Mobile Phone Detection of Atrial Fibrillation With Mechanocardiography
2018; Lippincott Williams & Wilkins; Volume: 137; Issue: 14 Linguagem: Inglês
10.1161/circulationaha.117.032804
ISSN1524-4539
AutoresJussi Jaakkola, Samuli Jaakkola, Olli Lahdenoja, Tero Hurnanen, Tero Koivisto, Mikko Pänkäälä, Timo Knuutila, Tuomas Kiviniemi, Tuija Vasankari, Juhani Airaksinen,
Tópico(s)Heart Rate Variability and Autonomic Control
ResumoHomeCirculationVol. 137, No. 14Mobile Phone Detection of Atrial Fibrillation With Mechanocardiography Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessLetterPDF/EPUBMobile Phone Detection of Atrial Fibrillation With MechanocardiographyThe MODE-AF Study (Mobile Phone Detection of Atrial Fibrillation) Jussi Jaakkola, MD, Samuli Jaakkola, MD, Olli Lahdenoja, DSc, Tero Hurnanen, MSc, Tero Koivisto, MSc, Mikko Pänkäälä, DSc, Timo Knuutila, PhD, Tuomas O. Kiviniemi, MD, PhD, Tuija Vasankari, RN and K.E. Juhani Airaksinen, MD, PhD Jussi JaakkolaJussi Jaakkola Heart Center, Turku University Hospital and University of Turku, Finland (J.J., S.J., T.O.K., T.V., K.E.J.A.) , Samuli JaakkolaSamuli Jaakkola Heart Center, Turku University Hospital and University of Turku, Finland (J.J., S.J., T.O.K., T.V., K.E.J.A.) , Olli LahdenojaOlli Lahdenoja Department of Future Technologies, University of Turku, Finland (O.L., T.H., T.K., M.P., T.K.) , Tero HurnanenTero Hurnanen Department of Future Technologies, University of Turku, Finland (O.L., T.H., T.K., M.P., T.K.) , Tero KoivistoTero Koivisto Department of Future Technologies, University of Turku, Finland (O.L., T.H., T.K., M.P., T.K.) , Mikko PänkääläMikko Pänkäälä Department of Future Technologies, University of Turku, Finland (O.L., T.H., T.K., M.P., T.K.) , Timo KnuutilaTimo Knuutila Department of Future Technologies, University of Turku, Finland (O.L., T.H., T.K., M.P., T.K.) , Tuomas O. KiviniemiTuomas O. Kiviniemi Heart Center, Turku University Hospital and University of Turku, Finland (J.J., S.J., T.O.K., T.V., K.E.J.A.) , Tuija VasankariTuija Vasankari Heart Center, Turku University Hospital and University of Turku, Finland (J.J., S.J., T.O.K., T.V., K.E.J.A.) and K.E. Juhani AiraksinenK.E. Juhani Airaksinen Heart Center, Turku University Hospital and University of Turku, Finland (J.J., S.J., T.O.K., T.V., K.E.J.A.) Originally published11 Mar 2018https://doi.org/10.1161/CIRCULATIONAHA.117.032804Circulation. 2018;137:1524–1527Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: April 3, 2018: Previous Version 1 Because of the frequent asymptomatic presentation of atrial fibrillation (AF), stroke is too often its first manifestation.1 For effective stroke prevention, timely diagnosis of AF is crucial. Mobile devices are becoming ubiquitous, providing significant possibilities for screening applications. In mechanocardiography, mechanical cardiac activity is recorded with accelerometers and gyroscopes, standard components of modern smartphones.2 In our previous proof-of-concept study, smartphone mechanocardiography demonstrated 94% sensitivity and 100% specificity for detecting AF among 39 subjects.2 Here, we validate smartphone mechanocardiography detection of AF against visual interpretation of telemetry electrocardiographic recordings in hospitalized patients.For the present case-control study, 150 consecutive patients in AF and 150 age- and sex-matched patients in sinus rhythm (SR) were enrolled from the cardiology and internal medicine wards of Turku University Hospital, Finland, between April and September 2017. After informed consent was obtained, a 3-minute mechanocardiography recording was acquired from each subject with a Sony Xperia smartphone placed on the sternum, and a simultaneously obtained 5-lead telemetry electrocardiography (Philips IntelliVue MX40) recording was used as the comparison method to assess rhythm and the number of supraventricular and ventricular extrasystoles. Electrocardiographic rhythm classifications were confirmed by 2 independent cardiologists, and a third cardiologist made the final decision if interpretations diverged. In addition, physical measurements were recorded, and electronic patient records were searched for the subjects' clinical history and investigations conducted during the index hospitalization. The institutional ethics review board approved the study protocol.The mechanocardiography recordings were analyzed with an algorithm developed beforehand by investigators blinded to the underlying rhythm. The data were first preprocessed by applying a band-pass filter to remove signal noise and bias. The algorithm then examined each of the 6 data axes of the signal with 5-second autocorrelation windows to find evidence of constant beat-to-beat intervals. Finally, for classification as AF or SR, the share of signal segments with regularity was determined. A visual presentation of mechanocardiography data is shown in the Figure.Download figureDownload PowerPointFigure. Visual presentation of mechanocardiography data.A, Electrocardiographic, accelerometer, and gyroscope signals are presented in sinus rhythm (top) and atrial fibrillation (bottom). The corresponding heartbeats can be located in both the mechanical and the electrocardiographic signals during sinus rhythm and atrial fibrillation. Because the different axes of the accelerometer and gyroscope signals appear to vary in quality, our algorithm takes advantage of combining the information from various axes to provide a reliable estimate of the heart rhythm. B, Mechanocardiography signal periodicity is represented visually in sinus rhythm (top), sinus rhythm converting to atrial fibrillation (middle), and atrial fibrillation (bottom). The vertical axis represents time in seconds, and the horizontal axis represents the instant period of the signal converted into beats per minute to denote heart rate. A continuous signal shape is observed during a regular heart rhythm such as sinus rhythm (top), whereas a scattered pattern is observed during an irregular rhythm such as atrial fibrillation (bottom). Middle, Sinus rhythm abruptly converts to atrial fibrillation at ≈140 seconds.The mean age of all subjects was 74.8 years (95% confidence interval [CI], 73.7–75.9), and 132 (44.0%) were female. The mechanocardiography algorithm correctly classified AF in 143 of 150 cases and SR in 144 of 150 controls. Altogether, 4 of the 6 cases in SR misclassified as AF had marked sinus arrhythmia, whereas no potential reasons for the other misclassifications could be identified. The resulting sensitivity was 95.3% (95% CI, 90.6–98.1) and the specificity was 96.0% (95% CI, 91.5–98.5). The positive and negative predictive values were 96.0% (95% CI, 91.6–98.1) and 95.4% (95% CI, 90.9–97.7), and the positive and the negative likelihood ratios were 23.8 (95% CI, 10.9–55.8) and 0.05 (95% CI, 0.02–0.10), respectively. Reducing the duration of analyzed section of recording to 60 seconds did not affect sensitivity or specificity. An unweighted κ coefficient of 0.913 (95% CI, 0.866–0.960) indicated near-perfect agreement in rhythm classification between the mechanocardiography algorithm and visual interpretation of telemetry ECG recordings.Body mass index, respiratory rate, heart rate, and supraventricular extrasystole count were not associated with false-positive rhythm classification. Compared with subjects with a true-negative result, those with a false-positive result had a higher median ventricular extrasystole count (1 [interquartile range, 0–1; maximum count 7] versus 0 [interquartile range, 0–6; maximum count 16]; P=0.011), more frequently had a history of heart failure (4 [66.7%] versus 20 [13.9%]; P=0.006), and more often had pulmonary edema in a chest x-ray (5 [100%] versus 33 [34.4%]; P=0.006). However, only 1 subject with false-positive classification had a left ventricular ejection fraction <40% (data missing on 1 subject). False-negative rhythm classification was not significantly associated with any recorded clinical characteristic.In the present study, smartphone mechanocardiography accurately discriminated AF from SR among a large, clinically relevant cohort. There is demand for self-operated rhythm screening tools because intermittent screening is required for effective detection of AF. Smartphones are becoming ubiquitous, even among the elderly and in third-world countries, thus presenting a unique opportunity for cost-effective screening of AF. AliveCor is a smartphone-mounted single-lead electrocardiographic recorder that recently demonstrated 67% sensitivity and 99% specificity for detecting AF with algorithm interpretation of rhythm in a large primary healthcare cohort.3 An irrefutable advantage of handheld electrocardiographic recorders is the option for physician interpretation of tracings,4 but additional hardware is required for recording, and single-lead ECG quality is not always sufficient for reliable AF diagnosis. Similar to mechanocardiography, AF detection with smartphone photoplethysmography requires no additional hardware. Recently, 95% sensitivity and 95% specificity were reported for AF detection with the method.5 Despite comparable accuracy, photoplethysmography has notable drawbacks not affecting mechanocardiography: Positioning a finger statically against a smartphone camera is difficult and unfeasible for elderly people, and extended recording periods of up to 5 minutes are required for reliable recordings.5 In the future, the precision of mechanocardiography should be further evaluated in a large-scale screening study.In conclusion, smartphone mechanocardiography reliably detects AF without any additional hardware and provides a new easy-to-use and accessible concept for AF screening.AcknowledgmentsThe authors thank cardiologists Tuomas Paana, Antti Ylitalo, and Juha Lund from the Turku University Hospital for their important contributions in interpreting the many electrocardiographic recordings collected during the study.Sources of FundingThis study was supported by the Academy of Finland and the Finnish Foundation for Cardiovascular Research. The funders had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.DisclosuresDr S. Jaakkola received research grants from the Finnish Foundation for Cardiovascular Research, the Clinical Research Fund of Turku University Hospital (Turku, Finland), and the Finnish Society of Cardiology. T. Koivisto and Dr Pänkäälä are shareholders of Precordior Oy. Dr Kiviniemi has given lectures for Bayer, Boehringer Ingelheim, Bristol-Myers-Squibb-Pfizer, The Medicines Company, MSD, AstraZeneca, and St. Jude Medical; received research grants from the Finnish Medical Foundation, the Finnish Foundation for Cardiovascular Research, the Clinical Research Fund of Turku University Hospital (Turku, Finland), and the Finnish Society of Cardiology and an unrestricted grant from Bristol-Myers-Squibb-Pfizer; and is a member of the advisory board for Boehringer-Ingelheim and MSD. Dr Airaksinen received research grants from the Finnish Foundation for Cardiovascular Research and the Clinical Research Fund of Turku University Hospital (Turku, Finland); has given lectures for Bayer, Cardiome, and Boehringer Ingelheim; and is a member of the advisory boards for Bayer, AstraZeneca, and Bristol-Myers-Squibb-Pfizer. The other authors report no conflicts.Footnotes*Drs J. Jaakkola and S. Jaakkola contributed equally.http://circ.ahajournals.orgData sharing: Access to study data is regulated by Finnish law. Data are available from the Turku University Hospital Institutional Data Access and Ethics Committee for researchers who meet the criteria as required by Finnish law for access to confidential data.K.E. Juhani Airaksinen, MD, PhD, Heart Center, Turku University Hospital and University of Turku, Hameentie 11, PO Box 52, FIN-20521 Turku, Finland. E-mail [email protected]References1. Jaakkola J, Mustonen P, Kiviniemi T, Hartikainen JE, Palomäki A, Hartikainen P, Nuotio I, Ylitalo A, Airaksinen KE. Stroke as the first manifestation of atrial fibrillation.PLoS One. 2016; 11:e0168010. doi: 10.1371/journal.pone.0168010.CrossrefMedlineGoogle Scholar2. Lahdenoja O, Hurnanen T, Iftikhar Z, Nieminen S, Knuutila T, Saraste A, Kiviniemi T, Vasankari T, Airaksinen J, Pankaala M, Koivisto T. Atrial fibrillation detection via accelerometer and gyroscope of a smartphone [published online ahead of print April 5, 2017].IEEE J Biomed Health Inform. doi: 10.1109/JBHI.2017.2688473. http://ieeexplore.ieee.org/document/7892837/?reload=true.Google Scholar3. Chan PH, Wong CK, Pun L, Wong YF, Wong MM, Chu DW, Siu CW. Head-to-head comparison of the AliveCor heart monitor and Microlife WatchBP Office AFIB for atrial fibrillation screening in a primary care setting.Circulation. 2017; 135:110–112. doi: 10.1161/CIRCULATIONAHA.116.024439.LinkGoogle Scholar4. Freedman B, Camm J, Calkins H, Healey JS, Rosenqvist M, Wang J, Albert CM, Anderson CS, Antoniou S, Benjamin EJ, Boriani G, Brachmann J, Brandes A, Chao TF, Conen D, Engdahl J, Fauchier L, Fitzmaurice DA, Friberg L, Gersh BJ, Gladstone DJ, Glotzer TV, Gwynne K, Hankey GJ, Harbison J, Hillis GS, Hills MT, Kamel H, Kirchhof P, Kowey PR, Krieger D, Lee VWY, Levin LÅ, Lip GYH, Lobban T, Lowres N, Mairesse GH, Martinez C, Neubeck L, Orchard J, Piccini JP, Poppe K, Potpara TS, Puererfellner H, Rienstra M, Sandhu RK, Schnabel RB, Siu CW, Steinhubl S, Svendsen JH, Svennberg E, Themistoclakis S, Tieleman RG, Turakhia MP, Tveit A, Uittenbogaart SB, Van Gelder IC, Verma A, Wachter R, Yan BP; AF-Screen Collaborators. Screening for atrial fibrillation: a report of the AF-SCREEN International Collaboration.Circulation. 2017; 135:1851–1867. doi: 10.1161/CIRCULATIONAHA.116.026693.LinkGoogle Scholar5. Krivoshei L, Weber S, Burkard T, Maseli A, Brasier N, Kühne M, Conen D, Huebner T, Seeck A, Eckstein J. Smart detection of atrial fibrillation.Europace. 2017; 19:753–757. doi: 10.1093/europace/euw125.MedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Koivisto T, Lahdenoja O, Hurnanen T, Vasankari T, Jaakkola S, Kiviniemi T and Airaksinen K (2022) Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study, Sensors, 10.3390/s22124384, 22:12, (4384) Mehrang S, Jafari Tadi M, Knuutila T, Jaakkola J, Jaakkola S, Kiviniemi T, Vasankari T, Airaksinen J, Koivisto T and Pänkäälä M (2022) End-to-end sensor fusion and classification of atrial fibrillation using deep neural networks and smartphone mechanocardiography, Physiological Measurement, 10.1088/1361-6579/ac66ba, 43:5, (055004), Online publication date: 31-May-2022. Bonini N, Vitolo M, Imberti J, Proietti M, Romiti G, Boriani G, Paaske Johnsen S, Guo Y and Lip G (2022) Mobile health technology in atrial fibrillation, Expert Review of Medical Devices, 10.1080/17434440.2022.2070005, 19:4, (327-340), Online publication date: 3-Apr-2022. Cook J, Umar M, Khalili F and Taebi A (2022) Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions, Bioengineering, 10.3390/bioengineering9040149, 9:4, (149) Subasi A, Kontio E and Jafaritadi M (2022) Deep learning approaches for the cardiovascular disease diagnosis using smartphone 5G IoT and Edge Computing for Smart Healthcare, 10.1016/B978-0-323-90548-0.00010-3, (163-193), . Katritsis D and Morady F (2022) Atrial fibrillation Clinical Cardiac Electrophysiology, 10.1016/B978-0-323-79338-4.00022-4, (223-251.e1), . Murphy A and Low C (2021) Another step (count) towards leveraging mobile health data for clinical prediction, The Lancet Digital Health, 10.1016/S2589-7500(21)00212-0, 3:11, (e687-e688), Online publication date: 1-Nov-2021. Sanders D, Wasserlauf J and Passman R (2021) Use of Smartphones and Wearables for Arrhythmia Monitoring, Cardiac Electrophysiology Clinics, 10.1016/j.ccep.2021.04.004, 13:3, (509-522), Online publication date: 1-Sep-2021. Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen L, Couderc J, Cronin E, Estep J, Grieten L, Lane D, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov P, Ribeiro A, Rich R, Russo A, Slotwiner D, Steinberg J and Svennberg E (2021) 2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals, European Heart Journal - Digital Health, 10.1093/ehjdh/ztab001, 2:1, (7-48), Online publication date: 4-May-2021. Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen L, Couderc J, Cronin E, Estep J, Grieten L, Lane D, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov P, Ribeiro A, Rich R, Russo A, Slotwiner D, Steinberg J and Svennberg E (2021) 2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals, Journal of Arrhythmia, 10.1002/joa3.12461, 37:2, (271-319), Online publication date: 1-Apr-2021. Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen L, Couderc J, Cronin E, Estep J, Grieten L, Lane D, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov P, Ribeiro A, Rich R, Russo A, Slotwiner D, Steinberg J and Svennberg E (2021) 2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals, Annals of Noninvasive Electrocardiology, 10.1111/anec.12795, 26:2, Online publication date: 1-Mar-2021. Iodice F, Romoli M, Giometto B, Clerico M, Tedeschi G, Bonavita S, Leocani L and Lavorgna L (2021) Stroke and digital technology: a wake-up call from COVID-19 pandemic, Neurological Sciences, 10.1007/s10072-020-04993-3, 42:3, (805-809), Online publication date: 1-Mar-2021. Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen L, Couderc J, Cronin E, Estep J, Grieten L, Lane D, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov P, Ribeiro A, Rich R, Russo A, Slotwiner D, Steinberg J and Svennberg E (2021) 2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society, Circulation: Arrhythmia and Electrophysiology, 14:2, Online publication date: 1-Feb-2021. Krittanawong C, Rogers A, Johnson K, Wang Z, Turakhia M, Halperin J and Narayan S (2020) Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management, Nature Reviews Cardiology, 10.1038/s41569-020-00445-9, 18:2, (75-91), Online publication date: 1-Feb-2021. Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen L, Couderc J, Cronin E, Estep J, Grieten L, Lane D, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov P, Ribeiro A, Rich R, Russo A, Slotwiner D, Steinberg J and Svennberg E (2021) 2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals, Cardiovascular Digital Health Journal, 10.1016/j.cvdhj.2020.11.004, 2:1, (4-54), Online publication date: 1-Feb-2021. Lopez Perales C, Van Spall H, Maeda S, Jimenez A, Laţcu D, Milman A, Kirakoya-Samadoulougou F, Mamas M, Muser D and Casado Arroyo R (2020) Mobile health applications for the detection of atrial fibrillation: a systematic review, EP Europace, 10.1093/europace/euaa139, 23:1, (11-28), Online publication date: 27-Jan-2021. Hossein A, Rabineau J, Gorlier D, Del Rio J, van de Borne P, Migeotte P and Nonclercq A (2021) Kinocardiography Derived from Ballistocardiography and Seismocardiography Shows High Repeatability in Healthy Subjects, Sensors, 10.3390/s21030815, 21:3, (815) Relander A, Hellman T, Vasankari T, Nuotio I, Airaksinen J and Kiviniemi T (2021) Advanced interatrial block predicts ineffective cardioversion of atrial fibrillation: a FinCV2 cohort study, Annals of Medicine, 10.1080/07853890.2021.1930139, 53:1, (722-729), Online publication date: 1-Jan-2021. Prasitlumkum N, Cheungpasitporn W, Chokesuwattanaskul A, Thangjui S, Thongprayoon C, Bathini T, Vallabhajosyula S, Kanitsoraphan C, Leesutipornchai T and Chokesuwattanaskul R (2021) Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation: A systematic review and meta-analysis, Archives of Cardiovascular Diseases, 10.1016/j.acvd.2020.05.015, 114:1, (4-16), Online publication date: 1-Jan-2021. Hellman T, Hakamäki M, Lankinen R, Koivuviita N, Pärkkä J, Kallio P, Kiviniemi T, Airaksinen K, Järvisalo M and Metsärinne K (2020) Interatrial block, P terminal force or fragmented QRS do not predict new-onset atrial fibrillation in patients with severe chronic kidney disease, BMC Cardiovascular Disorders, 10.1186/s12872-020-01719-3, 20:1, Online publication date: 1-Dec-2020. Ballesta-Ors J, Clua-Espuny J, Gentille-Lorente D, Lechuga-Duran I, Fernández-Saez J, Muria-Subirats E, Blasco-Mulet M, Lorman-Carbo B and Alegret J (2020) Results, barriers and enablers in atrial fibrillation case finding: barriers in opportunistic atrial fibrillation case finding—a cross-sectional study, Family Practice, 10.1093/fampra/cmaa023, 37:4, (486-492), Online publication date: 5-Sep-2020. Sieciński S, Kostka P and Tkacz E (2020) Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers, Sensors, 10.3390/s20164522, 20:16, (4522) Mehrang S, Lahdenoja O, Kaisti M, Tadi M, Hurnanen T, Airola A, Knuutila T, Jaakkola J, Jaakkola S, Vasankari T, Kiviniemi T, Airaksinen J, Koivisto T and Pankaala M Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multilabel Learning Approach, IEEE Sensors Journal, 10.1109/JSEN.2020.2981334, 20:14, (7957-7968) Siecinski S, Kostka P and Tkacz E (2020) Time Domain And Frequency Domain Heart Rate Variability Analysis on Gyrocardiograms 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, 10.1109/EMBC44109.2020.9176052, 978-1-7281-1990-8, (2630-2633) Lahdenoja O, Hurnanen T, Kaisti M, Koskinen J, Tuominen J, Vähä-Heikkilä M, Parikka L, Wiberg M, Koivisto T and Pänkäälä M (2019) Cardiac monitoring of dogs via smartphone mechanocardiography: a feasibility study, BioMedical Engineering OnLine, 10.1186/s12938-019-0667-9, 18:1, Online publication date: 1-Dec-2019. Kaisti M, Panula T, Leppänen J, Punkkinen R, Jafari Tadi M, Vasankari T, Jaakkola S, Kiviniemi T, Airaksinen J, Kostiainen P, Meriheinä U, Koivisto T and Pänkäälä M (2019) Clinical assessment of a non-invasive wearable MEMS pressure sensor array for monitoring of arterial pulse waveform, heart rate and detection of atrial fibrillation, npj Digital Medicine, 10.1038/s41746-019-0117-x, 2:1, Online publication date: 1-Dec-2019. Tirapu L, San Antonio R, Tolosana J, Roca-Luque I, Mont L and Guasch E Exercise and atrial fibrillation: how health turns harm, and how to turn it back, Minerva Cardioangiologica, 10.23736/S0026-4725.19.04998-3, 67:5 Ibáñez Criado J, Ibáñez Criado A, García-Fernández A, Brouzet T and Martínez Martínez J (2019) Selección de lo mejor del año 2018 en arritmología clínica, ablación con catéter y desfibriladores implantables, REC: CardioClinics, 10.1016/j.rccl.2019.01.001, 54, (3-9), Online publication date: 1-Mar-2019. San Antonio R, Guasch E, Tolosana J and Mont L (2018) Determining the best approach to reduce the impact of exercise-induced atrial fibrillation: prevention, screening, or symptom-based treatment?, Expert Review of Cardiovascular Therapy, 10.1080/14779072.2019.1550720, 17:1, (19-29), Online publication date: 2-Jan-2019. Kaisti M, Tadi M, Lahdenoja O, Hurnanen T, Saraste A, Pankaala M and Koivisto T Stand-Alone Heartbeat Detection in Multidimensional Mechanocardiograms, IEEE Sensors Journal, 10.1109/JSEN.2018.2874706, 19:1, (234-242) Mehrang S, Airaksinen J, Koivisto T, Pankaala M, Tadi M, Hurnanen T, Knuutila T, Lahdenoja O, Jaakkola J, Jaakkola S, Vasankari T and Kiviniemi T Reliability of Self-Applied Smartphone Mechanocardiography for Atrial Fibrillation Detection, IEEE Access, 10.1109/ACCESS.2019.2946117, 7, (146801-146812) Hochstadt A, Chorin E, Viskin S, Schwartz A, Lubman N and Rosso R (2019) Continuous heart rate monitoring for automatic detection of atrial fibrillation with novel bio-sensing technology, Journal of Electrocardiology, 10.1016/j.jelectrocard.2018.10.096, 52, (23-27), Online publication date: 1-Jan-2019. Panula T, Hurnanen T, Tuominen J, Kaisti M, Koskinen J, Pankaala M and Koivisto T (2018) A Wearable Sensor Node for Detecting Atrial Fibrillation Using Real-Time Digital Signal Processing 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 10.1109/ICECS.2018.8617931, 978-1-5386-9562-3, (681-684) Zungsontiporn N and Link M (2018) Newer technologies for detection of atrial fibrillation, BMJ, 10.1136/bmj.k3946, (k3946) Jaakkola S, Kiviniemi T and Airaksinen K (2018) Cardioversion for atrial fibrillation – how to prevent thromboembolic complications?, Annals of Medicine, 10.1080/07853890.2018.1523552, 50:7, (549-555), Online publication date: 3-Oct-2018. April 3, 2018Vol 137, Issue 14 Advertisement Article InformationMetrics © 2018 American Heart Association, Inc.https://doi.org/10.1161/CIRCULATIONAHA.117.032804PMID: 29526834 Originally publishedMarch 11, 2018 Keywordsmobile healthatrial fibrillationPDF download Advertisement SubjectsAtrial FibrillationDigital Health
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