ArticleBMC research notes2020
Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance.
Article in BMC research notes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Sensing Technologies for Wearable Sleep Staging.Sensors (Basel, Switzerland) · 2021Pooled it
- A computationally efficient algorithm for wearable sleep staging in clinical populations.Scientific reports · 2023Article
- Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging.Sensors (Basel, Switzerland) · 2023Article
- Contactless Camera-Based Sleep Staging: The HealthBed Study.Bioengineering (Basel, Switzerland) · 2023Article
- Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm.International journal of environmental research and public health · 2022Article
- A deep transfer learning approach for wearable sleep stage classification with photoplethysmography.NPJ digital medicine · 2021Article
- Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep.Sensors (Basel, Switzerland) · 2021Review
- It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography.Nature and science of sleep · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveThe maturation of neural network-based techniques in combination with the availability of large sleep datasets has increased the interest in alternative methods of sleep monitoring. For unobtrusive sleep staging, the most promising algorithms are based on heart rate variability computed from inter-beat intervals (IBIs) derived from ECG-data. The practical application of these algorithms is even more promising when alternative ways of obtaining IBIs, such as wrist-worn photoplethysmography (PPG) can be used. However, studies validating sleep staging algorithms directly on PPG-based data are limited.
resultsWe applied an automatic sleep staging algorithm trained and validated on ECG-data directly on inter-beat intervals derived from a wrist-worn PPG sensor, in 389 polysomnographic recordings of patients with a variety of sleep disorders. While the algorithm reached moderate agreement with gold standard polysomnography, the performance was significantly lower when applied on PPG- versus ECG-derived heart rate variability data (kappa 0.56 versus 0.60, p < 0.001; accuracy 73.0% versus 75.9% p < 0.001). These results show that direct application of an algorithm on a different source of data may negatively affect performance. Algorithms need to be validated using each data source and re-training should be considered whenever possible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.