An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device

Nicolò Pini; Ju Lynn Ong; Gizem Yilmaz; Nicholas I Y N Chee; Zhao Siting; Animesh Awasthi; Siddharth Biju; Kishan Kishan; Amiya Patanaik; William P Fifer; Maristella Lucchini

doi:10.3389/fnins.2022.974192

An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device

Front Neurosci. 2022 Oct 6:16:974192. doi: 10.3389/fnins.2022.974192. eCollection 2022.

Authors

Nicolò Pini^{1

2}, Ju Lynn Ong³, Gizem Yilmaz³, Nicholas I Y N Chee³, Zhao Siting⁴, Animesh Awasthi⁵, Siddharth Biju⁵, Kishan Kishan⁶, Amiya Patanaik⁶, William P Fifer^{1

2

7}, Maristella Lucchini^{1

2}

Affiliations

¹ Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.
² Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States.
³ Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
⁴ Electronic and Information Engineering, Imperial College London, London, United Kingdom.
⁵ Department of Biotechnology, Indian Institute of Technology, Kharagpur, India.
⁶ Neurobit Inc., New York, NY, United States.
⁷ Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States.

Abstract

Background: The rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG).

Materials and methods: This validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch.

Results: We evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment.

Conclusion: The results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.

Keywords: artificial intelligence; bio-inspired algorithms; heart rate variability (HRV); home testing; sleep monitoring algorithm; sleep monitoring devices; sleep state classification; wearable devices and sensors.