Automated Detection of Atrial Fibrillation Based on Time–Frequency Analysis of Seismocardiograms
In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonst...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2017-09, Vol.21 (5), p.1233-1241 |
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| creator | Hurnanen, Tero Lehtonen, Eero Tadi, Mojtaba Jafari Kuusela, Tom Kiviniemi, Tuomas Saraste, Antti Vasankari, Tuija Airaksinen, Juhani Koivisto, Tero Pankaala, Mikko |
| description | In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9 and an average true negative rate of 96.4 for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection. |
| doi_str_mv | 10.1109/JBHI.2016.2621887 |
| format | Article |
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The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9 and an average true negative rate of 96.4 for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.</description><identifier>ISSN: 2168-2194</identifier><identifier>EISSN: 2168-2208</identifier><identifier>DOI: 10.1109/JBHI.2016.2621887</identifier><identifier>PMID: 27834656</identifier><identifier>CODEN: IJBHA9</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Accelerometer ; Algorithms ; Arrhythmia ; atrial fibrillation (AFib) ; Atrial Fibrillation - diagnosis ; Automation ; Cardiac arrhythmia ; EKG ; Electrocardiography ; Entropy ; Female ; Fibrillation ; Frequency analysis ; Heart rate ; Heart Rate - physiology ; Heart rate variability ; Humans ; Informatics ; Kinetocardiography - methods ; Male ; microelectromechanical sensor (MEMS) ; Monitoring ; Morphology ; Rhythm ; Segments ; seismocardiography (SCG) ; Signal classification ; Signal Processing, Computer-Assisted ; Signal quality ; Time-frequency analysis</subject><ispartof>IEEE journal of biomedical and health informatics, 2017-09, Vol.21 (5), p.1233-1241</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-39ab03a5c37a1f26dbca7e482db484029ef476e70646afc49bd08d9eb35f5a443</citedby><cites>FETCH-LOGICAL-c349t-39ab03a5c37a1f26dbca7e482db484029ef476e70646afc49bd08d9eb35f5a443</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7736051$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7736051$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27834656$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hurnanen, Tero</creatorcontrib><creatorcontrib>Lehtonen, Eero</creatorcontrib><creatorcontrib>Tadi, Mojtaba Jafari</creatorcontrib><creatorcontrib>Kuusela, Tom</creatorcontrib><creatorcontrib>Kiviniemi, Tuomas</creatorcontrib><creatorcontrib>Saraste, Antti</creatorcontrib><creatorcontrib>Vasankari, Tuija</creatorcontrib><creatorcontrib>Airaksinen, Juhani</creatorcontrib><creatorcontrib>Koivisto, Tero</creatorcontrib><creatorcontrib>Pankaala, Mikko</creatorcontrib><title>Automated Detection of Atrial Fibrillation Based on Time–Frequency Analysis of Seismocardiograms</title><title>IEEE journal of biomedical and health informatics</title><addtitle>JBHI</addtitle><addtitle>IEEE J Biomed Health Inform</addtitle><description>In this paper, a novel method to detect atrial fibrillation (AFib) from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9 and an average true negative rate of 96.4 for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.</description><subject>Accelerometer</subject><subject>Algorithms</subject><subject>Arrhythmia</subject><subject>atrial fibrillation (AFib)</subject><subject>Atrial Fibrillation - diagnosis</subject><subject>Automation</subject><subject>Cardiac arrhythmia</subject><subject>EKG</subject><subject>Electrocardiography</subject><subject>Entropy</subject><subject>Female</subject><subject>Fibrillation</subject><subject>Frequency analysis</subject><subject>Heart rate</subject><subject>Heart Rate - physiology</subject><subject>Heart rate variability</subject><subject>Humans</subject><subject>Informatics</subject><subject>Kinetocardiography - methods</subject><subject>Male</subject><subject>microelectromechanical sensor (MEMS)</subject><subject>Monitoring</subject><subject>Morphology</subject><subject>Rhythm</subject><subject>Segments</subject><subject>seismocardiography (SCG)</subject><subject>Signal classification</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Signal quality</subject><subject>Time-frequency analysis</subject><issn>2168-2194</issn><issn>2168-2208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkblOxDAQhi0EAgQ8AEJCkWhodvEVH-VyLIeQKIDacpwJMkrWYCfFdrwDb8iT4LALBdPMaPz9o_H8CB0SPCUE67O785vbKcVETKmgRCm5gXYpEWpCKVabvzXRfAcdpPSKc6jc0mIb7VCpGBel2EXVbOhDZ3uoi0vowfU-LIrQFLM-etsWc19F37b2p31uU8Zy8eQ7-Pr4nEd4H2DhlsVsYdtl8mlUPoJPXXA21j68RNulfbTV2DbBwTrvoef51dPFzeT-4fr2YnY_cYzrfsK0rTCzpWPSkoaKunJWAle0rrjimGpouBQgseDCNo7rqsaq1lCxsikt52wPna7mvsWQ90q96XxykLdfQBiSIYppMh6nzOjJP_Q1DDF_IhlKJOclZwRniqwoF0NKERrzFn1n49IQbEYPzOiBGT0waw-y5ng9eag6qP8UvxfPwNEK8ADw9ywlE7gk7BufBYtm</recordid><startdate>201709</startdate><enddate>201709</enddate><creator>Hurnanen, Tero</creator><creator>Lehtonen, Eero</creator><creator>Tadi, Mojtaba Jafari</creator><creator>Kuusela, Tom</creator><creator>Kiviniemi, Tuomas</creator><creator>Saraste, Antti</creator><creator>Vasankari, Tuija</creator><creator>Airaksinen, Juhani</creator><creator>Koivisto, Tero</creator><creator>Pankaala, Mikko</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9 and an average true negative rate of 96.4 for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>27834656</pmid><doi>10.1109/JBHI.2016.2621887</doi><tpages>9</tpages></addata></record> |
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| subjects | Accelerometer Algorithms Arrhythmia atrial fibrillation (AFib) Atrial Fibrillation - diagnosis Automation Cardiac arrhythmia EKG Electrocardiography Entropy Female Fibrillation Frequency analysis Heart rate Heart Rate - physiology Heart rate variability Humans Informatics Kinetocardiography - methods Male microelectromechanical sensor (MEMS) Monitoring Morphology Rhythm Segments seismocardiography (SCG) Signal classification Signal Processing, Computer-Assisted Signal quality Time-frequency analysis |
| title | Automated Detection of Atrial Fibrillation Based on Time–Frequency Analysis of Seismocardiograms |
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