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Sharma N, Sunkaria RK, Sharma LD. QRS complex detection using stationary wavelet transform and adaptive thresholding. Biomed Phys Eng Express 2022; 8. [PMID: 36049389 DOI: 10.1088/2057-1976/ac8e70] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/01/2022] [Indexed: 11/11/2022]
Abstract
Purpose- Electrocardiogram (ECG) signal is a record of the electrical activity of the heart and contains important clinical data about cardiovascular-related misfunctioning. The goal of the present work is to develop an improved QRS detection algorithm for the detection of heart abnormalities. Methods- In this present work stationary wavelet transforms (SWT) based method has been proposed for precise detection of QRS complex with 'sym2' mother wavelet. The stationary wavelet transform is a systematic mathematical tool to decompose the signal without downsampling using scale analysis and provides high detection of QRS complex and accurate localization of signal components. In the proposed method four level of decomposition is applied and the initial thresholding value is computed by the maximum amplitude of scale one at level four in SWT coefficients without the zero-crossing amplitude detection method. The multi-layered dynamic thresholding method has been applied to detect the true R-peak values and locate the QRS complex in the ECG signal. Results- For evaluation of results, the presented methodology is assessed on MIT-BIH, QTDB, and Noise stress test databases. In MIT-BIH, the sensitivity = 99.88%, positive predictivity = 99.93%, accuracy = 99.80% and detection error rate = 0.18% is achieved. In NSTD database, sensitivity = 97.46%, positive predictivity = 94.20%, accuracy = 91.95% and detection error rate = 8.47% and in QTDB, sensitivity = 99.95%, positive predictivity = 99.90%, accuracy = 99.71% and detection error rate = 0.16% is executed. Conclusion- In the presented proposed methodology, the computation complexity is low and exhibits a simple technique rather than an empirical approach. The proposed technique corroborates the performance for the detection of QRS complex with improved accuracy.
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Affiliation(s)
- Neenu Sharma
- E.C.E, NITJ, G.T. Road, Amritsar Bye-Pass, Jalandhar (Punjab), India - 144011, Jalandhar, Punjab, 144011, INDIA
| | - Ramesh Kumar Sunkaria
- ECE, NITJ, G.T. Road, Amritsar Bye-Pass, Jalandhar (Punjab), India - 144011, Jalandhar, Punjab, 144011, INDIA
| | - Lakhan Dev Sharma
- Electronics and Communication Engineering, VIT-AP Campus, VIT-AP University, G-30, Inavolu, Beside AP Secretariat Amaravati, Andhra Pradesh, Amaravati, 522 237, INDIA
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Rahul J, Sora M, Sharma LD. Exploratory data analysis based efficient QRS-complex detection technique with minimal computational load. Phys Eng Sci Med 2020; 43:1049-1067. [PMID: 32734450 DOI: 10.1007/s13246-020-00906-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
Detection of QRS-complex in the electrocardiogram (ECG) plays a decisive role in cardiac disorder detection. We face many challenges in terms of powerline interference, baseline drift, and abnormal varying peaks. In this work, we propose an exploratory data analysis (EDA) based efficient QRS-complex detection technique with minimal computational load. This paper includes median and moving average filter for pre-processing of the ECG. The peak of filtered ECG is enhanced to third power of the signal. The root mean square (rms) of the signal is estimated for the decision making rule. This technique adapted the new concept for isoelectric line identification and EDA based QRS-complex detection. In this paper, total 10,70,981 beats were used for validation from MIT BIH-Arrhythmia Database (MIT-BIH), Fantasia Database (FDB), European ST-T database (ESTD), a self recorded dataset (SDB), and fetal ECG database (FTDB). Overall sensitivity of 99.65 % and positive predictivity rate of 99.84 % have been achieved. The proposed technique doesn't require selection, setting, and training for QRS-complex detection. Thus, this paper presents a QRS-complex detection technique based on simple decision rules.
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Affiliation(s)
- Jagdeep Rahul
- Department of Electronics and Communication Engineering, Rajiv Gandhi University, Itanagar, India.
| | - Marpe Sora
- Department of Computer Science and Engineering, Rajiv Gandhi University, Itanagar, India
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amaravati, India
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Cherifa M, Blet A, Chambaz A, Gayat E, Resche-Rigon M, Pirracchio R. Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm. Anesth Analg 2020; 130:1157-1166. [PMID: 32287123 DOI: 10.1213/ane.0000000000004539] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
BACKGROUND Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated with adverse outcome in critically ill patients. AHE prediction is of prime interest because it could allow for treatment adjustment to predict or shorten AHE. METHODS The Super Learner (SL) algorithm is an ensemble machine-learning algorithm that we specifically trained to predict an AHE 10 minutes in advance. Potential predictors included age, sex, type of care unit, severity scores, and time-evolving characteristics such as mechanical ventilation, vasopressors, or sedation medication as well as features extracted from physiological signals: heart rate, pulse oximetry, and arterial blood pressure. The algorithm was trained on the Medical Information Mart for Intensive Care dataset (MIMIC II) database. Internal validation was based on the area under the receiver operating characteristic curve (AUROC) and the Brier score (BS). External validation was performed using an external dataset from Lariboisière hospital, Paris, France. RESULTS Among 1151 patients included, 826 (72%) patients had at least 1 AHE during their ICU stay. Using 1 single random period per patient, the SL algorithm with Haar wavelets transform preprocessing was associated with an AUROC of 0.929 (95% confidence interval [CI], 0.899-0.958) and a BS of 0.08. Using all available periods for each patient, SL with Haar wavelets transform preprocessing was associated with an AUROC of 0.890 (95% CI, 0.886-0.895) and a BS of 0.11. In the external validation cohort, the AUROC reached 0.884 (95% CI, 0.775-0.993) with 1 random period per patient and 0.889 (0.768-1) with all available periods and BSs <0.1. CONCLUSIONS The SL algorithm exhibits good performance for the prediction of an AHE 10 minutes ahead of time. It allows an efficient, robust, and rapid evaluation of the risk of hypotension that opens the way to routine use.
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Affiliation(s)
- Ményssa Cherifa
- From the Université de Paris, Paris, France.,Statistic and Epidemiologic Research Center Sorbonne Paris Cité, INSERM UMR-1153, ECSTRRA Team, Paris, France.,The ACTERREA Research Group, Université De Paris, Paris, France
| | - Alice Blet
- The ACTERREA Research Group, Université De Paris, Paris, France.,Department of Anesthesia Burn and Critical Care, University Hospitals Saint-Louis-Lariboisière, AP-HP, Paris, France.,BIOCANVAS-Cardiovascular Biomarkers, INSERM UMR-S 942, Paris, France
| | - Antoine Chambaz
- Statistic and Epidemiologic Research Center Sorbonne Paris Cité, INSERM UMR-1153, ECSTRRA Team, Paris, France.,The ACTERREA Research Group, Université De Paris, Paris, France.,Department of Applied Mathematics, MAP5, (UMR CNRS 8145), Université de Paris, Paris, France
| | - Etienne Gayat
- Department of Anesthesia Burn and Critical Care, University Hospitals Saint-Louis-Lariboisière, AP-HP, Paris, France.,BIOCANVAS-Cardiovascular Biomarkers, INSERM UMR-S 942, Paris, France
| | - Matthieu Resche-Rigon
- From the Université de Paris, Paris, France.,Statistic and Epidemiologic Research Center Sorbonne Paris Cité, INSERM UMR-1153, ECSTRRA Team, Paris, France.,The ACTERREA Research Group, Université De Paris, Paris, France
| | - Romain Pirracchio
- Statistic and Epidemiologic Research Center Sorbonne Paris Cité, INSERM UMR-1153, ECSTRRA Team, Paris, France.,The ACTERREA Research Group, Université De Paris, Paris, France.,Department of Anesthesia and Perioperative Medicine, Zuckerberg San Francisco General Hospital, University of California San Francisco, San Francisco, California
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Yakut Ö, Bolat ED. An improved QRS complex detection method having low computational load. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.02.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Errors, Omissions, and Outliers in Hourly Vital Signs Measurements in Intensive Care. Crit Care Med 2017; 44:e1021-e1030. [PMID: 27509387 DOI: 10.1097/ccm.0000000000001862] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To empirically examine the prevalence of errors, omissions, and outliers in hourly vital signs recorded in the ICU. DESIGN Retrospective analysis of vital signs measurements from a large-scale clinical data warehouse (Multiparameter Intelligent Monitoring in Intensive Care III). SETTING Data were collected from the medical, surgical, cardiac, and cardiac surgery ICUs of a tertiary medical center in the United States. PATIENTS We analyzed data from approximately 48,000 ICU stays including approximately 28 million vital signs measurements. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We used the vital sign day as our unit of measurement, defined as all the recordings from a single patient for a specific vital sign over a single 24-hour period. Approximately 30-40% of vital sign days included at least one gap of greater than 70 minutes between measurements. Between 3% and 10% of blood pressure measurements included logical inconsistencies. With the exception of pulse oximetry vital sign days, the readings in most vital sign days were normally distributed. We found that 15-38% of vital sign days contained at least one statistical outlier, of which 6-19% occurred simultaneously with outliers in other vital signs. CONCLUSIONS We found a significant number of missing, erroneous, and outlying vital signs measurements in a large ICU database. Our results provide empirical evidence of the nonrepresentativeness of hourly vital signs. Additional studies should focus on determining optimal sampling frequencies for recording vital signs in the ICU.
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Ghosh S, Feng M, Nguyen H, Li J. Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure. IEEE J Biomed Health Inform 2016; 20:1416-1426. [PMID: 26168449 PMCID: PMC5219944 DOI: 10.1109/jbhi.2015.2453478] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.
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Merah M, Abdelmalik TA, Larbi BH. R-peaks detection based on stationary wavelet transform. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 121:149-160. [PMID: 26105724 DOI: 10.1016/j.cmpb.2015.06.003] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2014] [Revised: 05/01/2015] [Accepted: 06/05/2015] [Indexed: 06/04/2023]
Abstract
Automatic detection of the QRS complexes/R-peaks in an electrocardiogram (ECG) signal is the most important step preceding any kind of ECG processing and analysis. The performance of these systems heavily relies on the accuracy of the QRS detector. The objective of present work is to drive a new robust method based on stationary wavelet transform (SWT) for R-peaks detection. The decimation of the coefficients at each level of the transformation algorithm is omitted, more samples in the coefficient sequences are available and hence a better outlier detection can be performed. Using the information of local maxima, minima and zero crossings of the fourth SWT coefficient detail, the proposed algorithm identifies the significant points for detection and delineation of the QRS complexes, as well as detection and identification of the QRS individual waves peaks of the pre-processed ECG signal. Various experimental results show that the proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, achieving excellent performance on different databases, on the MIT-BIH database (Se=99.84%, P=99.88%), on the QT Database (Se=99.94%, P=99.89%) and on MIT-BIH Noise Stress Test Database, (Se=95.30%, P=93.98%). Reliability and accuracy are close to the highest among the ones obtained in other studies. Experiments results being satisfactory, the SWT may represent a novel QRS detection tool, for a robust ECG signal analysis.
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Affiliation(s)
- M Merah
- LAMIH, UMR CNRS 8201 UVHC Laboratory of industrial and Human Automation, Mechanics anc Computer Sciences, Université de Valenciennes et du Hainaut Cambrésis, Bat Malvache, 1er étage, bureau 204, Le mont Houy, 59313 Valenciennes Cedex 9, France; Laboratoire Signaux et Images (LSI), Département Electronique, Faculté Génie Electrique Université USTO-MB, B.P 1505, El M'Naouar, Bir el Djir- Oran, Algeria.
| | - T A Abdelmalik
- LAMIH, UMR CNRS 8201 UVHC Laboratory of industrial and Human Automation, Mechanics anc Computer Sciences, Université de Valenciennes et du Hainaut Cambrésis, Bat Malvache, 1er étage, bureau 204, Le mont Houy, 59313 Valenciennes Cedex 9, France.
| | - B H Larbi
- Laboratoire Signaux et Systèmes (LSS), Université Abdelhamid Ibn Badis de Mostaganem, Route Belahcel, 27000 Mostaganem, Algeria.
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Chiang HS, Shih DH, Lin B, Shih MH. An APN model for Arrhythmic beat classification. Bioinformatics 2014; 30:1739-46. [PMID: 24535096 DOI: 10.1093/bioinformatics/btu101] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this article, classification by using associative Petri net (APN) for personalized ECG-arrhythmia-pattern identification is proposed for the first time in literature. RESULTS A rule-based classification model and reasoning algorithm of APN are created for ECG arrhythmias classification. The performance evaluation using MIT-BIH arrhythmia database shows that our approach compares well with other reported studies.
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Affiliation(s)
- Hsiu-Sen Chiang
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
| | - Dong-Her Shih
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
| | - Binshan Lin
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
| | - Ming-Hung Shih
- Department of Information Management, National Taichung University of Science and Technology, 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu City, Yunlin County, Taiwan, College of Business Administration, BE321, Louisiana State University in Shreveport, Shreveport, LA 71115, USA and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
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Sejdić E, Steele CM, Chau T. A method for removal of low frequency components associated with head movements from dual-axis swallowing accelerometry signals. PLoS One 2012; 7:e33464. [PMID: 22479402 PMCID: PMC3315562 DOI: 10.1371/journal.pone.0033464] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2011] [Accepted: 02/14/2012] [Indexed: 11/22/2022] Open
Abstract
Head movements can greatly affect swallowing accelerometry signals. In this paper, we implement a spline-based approach to remove low frequency components associated with these motions. Our approach was tested using both synthetic and real data. Synthetic signals were used to perform a comparative analysis of the spline-based approach with other similar techniques. Real data, obtained data from 408 healthy participants during various swallowing tasks, was used to analyze the processing accuracy with and without the spline-based head motions removal scheme. Specifically, we analyzed the segmentation accuracy and the effects of the scheme on statistical properties of these signals, as measured by the scaling analysis. The results of the numerical analysis showed that the spline-based technique achieves a superior performance in comparison to other existing techniques. Additionally, when applied to real data, we improved the accuracy of the segmentation process by achieving a 27% drop in the number of false negatives and a 30% drop in the number of false positives. Furthermore, the anthropometric trends in the statistical properties of these signals remained unaltered as shown by the scaling analysis, but the strength of statistical persistence was significantly reduced. These results clearly indicate that any future medical devices based on swallowing accelerometry signals should remove head motions from these signals in order to increase segmentation accuracy.
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Affiliation(s)
- Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
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Homaeinezhad M, Ghaffari A, Aghaee M, Toosi H, Rahmani R. A high-speed C++/MEX solution for long-duration arterial blood pressure characteristic locations detection. Biomed Signal Process Control 2012. [DOI: 10.1016/j.bspc.2011.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Design of a unified framework for analyzing long-duration ambulatory ECG: Application for extracting QRS geometrical features. Biomed Eng Lett 2011. [DOI: 10.1007/s13534-011-0017-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Homaeinezhad MR, Atyabi SA, Daneshvar E, Ghaffari A, Tahmasebi M. Discrete Wavelet-Aided Delineation of PCG Signal Events via Analysis of an Area Curve Length-Based Decision Statistic. ACTA ACUST UNITED AC 2010; 10:218-34. [DOI: 10.1007/s10558-010-9110-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Ghaffari A, Homaeinezhad MR, Khazraee M, Daevaeiha MM. Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric. Ann Biomed Eng 2010; 38:1497-510. [DOI: 10.1007/s10439-010-9919-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2009] [Accepted: 01/07/2010] [Indexed: 10/20/2022]
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