Signal Quality for RR Interval Prediction on Wearable Sensors

Physiological responses are essential for health monitoring. Wearable devices are providing greater populations of people with the ability to monitor physiological signals during their day to day activities. However, wearable devices are particularly susceptible to degradation of signal quality due to noise from motion artifacts, environment, and user error. In this paper, we compare the impact of including signal quality on predictive models for RR intervals in a real world setting.

[1]  Hannu Sorvoja,et al.  Use of EMFi as a blood pressure pulse transducer , 2005, IEEE Transactions on Instrumentation and Measurement.

[2]  Ikaro Silva,et al.  Improving the quality of ECGs collected using mobile phones: The PhysioNet/Computing in Cardiology Challenge 2011 , 2011, 2011 Computing in Cardiology.

[3]  David A. Clifton,et al.  Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring , 2015, IEEE Journal of Biomedical and Health Informatics.

[4]  Joachim Behar,et al.  A Comparison of Single Channel Fetal ECG Extraction Methods , 2014, Annals of Biomedical Engineering.

[5]  J. Strackee,et al.  Relationships between short-term blood-pressure fluctuations and heart-rate variability in resting subjects I: a spectral analysis approach , 1985, Medical and Biological Engineering and Computing.

[6]  David Howard,et al.  A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.

[7]  O. Postolache,et al.  New Approach on Cardiac Autonomic Control Estimation Based on BCG Processing , 2007, 2007 Canadian Conference on Electrical and Computer Engineering.

[8]  Masa Ishijima.  Unobtrusive approaches to monitoring vital signs at home , 2007, Medical & Biological Engineering & Computing.

[9]  R. Oweis,et al.  QRS Detection and Heart Rate Variability Analysis: A Survey , 2014 .

[10]  J. Lauter,et al.  Wearable approach for continuous ECG - and activity patient-monitoring , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  Qiao Li,et al.  An open source benchmarked toolbox for cardiovascular waveform and interval analysis , 2018, Physiological measurement.

[13]  George B. Moody,et al.  A robust open-source algorithm to detect onset and duration of QRS complexes , 2003, Computers in Cardiology, 2003.

[14]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  S. Cerutti,et al.  Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. , 2015, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[16]  Meena Kumari,et al.  Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. , 2003, American journal of epidemiology.