Signal Quality Estimation With Multichannel Adaptive Filtering in Intensive Care Settings

A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG), arterial blood pressure (ABP) measurements, and ECG showed that the SQI is monotonically related to signal-to-noise ratio (simulated by adding white Gaussian noise) and to subjective human quality assessment of 1361 multichannel waveform epochs. A receiver-operating-characteristic (ROC) curve analysis, with the human “bad” quality label as positive and the “good” quality label as negative, yielded areas under the ROC curve of 0.86 (PPG), 0.82 (ABP), and 0.68 (ECG).

[1]  E. Brown,et al.  A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. , 2005, American journal of physiology. Heart and circulatory physiology.

[2]  Richard C. Burgess,et al.  An arrhythmia detector and heart rate estimator for overnight polysomnography studies , 2001, IEEE Transactions on Biomedical Engineering.

[3]  Xiaopeng Zhao,et al.  Computer algorithms for evaluating the quality of ECGs in real time , 2011, 2011 Computing in Cardiology.

[4]  G D Clifford,et al.  Signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments , 2011, 2011 Computing in Cardiology.

[5]  Benjamin E Moody.  Rule-based methods for ECG quality control , 2011, 2011 Computing in Cardiology.

[6]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: A Case-Study Approach , 2001 .

[7]  Steve Warren,et al.  Analysis and detection of motion artifact in photoplethysmographic data using higher order statistics , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Kameshwar Poolla,et al.  Threshold modeling of autonomic control of heart rate variability , 2000, IEEE Transactions on Biomedical Engineering.

[9]  A. Murray,et al.  Assessing ECG signal quality on a coronary care unit , 1996, Physiological measurement.

[10]  Lenka Lhotska,et al.  Simple scoring system for ECG quality assessment on Android platform , 2011, 2011 Computing in Cardiology.

[11]  R.G. Mark,et al.  A signal abnormality index for arterial blood pressure waveforms , 2006, 2006 Computers in Cardiology.

[12]  R. G. Mark,et al.  Reduction of false arterial blood pressure alarms using signal quality assessement and relationships between the electrocardiogram and arterial blood pressure , 2004, Medical and Biological Engineering and Computing.

[13]  M. Saeed.  Multiparameter Intelligent Monitoring in Intensive Care II ( MIMIC-II ) : A public-access intensive care unit database , 2011 .

[14]  Eduardo Gil,et al.  Detection of decreases in the amplitude fluctuation of pulse photoplethysmography signal as indication of obstructive sleep apnea syndrome in children , 2008, Biomed. Signal Process. Control..

[15]  Sergio Cerutti,et al.  Advanced Methods of Biomedical Signal Processing: Cerutti/Advanced , 2011 .

[16]  R G Mark,et al.  Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter , 2008, Physiological measurement.

[17]  Anthony G. Constantinides,et al.  A novel algorithm for the adaptation of the pole of Laguerre filters , 2006, IEEE Signal Processing Letters.

[18]  John C. Sakles,et al.  The ICU book , 1991 .

[19]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[20]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[21]  Ikaro Silva.  PhysioNet 2010 Challenge: A robust multi-channel adaptive filtering approach to the estimation of physiological recordings , 2010, 2010 Computing in Cardiology.

[22]  T. Oliveira e Silva.  On the adaptation of the pole of Laguerre-lattice filters , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[23]  Steve Warren,et al.  Two-Stage Approach for Detection and Reduction of Motion Artifacts in Photoplethysmographic Data , 2010, IEEE Transactions on Biomedical Engineering.

[24]  P. Laguna,et al.  Correlation between time domain baroreflex sensitivity and Sympathetic Nerve Activity , 2010, 2010 Computing in Cardiology.

[25]  Hanoch Lev-Ari,et al.  Adaptive Laguerre-lattice filters , 1997, IEEE Trans. Signal Process..

[26]  E. Gil,et al.  Pulse Photopletismography Amplitude Decrease Detector for Sleep Apnea Evaluation in Children , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[27]  Debra Trusso Haley,et al.  Using a New Inter-rater Reliability Statistic , 2007 .

[28]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[29]  A Deshmane,et al.  False arrhythmia alarm suppression using ECG, ABP, and photoplethysmogram , 2009 .

[30]  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.

[31]  D. Bradley,et al.  The ICU book, 2nd ed , 2000 .

[32]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[33]  Dieter Hayn,et al.  ECG quality assessment for patient empowerment in mHealth applications , 2011, 2011 Computing in Cardiology.

[34]  Jaques Reifman,et al.  Algorithms to qualify respiratory data collected during the transport of trauma patients , 2006, Physiological measurement.

[35]  Gail D. Baura.  System Theory and Practical Applications of Biomedical Signals , 2002 .

[36]  Pablo Laguna,et al.  Bioelectrical Signal Processing in Cardiac and Neurological Applications , 2005 .

[37]  Qiao Li,et al.  Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator , 2009, Biomedical engineering online.

[38]  J. Y. Wang.  A new method for evaluating ECG signal quality for multi-lead arrhythmia analysis , 2002, Computers in Cardiology.

[39]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[40]  K. Gwet.  Inter-Rater Reliability: Dependency on Trait Prevalence and Marginal Homogeneity , 2002 .

[41]  C. Goodman.  Association for the Advancement of Medical Instrumentation , 1988 .

[42]  Sergio Cerutti,et al.  Advanced methods of biomedical signal processing , 2011 .

[43]  N H Lovell,et al.  Signal quality measures for pulse oximetry through waveform morphology analysis , 2011, Physiological measurement.

[44]  T. H. Kyaw,et al.  Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database* , 2011, Critical care medicine.

[45]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[46]  J. Basilakis,et al.  Biosignal quality detection: An essential feature for unsupervised telehealth applications , 2010, The 12th IEEE International Conference on e-Health Networking, Applications and Services.

[47]  George B Moody,et al.  The PhysioNet/computing in cardiology challenge 2010: Mind the gap , 2010, 2010 Computing in Cardiology.