An elastic manifold learning approach to beat-to-beat interval estimation with ballistocardiography signals

• Published in: Advanced engineering informatics Vol. 44; p. 101051
• Main Authors: Shen, Gang, Ding, Ruidong, Yang, Mingqi, Han, Dan, Zhang, Biyong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2020
• Subjects: Ballistocardiogram; Elastic map; Manifold learning; Phase space; Elastic map; Ballistocardiogram; Manifold learning; Phase space
• ISSN: 1474-0346; 1873-5320
• DOI: 10.1016/j.aei.2020.101051

Continuous monitoring of heart rate variation is an important measure to diagnose cardiovascular problems and reduce related morbidity and mortality. The recent advances in wearable sensors have enabled the collection of ballistocardiographic (BCG) records over a long period without sacrificing the user’s normal daily life. However, there are multiple interferences that severely impact the BCG sampling process and thus degrade the signal quality. In this paper, we introduce a novel approach to estimating the beat-to-beat intervals by applying an unsupervised manifold learning framework in a hybrid phase space. First, we map the BCG time series into the three-dimensional space within which the desired BCG sample points are expected to form a low-dimensional manifold. This manifold is then reconstructed by its local linear property to remove the high-frequency noise; and overlapping manifold segments are projected to a low-dimensional principal subspace before aligned to mitigate the low-frequency non-stationary center shifts and amplitude variations. After we take the statistics to analyze the period indicators, the heartbeat intervals can be inferred. The proposed approach was tested with the BCG data collected from 10 subjects in different genders, ages, heights, and weights. We compare the estimates with the ground truth ECG references, and the results show that the proposed algorithm is able to provide reliable and accurate estimates for heart rates and beat-to-beat intervals, with the standard deviation of the interval estimate error of 22 ms.