dc.contributor.author | Wahlstrom, J | |
dc.contributor.author | Skog, I | |
dc.contributor.author | Handel, P | |
dc.contributor.author | Khosrow-khavar, F | |
dc.contributor.author | Tavakolian, K | |
dc.contributor.author | Stein, PK | |
dc.contributor.author | Nehorai, A | |
dc.date.accessioned | 2020-07-22T11:34:22Z | |
dc.date.issued | 2017-01-09 | |
dc.description.abstract | We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and 9 [ms], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services. | en_GB |
dc.identifier.citation | Vol. 64 (10), pp. 2361 - 2372 | en_GB |
dc.identifier.doi | 10.1109/tbme.2017.2648741 | |
dc.identifier.uri | http://hdl.handle.net/10871/122073 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2017 Canadian Crown Copyright | en_GB |
dc.subject | Heart rate variability | en_GB |
dc.subject | Electrocardiography | en_GB |
dc.subject | vibrations | en_GB |
dc.subject | Estimation | en_GB |
dc.subject | Accelerometers | en_GB |
dc.subject | Hidden Markov models | en_GB |
dc.title | A Hidden Markov Model for Seismocardiography | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-07-22T11:34:22Z | |
dc.identifier.issn | 0018-9294 | |
dc.description | This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record. | en_GB |
dc.identifier.journal | IEEE Transactions on Biomedical Engineering | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2016-12-28 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2016-12-28 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-07-22T11:30:24Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-07-22T11:34:28Z | |
refterms.panel | B | en_GB |