348","DOIUrl":"10.1088/1361-6579/ad7348","url":null,"abstract":"<p><p>Progressive overload describes the gradual increase of stress placed on the body during exercise training, and is often quantified (i.e. in resistance training studies) through increases in total training volume (i.e. sets × repetitions × load) from the first to final week of the exercise training intervention. Within the literature, it has become increasingly common for authors to discuss skeletal muscle growth adaptations in the context of increases in total training volume (i.e. the magnitude progression in total training volume). The present manuscript discusses a physiological rationale for progressive overload and then explains why, in our opinion, quantifying the progression of total training volume within research investigations tells very little about muscle growth adaptations to resistance training. Our opinion is based on the following research findings: (1) a noncausal connection between increases in total training volume (i.e. progressively overloading the resistance exercise stimulus) and increases in skeletal muscle size; (2) similar changes in total training volume may not always produce similar increases in muscle size; and (3) the ability to exercise more and consequently amass larger increases in total training volume may not inherently produce more skeletal muscle growth. The methodology of quantifying changes in total training volume may therefore provide a means through which researchers can mathematically determine the total amount of external 'work' performed within a resistance training study. It may not, however, always explain muscle growth adaptations.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1088/1361-6579/ad75e4
Insun Park, Jae Hyon Park, Bon-Wook Koo, Jin-Hee Kim, Young Tae Jeon, Hyo-Seok Na, Ah-Young Oh
Objective:
This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.
Approach: Long short-term memory and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the long short-term memory consisted of 10 sec CVP waveforms sampled at 2 sec intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.
Main results.
The model hyperparameters consisted of 12 memory cells in the long short-term memory layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval [CI], 0.992-0.993) for SVV measured by EV1000.
Significance.
Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.
.
{"title":"Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.","authors":"Insun Park, Jae Hyon Park, Bon-Wook Koo, Jin-Hee Kim, Young Tae Jeon, Hyo-Seok Na, Ah-Young Oh","doi":"10.1088/1361-6579/ad75e4","DOIUrl":"https://doi.org/10.1088/1361-6579/ad75e4","url":null,"abstract":"<p><strong>Objective: </strong>
This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.</p><p><strong>Approach: </strong>Long short-term memory and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the long short-term memory consisted of 10 sec CVP waveforms sampled at 2 sec intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.
Main results.
The model hyperparameters consisted of 12 memory cells in the long short-term memory layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval [CI], 0.992-0.993) for SVV measured by EV1000.
Significance. 
Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.
.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142110751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1088/1361-6579/ad74d7
Xavier Navarro-Suné, Mathieu Raux, Anna Hudson, Thomas Similowski, Mario Chavez
Time-Frequency (T-F) analysis of EEG is a common technique to characterise spectral changes in neural activity. This study explores the limitations of utilizing conventional spectral techniques in examining cyclic event-related cortical activities due to challenges, including high inter-trial variability. Introducing the Cycle-Frequency (C-F) analysis, we aim to enhance the evaluation of cycle-locked respiratory events. For synthetic EEG that mimicked cycle-locked pre-motor activity, C-F had more accurate frequency and time localization compared to conventional T-F analysis, even for a significantly reduced number of trials and a variability of breathing rhythm. Preliminary validations using real EEG data during both unloaded breathing and loaded breathing (that evokes pre-motor activity) suggest potential benefits of using the C-F method, particularly in normalizing time units to cyclic activity phases and refining baseline placement and duration. The proposed approach could provide new insights for the study of rhythmic neural activities, complementing T-F analysis.
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{"title":"Cycle-frequency content EEG analysis improves the assessment of respiratory-related cortical activity.","authors":"Xavier Navarro-Suné, Mathieu Raux, Anna Hudson, Thomas Similowski, Mario Chavez","doi":"10.1088/1361-6579/ad74d7","DOIUrl":"https://doi.org/10.1088/1361-6579/ad74d7","url":null,"abstract":"<p><p>Time-Frequency (T-F) analysis of EEG is a common technique to characterise spectral changes in neural activity. This study explores the limitations of utilizing conventional spectral techniques in examining cyclic event-related cortical activities due to challenges, including high inter-trial variability. Introducing the Cycle-Frequency (C-F) analysis, we aim to enhance the evaluation of cycle-locked respiratory events. For synthetic EEG that mimicked cycle-locked pre-motor activity, C-F had more accurate frequency and time localization compared to conventional T-F analysis, even for a significantly reduced number of trials and a variability of breathing rhythm. Preliminary validations using real EEG data during both unloaded breathing and loaded breathing (that evokes pre-motor activity) suggest potential benefits of using the C-F method, particularly in normalizing time units to cyclic activity phases and refining baseline placement and duration. The proposed approach could provide new insights for the study of rhythmic neural activities, complementing T-F analysis.
.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142093678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1088/1361-6579/ad74d6
Yesim Serinagaoglu Dogrusoz, Laura Bear, Jake A Bergquist, Ali Rababah, Wilson Good, Job Stoks, Jana Svehlikova, Eelco van Dam, Dana H Brooks, Rob MacLeod
Objective: This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.
Approach. We utilized experimental data from two distinct centers. Langendorff-perfused pig (n=2) and dog (n=2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.
Main results. The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.
Significance. This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.
{"title":"Evaluation of five methods for the interpolation of bad leads in the solution of the inverse electrocardiography problem.","authors":"Yesim Serinagaoglu Dogrusoz, Laura Bear, Jake A Bergquist, Ali Rababah, Wilson Good, Job Stoks, Jana Svehlikova, Eelco van Dam, Dana H Brooks, Rob MacLeod","doi":"10.1088/1361-6579/ad74d6","DOIUrl":"https://doi.org/10.1088/1361-6579/ad74d6","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.
Approach. We utilized experimental data from two distinct centers. Langendorff-perfused pig (n=2) and dog (n=2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.
Main results. The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.
Significance. This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142093679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1088/1361-6579/ad6be4
Kai Li, Jiuai Sun
Objective. The widespread adoption of Photoplethysmography (PPG) as a non-invasive method for detecting blood volume variations and deriving vital physiological parameters reflecting health status has surged, primarily due to its accessibility, cost-effectiveness, and non-intrusive nature. This has led to extensive research around this technique in both daily life and clinical applications. Interestingly, despite the existence of contradictory explanations of the underlying mechanism of PPG signals across various applications, a systematic investigation into this crucial matter has not been conducted thus far. This gap in understanding hinders the full exploitation of PPG technology and undermines its accuracy and reliability in numerous applications.Approach. Building upon a comprehensive review of the fundamental principles and technological advancements in PPG, this paper initially attributes the origin of PPG signals to a combination of physical and physiological transmission processes. Furthermore, three distinct models outlining the concerned physiological transmission processes are synthesized, with each model undergoing critical examination based on theoretical underpinnings, empirical evidence, and constraints.Significance. The ultimate objective is to form a fundamental framework for a better understanding of physiological transmission processes in PPG signal generation and to facilitate the development of more reliable technologies for detecting physiological signals.
{"title":"Understanding the physiological transmission mechanisms of photoplethysmography signals: a comprehensive review.","authors":"Kai Li, Jiuai Sun","doi":"10.1088/1361-6579/ad6be4","DOIUrl":"10.1088/1361-6579/ad6be4","url":null,"abstract":"<p><p><i>Objective</i>. The widespread adoption of Photoplethysmography (PPG) as a non-invasive method for detecting blood volume variations and deriving vital physiological parameters reflecting health status has surged, primarily due to its accessibility, cost-effectiveness, and non-intrusive nature. This has led to extensive research around this technique in both daily life and clinical applications. Interestingly, despite the existence of contradictory explanations of the underlying mechanism of PPG signals across various applications, a systematic investigation into this crucial matter has not been conducted thus far. This gap in understanding hinders the full exploitation of PPG technology and undermines its accuracy and reliability in numerous applications.<i>Approach</i>. Building upon a comprehensive review of the fundamental principles and technological advancements in PPG, this paper initially attributes the origin of PPG signals to a combination of physical and physiological transmission processes. Furthermore, three distinct models outlining the concerned physiological transmission processes are synthesized, with each model undergoing critical examination based on theoretical underpinnings, empirical evidence, and constraints.<i>Significance</i>. The ultimate objective is to form a fundamental framework for a better understanding of physiological transmission processes in PPG signal generation and to facilitate the development of more reliable technologies for detecting physiological signals.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}