Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep

TY - JOUR

T1 - Automatic, electrocardiographic-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep

AU - Olsen, Mads

AU - Schneider, Logan Douglas

AU - Cheung, Joseph

AU - Peppard, Paul E

AU - Jennum, Poul J

AU - Mignot, Emmanuel

AU - Sorensen, Helge Bjarup Dissing

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Study Objectives: The current definition of sleep arousals neglects to address the diversity of arousals and their systemic cohesion. Autonomic arousals (AA) are autonomic activations often associated with cortical arousals (CA), but they may also occur in relation to a respiratory event, a leg movement event or spontaneously, without any other physiological associations. AA should be acknowledged as essential events to understand and explore the systemic implications of arousals.Methods: We developed an automatic AA detection algorithm based on intelligent feature selection and advanced machine learning using the electrocardiogram. The model was trained and tested with respect to CA systematically scored in 258 (181 training size/77 test size) polysomnographic recordings from the Wisconsin Sleep Cohort.Results: A precision value of 0.72 and a sensitivity of 0.63 were achieved when evaluated with respect to CA. Further analysis indicated that 81% of the non-CA-associated AAs were associated with leg movement (38%) or respiratory (43%) events.Conclusions: The presented algorithm shows good performance when considering that more than 80% of the false positives (FP) found by the detection algorithm appeared in relation to either leg movement or respiratory events. This indicates that most FP constitute autonomic activations that are indistinguishable from those with cortical cohesion. The proposed algorithm provides an automatic system trained in a clinical environment, which can be utilized to analyze the systemic and clinical impacts of arousals.

AB - Study Objectives: The current definition of sleep arousals neglects to address the diversity of arousals and their systemic cohesion. Autonomic arousals (AA) are autonomic activations often associated with cortical arousals (CA), but they may also occur in relation to a respiratory event, a leg movement event or spontaneously, without any other physiological associations. AA should be acknowledged as essential events to understand and explore the systemic implications of arousals.Methods: We developed an automatic AA detection algorithm based on intelligent feature selection and advanced machine learning using the electrocardiogram. The model was trained and tested with respect to CA systematically scored in 258 (181 training size/77 test size) polysomnographic recordings from the Wisconsin Sleep Cohort.Results: A precision value of 0.72 and a sensitivity of 0.63 were achieved when evaluated with respect to CA. Further analysis indicated that 81% of the non-CA-associated AAs were associated with leg movement (38%) or respiratory (43%) events.Conclusions: The presented algorithm shows good performance when considering that more than 80% of the false positives (FP) found by the detection algorithm appeared in relation to either leg movement or respiratory events. This indicates that most FP constitute autonomic activations that are indistinguishable from those with cortical cohesion. The proposed algorithm provides an automatic system trained in a clinical environment, which can be utilized to analyze the systemic and clinical impacts of arousals.

KW - Adult

KW - Aged

KW - Algorithms

KW - Arousal/physiology

KW - Autonomic Nervous System/physiology

KW - Electrocardiography/methods

KW - Electroencephalography

KW - Female

KW - Humans

KW - Leg/physiology

KW - Longitudinal Studies

KW - Male

KW - Middle Aged

KW - Movement/physiology

KW - Polysomnography/methods

KW - Respiratory Mechanics/physiology

KW - Sleep/physiology

KW - Sleep Apnea, Obstructive/diagnosis

KW - Wisconsin/epidemiology

U2 - 10.1093/sleep/zsy006

DO - 10.1093/sleep/zsy006

M3 - Journal article

C2 - 29329416

SN - 0161-8105

VL - 41

JO - Sleep

JF - Sleep

IS - 3

M1 - zsy006

ER -