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

Olsen, Mads; Schneider, Logan; Cheung, Joseph; Peppard, Paul E.; Jennum, Poul; Mignot, Emmanuel; Sørensen, Helge Bjarup Dissing

doi:10.1093/sleep/zsy006

Cited by 20 publications

(25 citation statements)

References 25 publications

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“…Further exploration in future studies is required to improve performance of subject-independent model for marking arousal and non-arousal regions (segmentation).  Using the sleep stages as input features was considered in a previous study [2]. We initially used the sleep annotation in the training set as input features to the classifier.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals

Parvaneh¹,

Rubin

Samadani

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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The visual scoring of arousals during sleep routinely conducted by sleep experts is a challenging task warranting an automatic approach. This paper presents an algorithm for automatic detection of arousals during sleep. Using the Physionet/CinC Challenge dataset, an 80-20% subject-level split was performed to create in-house training and test sets, respectively. The data for each subject in the training set was split to 30-second epochs with no overlap. A total of 428 features from EEG, EMG, EOG, airflow, and SaO2 in each epoch were extracted and used for creating subject-specific models based on an ensemble of bagged classification trees, resulting in 943 models. For marking arousal and non-arousal regions in the test set, the data in the test set was split to 30-second epochs with 50% overlaps. The average of arousal probabilities from different patient-specific models was assigned to each 30-second epoch and then a sample-wise probability vector with the same length as test data was created for model evaluation. Using the PhysioNet/CinC Challenge 2018 scoring criteria, AUPRCs of 0.25 and 0.21 were achieved for the in-house test and blind test sets, respectively.

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Section: Resultsmentioning

confidence: 99%

“…Arousals during sleep can cause awakening or sleep stage shifts [1]. Arousals are naturally occurring micro events [2], but they can become pathological when the frequency of occurrence increases beyond the normal limit [1,2]. Arousals are found to be associated with the pathophysiology of several sleep disorders [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals

Parvaneh¹,

Rubin

Samadani

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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show abstract

“…Based on ECG, Olsen et al [46] developed a model for automatic detection of autonomic arousals (AA) with HRV using 258 (181 training size, 70%; 77 test size, 30%) polysomnographic recordings with a variety of sleep and cardiac disorders from the Wisconsin Sleep Cohort. Discarding the unstable heart rhythm, ectopic beats and/or atrial fibrillation (AF) as preprocessing, the signals were processed in the three blocks using the CWT.…”

Section: Microarousal Detection With Feed Forward Neural Network (Ffnns)mentioning

confidence: 99%

“…In previous works, the data sets were generally divided into a training set and a test set in various proportions (for example, 1:1 [66], 3:1 [84], or 7:3 [46]), or divided into a training set, a verification set, and a test set in various proportions (such as 8:1:1 [93]).…”

Section: Automated Detection Of Capmentioning

confidence: 99%

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Qian

Qiu

et al. 2021

Brain Sciences

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Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

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“…Many research efforts have been made in developing computational methods for automatic arousal detection based on polysomnographic recordings 17–21 . These methods mainly focus on 30-second epochs, and extract statistical features in the time and frequency domains through Fourier transform or in-house feature engineering.…”

Section: Mainmentioning

confidence: 99%

DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

Guan

2019

Preprint

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Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders. High-quality annotation of polysomnographic recordings is crucial for the diagnosis of sleep arousal disorders. Currently, sleep arousals are mainly annotated by human experts through looking at millions of data points manually, which requires considerable time and effort. Here we present a deep learning approach, DeepSleep, which ranked first in the 2018 PhysioNet Challenge for automatically segmenting sleep arousal regions based on polysomnographic recordings. DeepSleep features accurate (area under receiver operating characteristic curve of 0.93), high-resolution (5-millisecond resolution), and fast (10 seconds per sleep record) delineation of sleep arousals.

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

Cited by 20 publications

References 25 publications

Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals

Automatic Detection of Arousals During Sleep Using Multiple Physiological Signals

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

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