scholarly journals Assessing REM Sleep Behaviour Disorder: From Machine Learning Classification to the Definition of a Continuous Dissociation Index

Author(s):  
Irene Rechichi ◽  
Antonella Iadarola ◽  
Maurizio Zibetti ◽  
Alessandro Cicolin ◽  
Gabriella Olmo

Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a prodrome of neurodegeneration, with a high conversion rate to α–synucleinopathies such as Parkinson’s Disease (PD). The clinical diagnosis of RBD co–exists with evidence of REM Sleep Without Atonia (RSWA), a parasomnia that features loss of physiological muscular atonia during REM sleep. The objectives of this study are to implement an automatic detection of RSWA from polysomnographic traces, and to propose a continuous index (the Dissociation Index) to assess the level of dissociation between REM sleep stage and atonia. This is performed using Euclidean distance in proper vector spaces. Each subject is assigned a dissociation degree based on their distance from a reference, encompassing healthy subjects and clinically diagnosed RBD patients at the two extremes. Methods: Machine Learning models were employed to perform automatic identification of patients with RSWA through clinical polysomnographic scores, together with variables derived from electromyography. Proper distance metrics are proposed and tested to achieve a dissociation measure. Results: The method proved efficient in classifying RSWA vs. not-RSWA subjects, achieving an overall accuracy, sensitivity and precision of 87%, 93% and 87.5%, respectively. On its part, the Dissociation Index proved to be promising in measuring the impairment level of patients. Conclusions: The proposed method moves a step forward in the direction of automatically identifying REM sleep disorders and evaluating the impairment degree. We believe that this index may be correlated with the patients’ neurodegeneration process; this assumption will undergo a robust clinical validation process involving healthy, RSWA, RBD and PD subjects.

2008 ◽  
Vol 9 (4) ◽  
pp. 343-351 ◽  
Author(s):  
Michele Terzaghi ◽  
Elena Sinforiani ◽  
Chiara Zucchella ◽  
Elena Zambrelli ◽  
Chiara Pasotti ◽  
...  

2016 ◽  
Vol 22 ◽  
pp. S69-S72 ◽  
Author(s):  
Luigi Ferini-Strambi ◽  
Fabrizio Rinaldi ◽  
Enrico Giora ◽  
Sara Marelli ◽  
Andrea Galbiati

2009 ◽  
Vol 61 (3) ◽  
pp. 164-170 ◽  
Author(s):  
Asako Yoritaka ◽  
Hideki Ohizumi ◽  
Shigeki Tanaka ◽  
Nobutaka Hattori

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 62S-71S
Author(s):  
Josie J. Sivaraman ◽  
Scott K. Proescholdbell ◽  
David Ezzell ◽  
Meghan E. Shanahan

Objectives Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states’ data. Methods This study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose. Results Using a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%. Conclusions This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data.


Sign in / Sign up

Export Citation Format

Share Document