![]() ![]() The study also suggested that artificial neural networks and decision trees could be much more routinely utilized in the context of clinical sleep search. The large size of the database (approximately 337 000 epochs for 354 patients) provided a solid basis for determining the efficacy of actigraphy in sleep scoring. The quality of sleep–wake scoring was further improved by including more wake epochs in the training phase and by employing rescoring rules to remove artifacts. The use of artificial neural networks and decision trees was able to capture potentially nonlinear classification characteristics, when compared to the previously reported linear combination methods and hence showed improved performance. The models were then validated on the remaining 20% of the epochs. ![]() ![]() Approximately 80% of all the epochs were used to train the artificial neural network and decision tree models. The selection of the most discriminant actigraphy features was carried out using Fisher’s discriminant analysis. The participants were heterogeneous and grouped into four categories: healthy term, preterm, siblings of SIDS and infants with apparent life-threatening events (apnea of infancy). In our study, we used the overnight polysomnography scored data and ankle actimeter (Alice 3) raw data for 354 infants from this data set. The original CHIME data set contains recordings of 1079 infants <1 year old. Actimeter does this by counting the frequency of all your computer interactions or events: mouse clicks, keystrokes, scrolls and mouse moves. The study employed previously recorded longitudinal physiological infant data set from the Collaborative Home Infant Monitoring Evaluation (CHIME) study conducted between 19 at five clinical sites around the USA. Actimeter is an application that tracks your computer activity and shows it on a traditional, easy-to-read meter. The aim of this study was to investigate two new scoring algorithms employing artificial neural networks and decision trees for distinguishing sleep and wake states in infants using actigraphy and to validate and compare the performance of the proposed algorithms with known actigraphy scoring algorithms. ![]()
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