The level of motor unit alternation in compound muscle action potential scans alters markedly by ... more The level of motor unit alternation in compound muscle action potential scans alters markedly by varying the stimulus duration. The threshold variability of individual motor units remains unaffected by varying the stimulus duration. Implementation of compound muscle action potential scans in a clinical setting requires standardization of stimulation settings. a b s t r a c t Objective: To investigate the impact of stimulus duration on motor unit (MU) thresholds and alternation within compound muscle action potential (CMAP) scans. Methods: The stimulus duration (0.1, 0.2, 0.6, and 1.0 ms) in thenar CMAP scans and individual MUs of 14 healthy subjects was systematically varied. We quantified variability of individual MU's thresholds by relative spread (RS), MU thresholds by stimulus currents required to elicit target CMAPs of 5% (S5), 50% (S50) and 95% (S95) of the maximum CMAP, and relative range (RR) by 100*[S95-S5]/S50. We further assessed the strength-duration time constant (SDTC). Experimental observations were subsequently simulated to quantify alternation. Results: RS, unaffected by stimulus duration, was 1.65% averaged over all recordings. RR increased for longer stimulus duration (11.4% per ms, p < 0.001). SDTC shortened with higher target CMAPs (0.007 ms per 10% CMAP, p < 0.001). Experiments and simulations supported that this may underlie the increased RR. A short compared to long stimulus duration recruited relative more MUs at S50 (more alternation) than at the tails (less alternation). Conclusions: The stimulus duration significantly affects MU threshold distribution and alternation within CMAP scans. Significance: Stimulation settings can be further optimized and their standardization is preferred when using CMAP scans for monitoring neuromuscular diseases.
Automatic detection and analysis of respiratory events in sleep using a single respiratory effort... more Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning. Methods: Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings. Results: For binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.41±7.8 and a r 2 of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas. Conclusion: Our fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation. Significance: The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations.
The gold standard to assess respiration during sleep is polysomnography; a technique that is burd... more The gold standard to assess respiration during sleep is polysomnography; a technique that is burdensome, expensive (both in analysis time and measurement costs), and difficult to repeat. Automation of respiratory analysis can improve test efficiency and enable accessible implementation opportunities worldwide. Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) based on a single respiratory effort belt to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based and recording-based metrics – using an apnea-hypopnea index analysis. The model was further evaluated on a public dataset, the SleepHeart-Health-Study-1, containing 8,455 polysomnographic recordings. For binary apnea event detection in the MGH dataset, the neural network obtained an accuracy of 95%, an apnea-hypopnea index r2 of 0.89 and area under the curve for the receiver ope...
Cortex) is a region of the cortex engaged in focused attention processes, related to the search f... more Cortex) is a region of the cortex engaged in focused attention processes, related to the search for goals of a specific visual context. Transcranial magnetic stimulation with a series of stimuli (repetitive transcranial magnetic stimulation-rTMS) is a method of modulating brain plasticity. We have investigated the effect of rTMS in 20 healthy volunteers. All of them gave written informed consent to the study. rTMS was applied on posterior parietal cortices V1 on both hemispheres with the inhibitory Theta Bursts paradigm with the intensity of 70% of motor threshold. The targets were visualized with the neuronavigation system. Immediately after the stimulation, the functional MRI was performed with the protocol of recognizing of significant military objects from the visual field. In subgroup of participants (n = 15) after the stimulation there were significant reductions in blood flow within V1 cortices. The studies of reaction times after the rTMS also revealed the inhibitory effect of rTMS on the reaction times and recognition performance of significant (military) objects in the visual field. There were no side effects reported by participants and observed by researchers during the whole study.
The level of motor unit alternation in compound muscle action potential scans alters markedly by ... more The level of motor unit alternation in compound muscle action potential scans alters markedly by varying the stimulus duration. The threshold variability of individual motor units remains unaffected by varying the stimulus duration. Implementation of compound muscle action potential scans in a clinical setting requires standardization of stimulation settings. a b s t r a c t Objective: To investigate the impact of stimulus duration on motor unit (MU) thresholds and alternation within compound muscle action potential (CMAP) scans. Methods: The stimulus duration (0.1, 0.2, 0.6, and 1.0 ms) in thenar CMAP scans and individual MUs of 14 healthy subjects was systematically varied. We quantified variability of individual MU's thresholds by relative spread (RS), MU thresholds by stimulus currents required to elicit target CMAPs of 5% (S5), 50% (S50) and 95% (S95) of the maximum CMAP, and relative range (RR) by 100*[S95-S5]/S50. We further assessed the strength-duration time constant (SDTC). Experimental observations were subsequently simulated to quantify alternation. Results: RS, unaffected by stimulus duration, was 1.65% averaged over all recordings. RR increased for longer stimulus duration (11.4% per ms, p < 0.001). SDTC shortened with higher target CMAPs (0.007 ms per 10% CMAP, p < 0.001). Experiments and simulations supported that this may underlie the increased RR. A short compared to long stimulus duration recruited relative more MUs at S50 (more alternation) than at the tails (less alternation). Conclusions: The stimulus duration significantly affects MU threshold distribution and alternation within CMAP scans. Significance: Stimulation settings can be further optimized and their standardization is preferred when using CMAP scans for monitoring neuromuscular diseases.
Automatic detection and analysis of respiratory events in sleep using a single respiratory effort... more Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning. Methods: Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings. Results: For binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.41±7.8 and a r 2 of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas. Conclusion: Our fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation. Significance: The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations.
The gold standard to assess respiration during sleep is polysomnography; a technique that is burd... more The gold standard to assess respiration during sleep is polysomnography; a technique that is burdensome, expensive (both in analysis time and measurement costs), and difficult to repeat. Automation of respiratory analysis can improve test efficiency and enable accessible implementation opportunities worldwide. Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) based on a single respiratory effort belt to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based and recording-based metrics – using an apnea-hypopnea index analysis. The model was further evaluated on a public dataset, the SleepHeart-Health-Study-1, containing 8,455 polysomnographic recordings. For binary apnea event detection in the MGH dataset, the neural network obtained an accuracy of 95%, an apnea-hypopnea index r2 of 0.89 and area under the curve for the receiver ope...
Cortex) is a region of the cortex engaged in focused attention processes, related to the search f... more Cortex) is a region of the cortex engaged in focused attention processes, related to the search for goals of a specific visual context. Transcranial magnetic stimulation with a series of stimuli (repetitive transcranial magnetic stimulation-rTMS) is a method of modulating brain plasticity. We have investigated the effect of rTMS in 20 healthy volunteers. All of them gave written informed consent to the study. rTMS was applied on posterior parietal cortices V1 on both hemispheres with the inhibitory Theta Bursts paradigm with the intensity of 70% of motor threshold. The targets were visualized with the neuronavigation system. Immediately after the stimulation, the functional MRI was performed with the protocol of recognizing of significant military objects from the visual field. In subgroup of participants (n = 15) after the stimulation there were significant reductions in blood flow within V1 cortices. The studies of reaction times after the rTMS also revealed the inhibitory effect of rTMS on the reaction times and recognition performance of significant (military) objects in the visual field. There were no side effects reported by participants and observed by researchers during the whole study.
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Papers by Thijs Nassi