Summary: Arousals occur from all sleep stages and can be identified as abrupt electroencephalogram (EEG) and electromyogram (EMG) changes. Manual scoring of arousals is time consuming with low interscore agreement. The aim of this study was to design an arousal detection algorithm capable of detecting arousals from non–rapid eye movement (REM) and REM sleep, independent of the subject's age and disease. The proposed algorithm uses features from EEG, EMG, and the manual sleep stage scoring as input to a feed-forward artificial neural network (ANN). The performance of the algorithm has been assessed using polysomnographic (PSG) recordings from a total of 24 subjects. Eight of the subjects were diagnosed with Parkinson disease (PD) and the rest (16) were healthy adults in various ages. The performance of the algorithm was validated in 3 settings: testing on the 8 patients with PD using the leave-one-out method, testing on the 16 healthy adults using the leave-one-out method, and finally testing on all 24 subjects using a 4-fold crossvalidation. For these 3 validations, the sensitivities were 89.8%, 90.3%, and 89.4%, and the positive predictive values (PPVs) were 88.8%, 89.4%, and 86.1%. These results are high compared with those of previously presented arousal detection algorithms and especially compared with the high interscore variability of manual scorings.