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Brandon M Westover - One of the best experts on this subject based on the ideXlab platform.

  • algorithm for automatic detection of self similarity and prediction of residual central respiratory events during continuous positive airway pressure
    Sleep, 2021
    Co-Authors: Eline Oppersma, Wolfgang Ganglberger, Haoqi Sun, Robert J Thomas, Brandon M Westover
    Abstract:

    Study objectives Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies "expressed/manifest" HLG via a cyclical self-similarity feature in effort-based respiration signals. Methods Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 10. Central apnea labels are obtained both from manual scoring by sleep Technologists and from an automated algorithm developed for this study. The Massachusetts General Hospital sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on Technologist labels predicted REC with an area under the curve (AUC) of 0.82 ± 0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ± 0.02. A subanalysis was performed on a population with Technologist-labeled diagnostic CAI higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels. Conclusions The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC.

  • algorithm for automatic detection of self similarity and prediction of residual central respiratory events during cpap
    Sleep, 2020
    Co-Authors: E Mosoppersma, Wolfgang Ganglberger, Haoqi Sun, R K Thomas, Brandon M Westover
    Abstract:

    STUDY OBJECTIVES Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies "expressed/manifest" high loop gain via a cyclical self-similarity feature in effort-based respiration signals. METHODS Working under the assumption that high loop gain increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI)>10. Central apnea labels are obtained both from manual scoring by sleep Technologists, and from an automated algorithm developed for this study. The Massachusetts General Hospital (MGH) sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. RESULTS Diagnostic CAI based on Technologist labels predicted REC with an AUC of 0.82 ±0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ±0.02. A subanalysis was performed on a population with Technologist labeled diagnostic CAI>5. Full night similarity predicted REC with an AUC of 0.57 ±0.07 for manual and 0.65 ±0.06 for automated labels. CONCLUSIONS The proposed self-similarity feature, as a surrogate estimate of expressed respiratory high loop gain and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow-limitation, and can aid prediction of REC.

Dmitry Beyder - One of the best experts on this subject based on the ideXlab platform.

  • Reducing Radiation Exposure to the PET Technologist, While Increasing Volume.
    The Journal of Nuclear Medicine, 2016
    Co-Authors: Martin Schmitt, Renee Burney, Kinda Abdin, Dmitry Beyder
    Abstract:

    2735 Objectives Radiation Exposure for Technologists working in PET is higher than in traditional nuclear medicine. Anticipating changes to the operation of our PET facility due to increasing patient volume and staffing changes required us to evaluate how such changes would affect Technologist radiation exposure. Our objective was to identify the sources of exposure and to develop procedures to minimize exposure in anticipation of operational changes. Methods We first acquired some baseline measurements using our current process. We used a whole body and extremity pocket dosimeter to measure real time results. Exposure was recorded in mrem on a datasheet. The datasheet was divided according to where radiation dose was received: drawing dose, injecting dose. Next, we examined our current procedures and identified potential tasks to improve our dispensing and injection procedures. We implemented the changes, and then repeated the experiment. Analysis of the data indicated a decrease in overall tech exposure which led to implementing the new procedures division wide. Results Two areas were identified that decreased overall radiation exposure. First, opportunities were identified at the drawing station, more specifically using devices that gave the Technologist distance from the dose, changes to how we shield the dose during transport to the patient were made. This component resulted in negligible changes to overall exposure. Next, we identified an opportunity in how we administer the dose to the patient, and implemented changes to type of syringe shielding being used. On the initial experiment the total exposure for injecting FDG in a 3ml syringe averaged 2.0 mrem per patient. On the repeat experiment we did not see significant change. However, what we learned is that when the volume of the dose required the use of a 5ml syringe, our new shielding procedure showed a decrease in exposure by 70 percent. Following implementation of final changes, we analyzed 6 months of dosimetry reports. The average monthly study volumes increased by 9.75% while the average number of injections per Technologist increased by 30.0 % (increased patient volume using less staff). For that period of time, the monthly average TLD body dosimeter decreased by 24% and a decrease of 1.4% in ring badge measurements. Conclusions Implementing changes to the type of shielding used during injections and the way dose is transported from the drawing station to the patient resulted in decreased radiation exposure to the Technologist. There are many modern shielding tools available for PET radiation protection, and we recommend for all facilities to evaluate ways to reduce Technologist radiation exposure.

David L Skaggs - One of the best experts on this subject based on the ideXlab platform.

Haoqi Sun - One of the best experts on this subject based on the ideXlab platform.

  • algorithm for automatic detection of self similarity and prediction of residual central respiratory events during continuous positive airway pressure
    Sleep, 2021
    Co-Authors: Eline Oppersma, Wolfgang Ganglberger, Haoqi Sun, Robert J Thomas, Brandon M Westover
    Abstract:

    Study objectives Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies "expressed/manifest" HLG via a cyclical self-similarity feature in effort-based respiration signals. Methods Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 10. Central apnea labels are obtained both from manual scoring by sleep Technologists and from an automated algorithm developed for this study. The Massachusetts General Hospital sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on Technologist labels predicted REC with an area under the curve (AUC) of 0.82 ± 0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ± 0.02. A subanalysis was performed on a population with Technologist-labeled diagnostic CAI higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels. Conclusions The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC.

  • algorithm for automatic detection of self similarity and prediction of residual central respiratory events during cpap
    Sleep, 2020
    Co-Authors: E Mosoppersma, Wolfgang Ganglberger, Haoqi Sun, R K Thomas, Brandon M Westover
    Abstract:

    STUDY OBJECTIVES Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies "expressed/manifest" high loop gain via a cyclical self-similarity feature in effort-based respiration signals. METHODS Working under the assumption that high loop gain increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI)>10. Central apnea labels are obtained both from manual scoring by sleep Technologists, and from an automated algorithm developed for this study. The Massachusetts General Hospital (MGH) sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. RESULTS Diagnostic CAI based on Technologist labels predicted REC with an AUC of 0.82 ±0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ±0.02. A subanalysis was performed on a population with Technologist labeled diagnostic CAI>5. Full night similarity predicted REC with an AUC of 0.57 ±0.07 for manual and 0.65 ±0.06 for automated labels. CONCLUSIONS The proposed self-similarity feature, as a surrogate estimate of expressed respiratory high loop gain and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow-limitation, and can aid prediction of REC.

Wolfgang Ganglberger - One of the best experts on this subject based on the ideXlab platform.

  • algorithm for automatic detection of self similarity and prediction of residual central respiratory events during continuous positive airway pressure
    Sleep, 2021
    Co-Authors: Eline Oppersma, Wolfgang Ganglberger, Haoqi Sun, Robert J Thomas, Brandon M Westover
    Abstract:

    Study objectives Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies "expressed/manifest" HLG via a cyclical self-similarity feature in effort-based respiration signals. Methods Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 10. Central apnea labels are obtained both from manual scoring by sleep Technologists and from an automated algorithm developed for this study. The Massachusetts General Hospital sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on Technologist labels predicted REC with an area under the curve (AUC) of 0.82 ± 0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ± 0.02. A subanalysis was performed on a population with Technologist-labeled diagnostic CAI higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels. Conclusions The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC.

  • algorithm for automatic detection of self similarity and prediction of residual central respiratory events during cpap
    Sleep, 2020
    Co-Authors: E Mosoppersma, Wolfgang Ganglberger, Haoqi Sun, R K Thomas, Brandon M Westover
    Abstract:

    STUDY OBJECTIVES Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies "expressed/manifest" high loop gain via a cyclical self-similarity feature in effort-based respiration signals. METHODS Working under the assumption that high loop gain increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI)>10. Central apnea labels are obtained both from manual scoring by sleep Technologists, and from an automated algorithm developed for this study. The Massachusetts General Hospital (MGH) sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. RESULTS Diagnostic CAI based on Technologist labels predicted REC with an AUC of 0.82 ±0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ±0.02. A subanalysis was performed on a population with Technologist labeled diagnostic CAI>5. Full night similarity predicted REC with an AUC of 0.57 ±0.07 for manual and 0.65 ±0.06 for automated labels. CONCLUSIONS The proposed self-similarity feature, as a surrogate estimate of expressed respiratory high loop gain and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow-limitation, and can aid prediction of REC.