15 research outputs found

    Optimal length-constrained segmentation and subject-adaptive learning for real-time arrhythmia detection

    Full text link
    © 2018 Association for Computing Machinery. An algorithm of data segmentation with length constraints for each segment is presented and applied in the context of arrhythmia detection. The additivity property of the cost function for each segment yields the induction proof of the exact global optimal solution. The experiments were conducted on the MIT-BIH arrhythmia dataset with the heartbeat categories recommended by the ANSI/AAMI EC57:1998 standard. The heartbeat classification task is enhanced by an adaptive learning scheme. Incremental support vector machine is used to integrate a small number of expert-annotated samples specific to the subject into the existing classifier previously learned from the dataset. The proposed segmentation scheme obtains the sensitivity of 99.89% and the positive predictivity of 99.83%. The classification sensitivities of ventricular and supraventricular detection are significantly boosted from 85.9% and 83.5% (subject-unadaptive) to 97.7% and 93.2% (subject-adaptive), respectively. Similarly the pre-dictivities increase from 94.8% to 99.3% (ventricular), and from 67.7% to 88.0% (supraventricular) when plugging in the adaptive learning method. The signal processing framework is conducted in a simulated real-time model. As compared to the previously reported studies we achieve a competitive performance in terms of all assessment measures

    Detecting Premature Ventricular Contraction by using Regulated Discriminant Analysis with very sparse training data

    Get PDF
    Pathological electrocardiogram is often used to diagnose abnormal cardiac disorders where accurate classification of the cardiac beat types is crucial for timely diagnosis of dangerous conditions. However, accurate, timely, and precise detection of arrhythmia-types like premature ventricular contraction is very challenging as these signals are multiform, i.e. a reliable detection of these requires expert annotations. In this paper, a multivariate statistical classifier that is able to detect premature ventricular contraction beats is presented. This novel classifier can be trained with a very sparse amount of expert annotated data. To enable this, the dimensionality of the feature vector is kept very low, it uses strong designed features and a regularization mechanism. This approach is compared to other classifiers by using the MIT-BIH arrhythmia database. It has been found that the average accuracy, specificity, and sensitivity are above 96%, which is superior given the sparse amount of training data

    A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

    Full text link
    [EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.Huerta Herraiz, Á.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Quesada, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. Entropy. 22(7):1-17. https://doi.org/10.3390/e22070733S117227Lippi, G., Sanchis-Gomar, F., & Cervellin, G. (2020). Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. International Journal of Stroke, 16(2), 217-221. doi:10.1177/1747493019897870Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T., & Liu, X. (2013). Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population. The American Journal of Cardiology, 112(8), 1142-1147. doi:10.1016/j.amjcard.2013.05.063Khoo, C. W., Krishnamoorthy, S., Lim, H. S., & Lip, G. Y. H. (2012). Atrial fibrillation, arrhythmia burden and thrombogenesis. International Journal of Cardiology, 157(3), 318-323. doi:10.1016/j.ijcard.2011.06.088Warmus, P., Niedziela, N., Huć, M., Wierzbicki, K., & Adamczyk-Sowa, M. (2020). Assessment of the manifestations of atrial fibrillation in patients with acute cerebral stroke – a single-center study based on 998 patients. Neurological Research, 42(6), 471-476. doi:10.1080/01616412.2020.1746508Sposato, L. A., Cipriano, L. E., Saposnik, G., Vargas, E. R., Riccio, P. M., & Hachinski, V. (2015). Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. The Lancet Neurology, 14(4), 377-387. doi:10.1016/s1474-4422(15)70027-xSchotten, U., Dobrev, D., Platonov, P. G., Kottkamp, H., & Hindricks, G. (2016). Current controversies in determining the main mechanisms of atrial fibrillation. Journal of Internal Medicine, 279(5), 428-438. doi:10.1111/joim.12492Ferrari, R., Bertini, M., Blomstrom-Lundqvist, C., Dobrev, D., Kirchhof, P., Pappone, C., … Vicedomini, G. G. (2016). An update on atrial fibrillation in 2014: From pathophysiology to treatment. International Journal of Cardiology, 203, 22-29. doi:10.1016/j.ijcard.2015.10.089Meyre, P., Blum, S., Berger, S., Aeschbacher, S., Schoepfer, H., Briel, M., … Conen, D. (2019). Risk of Hospital Admissions in Patients With Atrial Fibrillation: A Systematic Review and Meta-analysis. Canadian Journal of Cardiology, 35(10), 1332-1343. doi:10.1016/j.cjca.2019.05.024Van Wagoner, D. R., Piccini, J. P., Albert, C. M., Anderson, M. E., Benjamin, E. J., Brundel, B., … Wehrens, X. H. T. (2015). Progress toward the prevention and treatment of atrial fibrillation: A summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9–10, 2013. Heart Rhythm, 12(1), e5-e29. doi:10.1016/j.hrthm.2014.11.011De Vos, C. B., Pisters, R., Nieuwlaat, R., Prins, M. H., Tieleman, R. G., Coelen, R.-J. S., … Crijns, H. J. G. M. (2010). Progression From Paroxysmal to Persistent Atrial Fibrillation. Journal of the American College of Cardiology, 55(8), 725-731. doi:10.1016/j.jacc.2009.11.040SCHUCHERT, A., BEHRENS, G., & MEINERTZ, T. (1999). Impact of Long-Term ECG Recording on the Detection of Paroxysmal Atrial Fibrillation in Patients After an Acute Ischemic Stroke. Pacing and Clinical Electrophysiology, 22(7), 1082-1084. doi:10.1111/j.1540-8159.1999.tb00574.xPagola, J., Juega, J., Francisco-Pascual, J., Moya, A., Sanchis, M., Bustamante, A., … Arenillas, J. F. (2018). Yield of atrial fibrillation detection with Textile Wearable Holter from the acute phase of stroke: Pilot study of Crypto-AF registry. International Journal of Cardiology, 251, 45-50. doi:10.1016/j.ijcard.2017.10.063Luong, D. T., Ha, N. T., & Thuan, N. D. (2019). Android Smart Phones Application in Tele-monitoring Electrocardiogram (ECG). American Journal of Biomedical Sciences, 15-21. doi:10.5099/aj190100015Haverkamp, H. T., Fosse, S. O., & Schuster, P. (2019). Accuracy and usability of single-lead ECG from smartphones - A clinical study. Indian Pacing and Electrophysiology Journal, 19(4), 145-149. doi:10.1016/j.ipej.2019.02.006Nagai, S., Anzai, D., & Wang, J. (2017). Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, 4(4), 138-141. doi:10.1049/htl.2016.0100Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Reviews in Biomedical Engineering, 11, 36-52. doi:10.1109/rbme.2018.2810957Aboukhalil, A., Nielsen, L., Saeed, M., Mark, R. G., & Clifford, G. D. (2008). Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. Journal of Biomedical Informatics, 41(3), 442-451. doi:10.1016/j.jbi.2008.03.003Bashar, S. K., Ding, E., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients. IEEE Access, 7, 88357-88368. doi:10.1109/access.2019.2926199Yoon, D., Lim, H. S., Jung, K., Kim, T. Y., & Lee, S. (2019). Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthcare Informatics Research, 25(3), 201. doi:10.4258/hir.2019.25.3.201Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A. E. W., & Clifford, G. D. (2015). Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters. IEEE Transactions on Biomedical Engineering, 62(9), 2125-2134. doi:10.1109/tbme.2015.2402236Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. BioMedical Engineering OnLine, 4(1). doi:10.1186/1475-925x-4-50Luo, S., & Johnston, P. (2010). A review of electrocardiogram filtering. Journal of Electrocardiology, 43(6), 486-496. doi:10.1016/j.jelectrocard.2010.07.007Martínez, A., Alcaraz, R., & Rieta, J. J. (2010). Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiological Measurement, 31(11), 1467-1485. doi:10.1088/0967-3334/31/11/005Manikandan, M. S., & Ramkumar, B. (2014). Straightforward and robust QRS detection algorithm for wearable cardiac monitor. Healthcare Technology Letters, 1(1), 40-44. doi:10.1049/htl.2013.0019Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocybernetics and Biomedical Engineering, 38(1), 54-70. doi:10.1016/j.bbe.2017.10.002Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. IEEE Journal of Biomedical and Health Informatics, 22(3), 722-732. doi:10.1109/jbhi.2017.2686436Zhang, Q., Fu, L., & Gu, L. (2019). A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG. Computational and Mathematical Methods in Medicine, 2019, 1-12. doi:10.1155/2019/7095137Xu, X., Wei, S., Ma, C., Luo, K., Zhang, L., & Liu, C. (2018). Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. Journal of Healthcare Engineering, 2018, 1-8. doi:10.1155/2018/2102918Al Rahhal, M. M., Bazi, Y., Al Zuair, M., Othman, E., & BenJdira, B. (2018). Convolutional Neural Networks for Electrocardiogram Classification. Journal of Medical and Biological Engineering, 38(6), 1014-1025. doi:10.1007/s40846-018-0389-7He, R., Wang, K., Zhao, N., Liu, Y., Yuan, Y., Li, Q., & Zhang, H. (2018). Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01206Yildirim, O., Talo, M., Ay, B., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Computers in Biology and Medicine, 113, 103387. doi:10.1016/j.compbiomed.2019.103387SINGH, S. A., & MAJUMDER, S. (2019). A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK. Journal of Mechanics in Medicine and Biology, 19(04), 1950026. doi:10.1142/s021951941950026xByeon, Y.-H., Pan, S.-B., & Kwak, K.-C. (2019). Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics. Sensors, 19(4), 935. doi:10.3390/s19040935Clifford, G., Liu, C., Moody, B., Lehman, L., Silva, I., Li, Q., … Mark, R. (2017). AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017. 2017 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2017.065-469Redmond, S. J., Xie, Y., Chang, D., Basilakis, J., & Lovell, N. H. (2012). Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiological Measurement, 33(9), 1517-1533. doi:10.1088/0967-3334/33/9/1517Li, T., & Zhou, M. (2016). ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy, 18(8), 285. doi:10.3390/e18080285Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751-5757. doi:10.1016/j.eswa.2010.02.033Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of The Royal Society Interface, 15(138), 20170821. doi:10.1098/rsif.2017.0821Mincholé, A., & Rodriguez, B. (2019). Artificial intelligence for the electrocardiogram. Nature Medicine, 25(1), 22-23. doi:10.1038/s41591-018-0306-1Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48. doi:10.1016/j.neucom.2015.09.116Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Zhao, Z., & Zhang, Y. (2018). SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00727Moeyersons, J., Smets, E., Morales, J., Villa, A., De Raedt, W., Testelmans, D., … Varon, C. (2019). Artefact detection and quality assessment of ambulatory ECG signals. Computer Methods and Programs in Biomedicine, 182, 105050. doi:10.1016/j.cmpb.2019.105050Clifford, G. D., Behar, J., Li, Q., & Rezek, I. (2012). Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiological Measurement, 33(9), 1419-1433. doi:10.1088/0967-3334/33/9/1419Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014). Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2014.2338351Hayn, D., Jammerbund, B., & Schreier, G. (2012). QRS detection based ECG quality assessment. Physiological Measurement, 33(9), 1449-1461. doi:10.1088/0967-3334/33/9/1449Casey, S., Avalos, G., & Dowling, M. (2018). Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study. Intensive and Critical Care Nursing, 48, 36-41. doi:10.1016/j.iccn.2018.05.004Nattel, S., Guasch, E., Savelieva, I., Cosio, F. G., Valverde, I., Halperin, J. L., … Camm, A. J. (2014). Early management of atrial fibrillation to prevent cardiovascular complications. European Heart Journal, 35(22), 1448-1456. doi:10.1093/eurheartj/ehu028Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B.-S., & Li, J. (2019). Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. IEEE Access, 7, 34060-34067. doi:10.1109/access.2019.2900719Petrėnas, A., Marozas, V., & Sörnmo, L. (2015). Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Computers in Biology and Medicine, 65, 184-191. doi:10.1016/j.compbiomed.2015.01.01

    Unsupervised ensembling of multiple software sensors: a new approach for electrocardiogram-derived respiration using one or two channels

    Full text link
    While several electrocardiogram-derived respiratory (EDR) algorithms have been proposed to extract breathing activity from a single-channel ECG signal, conclusively identifying a superior technique is challenging. We propose viewing each EDR algorithm as a {\em software sensor} that records the breathing activity from the ECG signal, and ensembling those software sensors to achieve a higher quality EDR signal. We refer to the output of the proposed ensembling algorithm as the {\em ensembled EDR}. We test the algorithm on a large scale database of 116 whole-night polysomnograms and compare the ensembled EDR signal with four respiratory signals recorded from four different hardware sensors. The proposed algorithm consistently improves upon other algorithms, and we envision its clinical value and its application in future healthcare

    One-class classifiers based on entropic spanning graphs

    Get PDF
    One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the α\alpha-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

    Disease diagnosis in smart healthcare: Innovation, technologies and applications

    Get PDF
    To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

    Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

    Get PDF
    In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy

    Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

    Get PDF
    In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidenceThis research was funded by the Ministry of Science, Innovation and Universities of Spain, and the European Regional Development Fund of the European Commission, Grant Nos. RTI2018-095324-B-I00, RTI2018-097122-A-I00, and RTI2018-099646-B-I00S
    corecore