A deep learning-based algorithm for detection of cortical arousal during sleep

Li, Ao; Chen, Siteng; Quan, Stuart F.; Powers, Linda S.; Roveda, Janet

doi:10.1093/sleep/zsaa120

Cited by 22 publications

(25 citation statements)

References 40 publications

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“…The results of the different approaches are shown in table 1. By only using HRV, we obtained similar results on a comparable dataset from the leading approach Deep-Cad [9]. The approaches using only the ECG or features from the ECG have an AUPRC that is approximately 0.14 lower than the state-of-the-art arousal detection using all signals from the PSG.…”

Section: Resultsmentioning

confidence: 56%

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Automatic Sleep Arousal Detection Using Heart Rate From a Single-Lead Electrocardiogram

Ehrlich¹,

Bender²,

Malberg³

et al. 2022

Computing in Cardiology Conference (CinC)

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Arousals during sleep give deep insights into the pathophysiology of sleep disorders and sleep quality. Detecting arousals is a time-consuming process manually performed by a trained expert. The required measurement is performed on an inpatient basis and is uncomfortable for the patient. As arousals relate to the autonomic nervous system, they also reflect in the electrocardiogram, which is therefore a promising alternative biosignal. In this study, we developed a deep learning model for automatic detection of sleep arousals from heart rate.We developed our algorithm using 5323 recordings from the Sleep Heart Health Study. 1003 of them were held-out as test data. We derived RR intervals from the ECG and interpolated them into a 4 Hz signal. Next, we developed a convolutional neural network (CNN) for end-to-end event detection. Model output is a continuous arousal probability with a frequency of 1 Hz.The optimization resulted in a twelve-layer CNN that achieved a Cohens kappa of 0.47, an area under the precision-recall curve of 0.54 on hold-out test data.This study demonstrates the ability of machine learning to detect arousals during sleep from heart rate. As our approach uses only the heart rate, it is potentially transferable to other signals, e.g. the photoplethysmogram.

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Section: Resultsmentioning

confidence: 56%

“…They trained a feedforward network with hand-crafted features that also included manually scored sleep stages. In 2020, Li et al [9] developed DeepCAD, a machine learning algorithm using CNN and long short term memory (LSTM). They used the raw ECG signal to train their algorithm on 1547 patients from the Multi-Ethnic Study of Atherosclerosis.…”

Section: Resultsmentioning

confidence: 99%

Automatic Sleep Arousal Detection Using Heart Rate From a Single-Lead Electrocardiogram

Ehrlich¹,

Bender²,

Malberg³

et al. 2022

Computing in Cardiology Conference (CinC)

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“…8,9,12,13 In taking a more global analytic approach, we have identified a potential role for ECG-based classification of non-cardiac disease. We additionally highlight conditions for which further study may be particularly high yield, including diseases not classically associated with ECG findings but each independently supported by prior studies (e.g., type 2 diabetes, 36,37 sleep apnea, [38][39][40][41] chronic liver disease/cirrhosis, 15,42 and renal failure 43 ), as well as diseases with previously undescribed associations.…”

Section: Discussionmentioning

confidence: 72%

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

Venn

Wang

Friedman

et al. 2022

Preprint

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The electrocardiogram (ECG) is an inexpensive and widely available diagnostic tool, and therefore has great potential to facilitate disease detection in large-scale populations. Both cardiac and noncardiac diseases may alter the appearance of the ECG, though the extent to which diseases across the human phenotypic landscape can be detected on the ECG remains unclear. We developed a deep learning variational autoencoder model that encodes and reconstructs ECG waveform data within a multidimensional latent space. We then systematically evaluated whether associations between ECG encodings and a broad range of disease phenotypes could be detected using the latent space model by deriving disease vectors and projecting individual ECG encodings onto the vectors. We developed models for both 12- and single-lead ECGs, akin to those used in wearable ECG technology. We leveraged phecodes to generate disease labels using International Classification of Disease (ICD) codes for about 1,600 phenotypes in three different datasets linked to electronic health record data. We tested associations between ECG encodings and disease phenotypes using a phenome-wide association study approach in each dataset, and meta-analyzed the results. We observed that the latent space ECG model identified associations for 645 (40%) diseases tested in the 12-lead model. Associations were enriched for diseases of the circulatory (n=140, 82% of category-specific diseases), respiratory (n=53, 62%), and endocrine/metabolic (n=73, 45%) systems, with additional associations evident across the human phenome; results were similar for the single-lead models. The top ECG latent space association was with hypertension in the 12-lead ECG model, and cardiomyopathy in the single-lead ECG model (p<2.2x10-308 for each). The ECG latent space model demonstrated a greater number of associations than ECG models using standard ECG intervals alone, and generally resulted in improvements in discrimination of diseases compared to models comprising only age, sex, and race. We further demonstrate how a latent space model can be used to generate disease-specific ECG waveforms and facilitate disease profiling for individual patients.

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“…We used the pre-trained deep learning model reported in our previous study for arousal detection [ 22 ]. Specifically, the model took a 256 Hz ECG signal as input and output the sequence of arousal probabilities at a one-second resolution based on the presence or absence of an arousal previously scored in the MESA dataset.…”

Section: Methodsmentioning

confidence: 99%

Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm

et al. 2023

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Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variability (HRV) can represent pathological conditions associated with autonomic nervous system dysfunction. Previous studies showed changes in cardiac activity due to cortical arousals. However, few studies have examined the instantaneous association between cortical arousal and HRV in an ethnically diverse population. In this study, we included 1,069 subjects’ full night ECG signals from unattended polysomnography in the Multi-Ethnic Study of Atherosclerosis dataset. An automated deep learning tool was employed to annotate arousal events from ECG signals. The etiology (e.g., respiratory, or spontaneous) of each arousal event was classified through a temporal analysis. Time domain HRVs and mean heart rate were calculated on pre-, intra-, and post-arousal segments of a 25-s period for each arousal event. We observed that heart rate and HRVs increased during the arousal onsets in the intra-arousal segments, regardless of arousal etiology. Furthermore, HRVs response to cortical arousal occurrence differed according to gender and the sleep stages in which arousal occurred. The more intense HRVs variation due to arousal in females can contribute to a potentially stronger association between arousal burden and long-term mortality. The excessive abrupt sympathetic tone elevation in REM caused by arousal may provide insights on the association between sleep and sudden cardiac death.

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A deep learning-based algorithm for detection of cortical arousal during sleep

Cited by 22 publications

References 40 publications

Automatic Sleep Arousal Detection Using Heart Rate From a Single-Lead Electrocardiogram

Automatic Sleep Arousal Detection Using Heart Rate From a Single-Lead Electrocardiogram

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm

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