Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

IEEE Trans. Biomed. Eng.

Kunkel

Nguyen

et al. 2023

Self Cite

Objective:The goal of this study was to identify autonomic and gastric myoelectric biomarkers from throughout the day that differentiate patients with gastroparesis, diabetics without gastroparesis, and healthy controls, while providing insight into etiology. Methods: We collected 19 24-hour recordings of electrocardiogram (ECG) and electrogastrogram (EGG) data from healthy controls and patients with diabetic or idiopathic gastroparesis. We used physiologically and statistically rigorous models to extract autonomic and gastric myoelectric information from the ECG and EGG data, respectively. From these, we constructed quantitative indices which differentiated the distinct groups and demonstrated their application in automatic classification paradigms and as quantitative summary scores. Results: We identified several differentiators that separate healthy controls from gastroparetic patient groups, specifically around sleep and meals. We also demonstrated the downstream utility of these differentiators in automatic classification and quantitative scoring paradigms. Even with this small pilot dataset, automated classifiers achieved an accuracy of 79% separating autonomic phenotypes and 65% separating gastrointestinal phenotypes. We also achieved 89% accuracy separating controls from gastroparetic patients in general and 90% accuracy separating diabetics with and without gastroparesis. These differentiators also suggested varying etiologies for different phenotypes. Conclusion:The differentiators we identified were able to successfully distinguish between several autonomic and gastrointestinal (GI) phenotypes using data collected while athome with non-invasive sensors. Significance: Autonomic and gastric myoelectric differentiators, obtained using at-home recording of fully non-invasive signals, can be the first step towards dynamic quantitative markers to track severity, disease progression, and treatment response for combined autonomic and GI phenotypes.

Section: ) Autonomic Informationmentioning

confidence: 99%

Exploring the Gut-Brain Connection in Gastroparesis With Autonomic and Gastric Myoelectric Monitoring

IEEE Trans. Biomed. Eng.

Kunkel

Nguyen

et al. 2023

Self Cite

“…We captured HRV information from the ECG using a point process statistical model derived from physiology that yields truly instantaneous estimates of both time domain and frequency domain HRV measures [19] and has been validated in a variety of clinical settings, including anesthesia [20]. After extracting the times of R peaks from the ECG using the rpeakdetect package [21] in Python 3.0, we computed several continuous indices using the point process HRV model.…”

Section: ) Autonomic Informationmentioning

confidence: 99%

Exploring the Gut-Brain Connection in Gastroparesis with Autonomic and Gastric Myoelectric Monitoring

Kunkel

Nguyen

et al. 2022

Preprint

Self Cite

ObjectiveThe goal of this study was to identify autonomic and gastric myoelectric biomarkers from throughout the day that differentiate patients with gastroparesis, diabetics without gastroparesis, and healthy controls, while providing insight into etiology.MethodsWe collected 19 24-hour recordings of electrocardiogram (ECG) and electrogastrogram (EGG) data from healthy controls and patients with diabetic or idiopathic gastroparesis. We used physiologically and statistically rigorous models to extract autonomic and gastric myoelectric information from the ECG and EGG data respectively. From these, we constructed quantitative indices which differentiated the different groups and demonstrated their application in automatic classification paradigms and as quantitative summary scores.ResultsWe identified several differentiators that separate healthy controls from gastroparetic patient groups, specifically around sleep and meals. We also demonstrated the downstream utility of these differentiators in automatic classification and quantitative scoring paradigms. Even with this small pilot dataset, automated classifiers achieved an accuracy of 79% separating autonomic phenotypes and 65% separating gastrointestinal phenotypes. We also achieved 89% accuracy separating controls from gastroparetic patients in general and 90% accuracy separating diabetics with and without gastroparesis. These differentiators also suggested varying etiologies for different phenotypes.ConclusionThe differentiators we identified were able to successfully distinguish between several autonomic and gastrointestinal (GI) phenotypes using data collected while at-home with non-invasive sensors.SignificanceAutonomic and gastric myoelectric differentiators, obtained using at-home recording of fully non-invasive signals, can be the first step towards dynamic quantitative markers to track severity, disease progression, and treatment response for combined autonomic and GI phenotypes.

“…It has immense potential as a physiological marker to track sympathetic activation in situations such as pain or stress (Amin and Faghih 2022). In clinical settings, it could be used as a non-invasive marker of physiological pain processing in situations in which patients cannot communicate for themselves, such as under anesthesia, during surgery, or when in a coma (Subramanian et al 2020a(Subramanian et al , 2020b(Subramanian et al , 2021a. Tracking sympathetic nervous system activation and regulation would be of clinical utility in the operating room to dose pain medication or in the ICU to measure brainstem function and nociceptive reflexes (Subramanian et al 2020a(Subramanian et al , 2020b(Subramanian et al , 2021a.…”

Section: Introductionmentioning

confidence: 99%

“…In clinical settings, it could be used as a non-invasive marker of physiological pain processing in situations in which patients cannot communicate for themselves, such as under anesthesia, during surgery, or when in a coma (Subramanian et al 2020a(Subramanian et al , 2020b(Subramanian et al , 2021a. Tracking sympathetic nervous system activation and regulation would be of clinical utility in the operating room to dose pain medication or in the ICU to measure brainstem function and nociceptive reflexes (Subramanian et al 2020a(Subramanian et al , 2020b(Subramanian et al , 2021a. Developing frameworks and methodologies to process EDA, including artifact detection and removal specific to clinical situations, would bring it one step closer to being used in the clinic.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting

Tseng²,

Barbieri

et al. 2022

Physiol. Meas.

Self Cite

Objective. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance and could be used in clinical settings in which patients cannot self-report pain, such as during surgery or when in a coma. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings while salvaging as much useful information as possible. Approach. In this study, we collected EDA data from 70 subjects while they were undergoing surgery in the operating room. We then built a fully automated artifact removal framework to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data. This automated framework consisted of first utilizing three unsupervised machine learning methods for anomaly detection, and then customizing the threshold to separate artifact for each data instance by taking advantage of the statistical properties of the artifact in that data instance. We also created simulated surgical data by introducing artifacts into cleaned surgical data and measured the performance of all three methods in removing it. Main results. Our method achieved the highest overall accuracy and precision and lowest overall error on simulated data. One of the other methods prioritized high sensitivity while sacrificing specificity and precision, while the other had low sensitivity, high error, and left behind several artifacts. These results were qualitatively similar between the simulated data instances and operating room data instances. Significance. Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery, which is the first step to enable clinical integration of EDA as part of standard monitoring.