Algorithm for heart rate extraction in a novel wearable acoustic sensor
Open accessAbstract
Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds – S 1 and S 2 – that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S 1 and S 2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long‐term wearable vital signs monitoring.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Bone conducted sounds collected via wearables have been analysed for health applications.
“In addition to the normal airborne sound, bone conducted sounds collected via wearables have also been analysed for health application, which is generally collected via earables or sensors placed near neck.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [44]
Heart rate and breathing rate are estimated from in-ear microphones, demonstrating potential of earables for health monitoring.
“Heart rate and breathing rate are also estimated from in-ear microphones, and demonstrated the potential of earables for health monitoring [44,84].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [44]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Systolic Time Intervals in Heart Failure in Man
WEISSLER, A. M., HARRIS, W. S., & SCHOENFELD, C. D. (1968) · Circulation · doi:10.1161/01.cir.37.2.149 · Cited by 1,217 · see citing works
Heart sound segmentation algorithm based on heart sound envelogram
Liang, H. P., Lukkarinen, S., & Hartimo, I. O. (2002) · Computers in cardiology · doi:10.1109/cic.1997.647841 · Cited by 416 · see citing works
Neural network classification of homomorphic segmented heart sounds
Gupta, C. N., Palaniappan, R., Swaminathan, S., & Krishnan, S. M. (2005) · Applied Soft Computing · doi:10.1016/j.asoc.2005.06.006 · Cited by 250 · see citing works
Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System
Corbishley, P., & Rodriguez-Villegas, E. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.910679 · Cited by 221 · see citing works
Self-citationA heart sound segmentation algorithm using wavelet decomposition and reconstruction
Huiying, L., Sakari, L., & Iiro, H. (2002) · doi:10.1109/iembs.1997.757028 · Cited by 187 · see citing works
Detection of S1 and S2 Heart Sounds by High Frequency Signatures
Kumar, D., Carvalho, P., Antunes, M., et al. (2006) · doi:10.1109/iembs.2006.260735 · Cited by 107 · see citing works
Detection of the first and second heart sound using probabilistic models
Gamero, L. G., & Watrous, R. L. (2004) · doi:10.1109/iembs.2003.1280519 · Cited by 75 · see citing works
Automatic segmentation of heart sound signals using hidden markov models
Ricke, A. D., Povinelli, R. J., & Johnson, M. T. (2005) · Computers in cardiology · doi:10.1109/cic.2005.1588266 · Cited by 67 · see citing works
A New Algorithm for Detection of S1 and S2 Heart Sounds
Kumar, D., Carvalho, P., Antunes, M., et al. (2006) · doi:10.1109/icassp.2006.1660559 · Cited by 54 · see citing works
First Heart Sound Detection for Phonocardiogram Segmentation
Wang, P., Kim, Y., Ling, L. H., et al. (2005) · doi:10.1109/iembs.2005.1615733 · Cited by 52 · see citing works
A pilot study of a wearable apnoea detection device
Rodriguez–Villegas, E., Chen, G., Radcliffe, J. J., & Duncan, J. S. (2014) · BMJ Open · doi:10.1136/bmjopen-2014-005299 · Cited by 51 · see citing works
Self-citationA robust heart sound segmentation algorithm for commonly occurring heart valve diseases
Ari, S., Kumar, P., & Saha, G. (2008) · Journal of Medical Engineering & Technology · doi:10.1080/03091900601015162 · Cited by 51 · see citing works
Development of an Intelligent PDA-based Wearable Digital Phonocardiograph
Brusco, M., & Nazeran, H. (2005) · doi:10.1109/iembs.2005.1617235 · Cited by 35 · see citing works
Geddes, L. A. (2005) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2005.1384105 · Cited by 34 · see citing works
Robust heart rate measurement with phonocardiogram by on-line template extraction and matching
Chen, Y., Chen, H., Chen, T.-C., et al. (2011) · doi:10.1109/iembs.2011.6090552 · Cited by 17 · see citing works
Segmentation of S1–S2 sounds in phonocardiogram records using wavelet energies
Yamacli, M., Dokur, Z., & Ölmez, T. (2008) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2008.4717964 · Cited by 14 · see citing works
Automatic segmentation of the second cardiac sound by using wavelets and hidden Markov models
Lima, C., & Barbosa, D. (2008) · doi:10.1109/iembs.2008.4649158 · Cited by 11 · see citing works
Estimation of Respiratory Rate and Heart Rate During Treadmill Tests Using Acoustic Sensor
Popov, B., Sierra, G., Telfort, V., et al. (2005) · doi:10.1109/iembs.2005.1615829 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
In-Ear Audio Wearable: Measurement of Heart and Breathing Rates for Health and Safety Monitoring
Martin, A., & Voix, J. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2720463 · Cited by 112 · see citing works
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Chen, G., Imtiaz, S. A., Aguilar–Pelaez, E., & Rodriguez–Villegas, E.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Chen, Guangwei
- Venue
- Healthcare Technology Letters
- Published
- 2015-02
- Volume
- 2
- Issue
- 1
- Pages
- 28-33
- ISSN
- 2053-3713 (ISSN-L)
- PMC
- PMC4613720
- OpenAlex
- W2025204650
- Cited by
- 27
- References
- 18
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 5 · 2022: 1 · 2021: 4 · 2020: 1 · 2019: 5 · 2018: 4 · 2017: 5
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Healthcare Technology Letters (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- heart rate estimation, phonocardiography, respiratory monitoring, sleep monitoring, long-term monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
