OBJECTIVES: EEG arousals are associated with autonomic activations. Visual EEG arousal scoring is time consuming and suffers from low interobserver agreement. We hypothesized that information on changes in heart rate alone suffice to predict the occurrence of cortical arousal. METHODS: Two visual AASM EEG arousal scorings of 56 healthy subject nights (mean age 37.0 +/- 12.8 years, 26 male) were obtained. For each of 5 heartbeats following the onset of 3581 consensus EEG arousals and of an equal number of control conditions, differences to a moving median were calculated and used to estimate likelihood ratios (LRs) for 10 categories of heartbeat differences. Comparable to 5 consecutive diagnostic tests, these LRs were used to calculate the probability of heart rate responses being associated with cortical arousals. RESULTS: EEG and ECG arousal indexes agreed well across a wide range of decision thresholds, resulting in a receiver operating characteristic (ROC) with an area under the curve of 0.91. For the decision threshold chosen for the final analyses, a sensitivity of 68.1% and a specificity of 95.2% were obtained. ECG and EEG arousal indexes were poorly correlated (r = 0.19, P <0.001, ICC = 0.186), which could in part be attributed to 3 outliers. The Bland-Altman plot showed an unbiased estimation of EEG arousal indexes by ECG arousal indexes with a standard deviation of +/- 7.9 arousals per hour sleep. In about two-thirds of all cases, ECG arousal scoring was matched by at least one (22.2%) or by both (42.5%) of the visual scorings. Sensitivity of the algorithm increased with increasing duration of EEG arousals. The ECG algorithm was also successfully validated with 30 different nights of 10 subjects (mean age 35.3 [ 13.6 years, 5 male). CONCLUSIONS: In its current version, the ECG algorithm cannot replace visual EEG arousal scoring. Sensitivity for detecting <10-s EEG arousals needs to be improved. However, in a nonclinical population, it may be valuable to supplement visual EEG arousal scoring by this automatic, objective, reproducible, cheap, and time-saving method.
OBJECTIVES: EEG arousals are associated with autonomic activations. Visual EEG arousal scoring is time consuming and suffers from low interobserver agreement. We hypothesized that information on changes in heart rate alone suffice to predict the occurrence of cortical arousal. METHODS: Two visual AASM EEG arousal scorings of 56 healthy subject nights (mean age 37.0 +/- 12.8 years, 26 male) were obtained. For each of 5 heartbeats following the onset of 3581 consensus EEG arousals and of an equal number of control conditions, differences to a moving median were calculated and used to estimate likelihood ratios (LRs) for 10 categories of heartbeat differences. Comparable to 5 consecutive diagnostic tests, these LRs were used to calculate the probability of heart rate responses being associated with cortical arousals. RESULTS: EEG and ECG arousal indexes agreed well across a wide range of decision thresholds, resulting in a receiver operating characteristic (ROC) with an area under the curve of 0.91. For the decision threshold chosen for the final analyses, a sensitivity of 68.1% and a specificity of 95.2% were obtained. ECG and EEG arousal indexes were poorly correlated (r = 0.19, P <0.001, ICC = 0.186), which could in part be attributed to 3 outliers. The Bland-Altman plot showed an unbiased estimation of EEG arousal indexes by ECG arousal indexes with a standard deviation of +/- 7.9 arousals per hour sleep. In about two-thirds of all cases, ECG arousal scoring was matched by at least one (22.2%) or by both (42.5%) of the visual scorings. Sensitivity of the algorithm increased with increasing duration of EEG arousals. The ECG algorithm was also successfully validated with 30 different nights of 10 subjects (mean age 35.3 [ 13.6 years, 5 male). CONCLUSIONS: In its current version, the ECG algorithm cannot replace visual EEG arousal scoring. Sensitivity for detecting <10-s EEG arousals needs to be improved. However, in a nonclinical population, it may be valuable to supplement visual EEG arousal scoring by this automatic, objective, reproducible, cheap, and time-saving method.
Authors: T Hori; Y Sugita; E Koga; S Shirakawa; K Inoue; S Uchida; H Kuwahara; M Kousaka; T Kobayashi; Y Tsuji; M Terashima; K Fukuda; N Fukuda Journal: Psychiatry Clin Neurosci Date: 2001-06 Impact factor: 5.188
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