Automatic Detection of Atrial Fibrillation using MEMS accelerometer

被引：0
|
作者：
Koivisto, Tero [1 ]
Pankaala, Mikko [1 ]
Hurnanen, Tero [1 ]
Vasankari, Tuija [2 ]
Kiviniemi, Tuomas [2 ]
Saraste, Antti [2 ]
Airaksinen, Juhani [2 ]
机构：
[1] Univ Turku, Technol Res Centrer, Turku, Finland
[2] Turku Univ Hosp, Ctr Heart, Turku, Finland
来源：
2015 COMPUTING IN CARDIOLOGY CONFERENCE (CINC) | 2015年 / 42卷
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The aim of the study was to assess the applicability of seismocardiogram (SCG) for the detection of atrial fibrillation (AF) in telemonitoring applications. SCG data used in this study consists of simultaneous SCG and ECG recordings of 12 patients during both AF and sinus rhythm (after cardioversion). An SCG-based AF-detection algorithm was developed and its performance tested with the acquired clinical data. The algorithm is able to distinguish AF positive samples from samples with sinus rhythm with high accuracy.
引用
收藏
页码：829 / 832
页数：4
相关论文
共 50 条
  • [31] Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques
    Pandey, Saroj Kumar
    Kumar, Gaurav
    Shukla, Shubham
    Kumar, Ankit
    Singh, Kamred Udham
    Mahato, Shambhu
    JOURNAL OF SENSORS, 2022, 2022
  • [32] A Feasibility Study on the Automatic Detection of Atrial Fibrillation using an Unobtrusive Bed-Mounted Sensor
    Brueser, Christoph
    Zink, Matthias D. H.
    Winter, Stefan
    Schauerte, Patrick
    Leonhardt, Steffen
    2011 COMPUTING IN CARDIOLOGY, 2011, 38 : 13 - 16
  • [33] Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals
    Tateno, K
    Glass, L
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2001, 39 (06) : 664 - 671
  • [34] Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals
    K. Tateno
    L. Glass
    Medical and Biological Engineering and Computing, 2001, 39 : 664 - 671
  • [35] An End-to-End framework for automatic detection of Atrial Fibrillation using Deep Residual Learning
    Nankani, Deepankar
    Baruah, Rashmi Dutta
    PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY, 2019, : 690 - 695
  • [36] Development of integrated detection system for shock vibration by using MEMS accelerometer
    Bin Abd Wahid, Khairul Anuar
    Azid, Ishak Abdul
    Shahar, Aftanasar Md
    Sidek, Othman
    2006 IEEE INTERNATIONAL CONFERENCE ON SEMICONDUCTOR ELECTRONICS, PROCEEDINGS, 2006, : 127 - +
  • [37] Detection of Atrial Fibrillation Using a Smartphone Camera
    Maitas, Oscar
    Lee, Jinseok
    Robotis, Dionyssios
    Chon, Ki H.
    McManus, David D.
    CIRCULATION RESEARCH, 2012, 111 (04)
  • [38] ATRIAL FIBRILLATION DETECTION USING A PHOTOPLETHYSMOGRAPH WAVEFORM
    Amir, O.
    Barak-Shinar, D.
    Wolff, R.
    Amos, Y.
    Paz, H.
    Smart, F.
    Lewis, B.
    SLEEP, 2011, 34 : A234 - A234
  • [39] Atrial Fibrillation Detection using a Smart Phone
    Lee, Jinseok
    Reyes, Bersain A.
    McManus, David D.
    Mathias, Oscar
    Chon, Ki H.
    2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2012, : 1177 - 1180
  • [40] Detection of Atrial Fibrillation Episodes Using SVM
    Mohebbi, Maryam
    Ghassemian, Hassan
    2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8, 2008, : 177 - 180
12345