Robustness of electrocardiogram signal quality indices

Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQIp), skewness (SQIskew), signal-to-noise ratio (SQIsnr), higher order statistics SQI (SQIhos) and peakedness of kurtosis (SQIkur). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.

[1]  Aleksandr Ometov,et al.  A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges , 2021, Comput. Networks.

[2]  Nigel H. Lovell,et al.  Robust PPG motion artifact detection using a 1-D convolution neural network , 2020, Comput. Methods Programs Biomed..

[3]  Vijay Kumar Sharma,et al.  Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis , 2020, Healthcare technology letters.

[4]  Enzo Pasquale Scilingo,et al.  A tool for the real-time evaluation of ECG signal quality and activity: Application to submaximal treadmill test in horses , 2020, Biomed. Signal Process. Control..

[5]  Barry K. Gilbert,et al.  Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions , 2020, IEEE Transactions on Biomedical Engineering.

[6]  Meiyan Huang,et al.  Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images , 2019, Comput. Math. Methods Medicine.

[7]  Qifei Zhang,et al.  A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG , 2019, Comput. Math. Methods Medicine.

[8]  Yue Liao,et al.  The Future of Wearable Technologies and Remote Monitoring in Health Care. , 2019, American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting.

[9]  Toshiyo Tamura,et al.  A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement , 2019, Sensors.

[10]  Xiangyu Zhang,et al.  Signal Quality Assessment and Lightweight QRS Detection for Wearable ECG SmartVest System , 2019, IEEE Internet of Things Journal.

[11]  Bor-Shyh Lin,et al.  Design of a Wearable 12-Lead Noncontact Electrocardiogram Monitoring System , 2019, Sensors.

[12]  Minglei Shu,et al.  ECG Baseline Wander Correction and Denoising Based on Sparsity , 2019, IEEE Access.

[13]  Chengyu Liu,et al.  Electrocardiogram of a Silver Nanowire Based Dry Electrode: Quantitative Comparison With the Standard Ag/AgCl Gel Electrode , 2019, IEEE Access.

[14]  Christina Orphanidou.  A review of big data applications of physiological signal data , 2019, Biophysical Reviews.

[15]  Nicholas Bambos,et al.  Physiological Waveform Imputation of Missing Data using Convolutional Autoencoders , 2018, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom).

[16]  M. Sabarimalai Manikandan,et al.  Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring , 2018, IEEE Journal of Biomedical and Health Informatics.

[17]  K. Schwarz,et al.  Nontraditional Electrocardiogram and Algorithms for Inconspicuous In-Home Monitoring: Comparative Study , 2018, JMIR mHealth and uHealth.

[18]  M. Sabarimalai Manikandan,et al.  A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment , 2018, IEEE Reviews in Biomedical Engineering.

[19]  Sabine Van Huffel,et al.  Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring , 2018, Sensors.

[20]  Young-Pil Kim,et al.  A comprehensive adaptive filter to eliminate baseline wander from ECG signals , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[21]  David A. Clifton,et al.  Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters , 2016 .

[22]  M. Sabarimalai Manikandan,et al.  A robust sparse signal decomposition framework for baseline wander removal from ECG signal , 2016, 2016 IEEE Region 10 Conference (TENCON).

[23]  Mohamed Elgendi,et al.  Optimal Signal Quality Index for Photoplethysmogram Signals , 2016, Bioengineering.

[24]  D. Strauss,et al.  Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs , 2016, Physiological measurement.

[25]  Nigel H. Lovell,et al.  QRS Detection Algorithm for Telehealth Electrocardiogram Recordings , 2016, IEEE Transactions on Biomedical Engineering.

[26]  M. Sabarimalai Manikandan,et al.  A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[27]  M. Sabarimalai Manikandan,et al.  On the use of variational mode decomposition for removal of baseline wander in ECG signals , 2016, 2016 Twenty Second National Conference on Communication (NCC).

[28]  Kuanquan Wang,et al.  Reducing false arrhythmia alarms in the ICU using novel signal quality indices assessment method , 2015, 2015 Computing in Cardiology Conference (CinC).

[29]  Tiago H. Falk,et al.  Online ECG quality assessment for context-aware wireless body area networks , 2015, 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE).

[30]  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.

[31]  Shamim Nemati,et al.  Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters , 2015, IEEE Transactions on Biomedical Engineering.

[32]  G. Clifford,et al.  A machine learning approach to multi-level ECG signal quality classification , 2014, Comput. Methods Programs Biomed..

[33]  M. J. Rooijakkers,et al.  Influence of Electrode Placement on Signal Quality for Ambulatory Pregnancy Monitoring , 2014, Comput. Math. Methods Medicine.

[34]  Qiao Li,et al.  ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction , 2013, IEEE Transactions on Biomedical Engineering.

[35]  G. Clifford,et al.  Signal quality and data fusion for false alarm reduction in the intensive care unit. , 2012, Journal of electrocardiology.

[36]  G D Clifford,et al.  Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms , 2012, Physiological measurement.

[37]  Kirk H. Shelley,et al.  Statistical approach for the detection of motion/noise artifacts in Photoplethysmogram , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[38]  Michal Huptych,et al.  Data driven approach to ECG signal quality assessment using multistep SVM classification , 2011, 2011 Computing in Cardiology.

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

[40]  Thomas Ho Chee Tat,et al.  Physionet Challenge 2011: Improving the quality of electrocardiography data collected using real time QRS-complex and T-Wave detection , 2011, 2011 Computing in Cardiology.

[41]  Lars Johannesen,et al.  Assessment of ECG quality on an Android platform , 2011, 2011 Computing in Cardiology.

[42]  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.

[43]  Mirjana Ivanovic,et al.  The Influence of Global Constraints on Similarity Measures for Time-Series Databases , 2011, Knowl. Based Syst..

[44]  Evon M. O. Abu-Taieh,et al.  Comparative Study , 2020, Definitions.

[45]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[46]  C.K. Chang,et al.  Evaluation measures for adaptive PLI filters in ECG signal processing , 2007, 2007 Computers in Cardiology.

[47]  I. Jekova,et al.  QRS Template Matching for Recognition of Ventricular Ectopic Beats , 2007, Annals of Biomedical Engineering.

[48]  Dariusz Janusek,et al.  Evaluation of the T-wave alternans detection methods: a simulation study. , 2007, Anadolu kardiyoloji dergisi : AKD = the Anatolian journal of cardiology.

[49]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[50]  C Zywietz,et al.  Signal Analysis for ECG Interpretation , 1990, Methods of Information in Medicine.

[51]  Joos Vandewalle,et al.  Two Methods for Optimal MECG Elimination and FECG Detection from Skin Electrode Signals , 1987, IEEE Transactions on Biomedical Engineering.

[52]  A. Damen,et al.  The use of the singular value decomposition in electrocardiography , 1982, Medical and Biological Engineering and Computing.

[53]  Yu Chen,et al.  ECG baseline wander correction based on mean-median filter and empirical mode decomposition. , 2014, Bio-medical materials and engineering.

[54]  O Erogul,et al.  PHYSIOLOGICAL MEASUREMENT , 2007 .

[55]  H.L. Chan,et al.  Heartbeat Detection Using Energy Thresholding and Template Match , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[56]  Leif Sörnmo,et al.  Chapter 7 – ECG Signal Processing , 2005 .

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

[58]  G. Saha,et al.  Fetal ECG extraction from single-channel maternal ECG using singular value decomposition , 1997, IEEE Transactions on Biomedical Engineering.

[59]  A. Oosterom,et al.  Spatial filtering of the fetal electrocardiogram , 1986, Journal of perinatal medicine.

[60]  P K Buchholz,et al.  Understanding the ECG. , 1972, RN.