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A transformation based on the first difference of the digitized electrocardiogram (ECG) signal is developed for the detection and delineation of QRS complexes. The method for classifying the abnormal complexes from the normal ones is based on the concepts of minimum phase and signal length. The parameters of a linear discriminant function obtained from a training feature vector set are used to classify the complexes. Results of application of the scheme to ECG of two arrhythmia patients are presented.", "publicationTitle": "IEEE Transactions on Biomedical Engineering", "publisher": "", "place": "", "date": "July 1979", "volume": "BME-26", "issue": "7", "section": "", "partNumber": "", "partTitle": "", "pages": "409-416", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TBME.1979.326420", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0018-9294", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Biomedical engineering", "type": 1 }, { "tag": "Biomedical monitoring", "type": 1 }, { "tag": "Cardiac Complexes, Premature", "type": 1 }, { "tag": "Computers", "type": 1 }, { "tag": "Fibrillation", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Mathematics", "type": 1 }, { "tag": "Models, Biological", 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"library": { "type": "group", "id": 232935, "name": "EHealth-thesis", "links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/ZPXBC37Q", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/ZPXBC37Q", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } }, "creatorSummary": "Zhang and Zhang", "parsedDate": "2005-10", "numChildren": 2 }, "data": { "key": "ZPXBC37Q", "version": 71, "itemType": "conferencePaper", "title": "ECG analysis based on PCA and Support Vector Machines", "creators": [ { "creatorType": "author", "firstName": "Hao", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "Li-Qing", "lastName": "Zhang" } ], "abstractNote": "Cardiovascular diseases is one of the main courses of death around the world. Electrocardiogram (ECG) supervising is the most important and efficient way of preventing heart attacks. Machine monitoring and analysis of ECG is becoming a major topic of the modern medical research. In this paper, we propose a system to detect cardiac arrhythmia using the ECG data form MIT-BIH database as a reference. The purpose of this paper is to develop an algorithm for recognizing and classifying normal beat, left bundle branch block beat, right bundle branch block beat and premature ventricular contraction (PVC). In order to do so, we extract more than 6000 signals from the original database, each signal representing a single and complete heart beat. We extract the principal characteristics of the signal by means of the principal component analysis (PCA) technique. Support vector machine (SVM) has a major predominance over other classification methods in complicated problems. SVM method is applied to classify the ECG data into the 4 categories of heart diseases. Base on this idea, we achieved better results in comparison with other pattern classification method from our computer simulations", "proceedingsTitle": "International Conference on Neural Networks and Brain, 2005. ICNN B '05", "conferenceName": "International Conference on Neural Networks and Brain, 2005. ICNN B '05", "publisher": "", "place": "", "date": "October 2005", "eventPlace": "", "volume": "2", "issue": "", "numberOfVolumes": "", "pages": "743-747", "series": "", "seriesNumber": "", "DOI": "10.1109/ICNNB.2005.1614733", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Biomedical monitoring", "type": 1 }, { "tag": "Cardiac arrest", "type": 1 }, { "tag": "Condition monitoring", "type": 1 }, { "tag": "Databases", "type": 1 }, { "tag": "ECG analysis", "type": 1 }, { "tag": "MIT-BIH database", "type": 1 }, { "tag": "PCA", "type": 1 }, { "tag": "Support vector machine classification", "type": 1 }, { "tag": "cardiovascular diseases", "type": 1 }, { "tag": "data mining", "type": 1 }, { "tag": "diseases", "type": 1 }, { "tag": "electrocardiogram", "type": 1 }, { "tag": "electrocardiography", "type": 1 }, { "tag": "medical signal processing", "type": 1 }, { "tag": "pattern classification method", "type": 1 }, { "tag": "premature ventricular contraction", "type": 1 }, { "tag": "principal component analysis", "type": 1 }, { "tag": "support vector machines", "type": 1 } ], "collections": [ "VK676MB9" ], "relations": {}, "dateAdded": "2014-02-21T21:45:07Z", "dateModified": "2014-02-21T21:45:07Z" } }, { "key": "M8UURZ44", "version": 71, "library": { "type": "group", "id": 232935, "name": "EHealth-thesis", "links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/M8UURZ44", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/M8UURZ44", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/232935/items/F9R7G6B9", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } } }, "data": { "key": "M8UURZ44", "version": 71, "parentItem": "F9R7G6B9", "itemType": "attachment", "linkMode": "linked_url", "title": "IEEE Xplore Abstract Record", "accessDate": "2014-02-21T21:45:04Z", "url": "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=754465&sortType%3Ddesc_p_Citation_Count%26queryText%3Decg+pvc", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2014-02-21T21:45:04Z", "dateModified": "2014-02-21T21:45:04Z" } }, { "key": "HI27AD9A", "version": 71, "library": { "type": "group", "id": 232935, "name": "EHealth-thesis", "links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/HI27AD9A", "type": "application/json" }, "alternate": { "href": 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"https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/F9R7G6B9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/F9R7G6B9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } }, "creatorSummary": "Paul et al.", "parsedDate": "1997-10", "numChildren": 2 }, "data": { "key": "F9R7G6B9", "version": 71, "itemType": "conferencePaper", "title": "Automatic detection of PVCs using autoregressive models", "creators": [ { "creatorType": "author", "firstName": "J.S.", "lastName": "Paul" }, { "creatorType": "author", "firstName": "M.R.S.", "lastName": "Reddy" }, { "creatorType": "author", "firstName": "V.J.", "lastName": "Kumar" } ], "abstractNote": "An algorithm is presented for the identification of ventricular ectopic beats (PVC) by performing a beat by beat analysis of the electrocardiogram (ECG). The discrete cosine transform (DCT) of a windowed ECG cycle is decomposed into spectra of system and excitatory functions representing action potential and excitation pattern of the heart muscle during the cardiac cycle. The autoregressive (AR) modeling of the system function provides necessary information for identification of PVCs. The partial energy spectrum derived from the DCT coefficients characterises the decay rate of DCT of the system function and is related to bandwidths of resonances in the AR spectrum. The algorithm was able to successfully identify PVCs from the recordings of MIT-BIH database. Under noisy conditions, the algorithm clearly distinguishes PVC patterns from those of normal beats up to a signal to noise ratio (SNR) of 10 dB", "proceedingsTitle": "Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1997", "conferenceName": "Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1997", "publisher": "", "place": "", "date": "October 1997", "eventPlace": "", "volume": "1", "issue": "", "numberOfVolumes": "", "pages": "68-71 vol.1", "series": "", "seriesNumber": "", "DOI": "10.1109/IEMBS.1997.754465", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithm design and analysis", "type": 1 }, { "tag": "Bandwidth", "type": 1 }, { "tag": "Databases", "type": 1 }, { "tag": "Discrete cosine transforms", "type": 1 }, { "tag": "MIT-BIH database", "type": 1 }, { "tag": "Muscles", "type": 1 }, { "tag": "Performance analysis", "type": 1 }, { "tag": "Resonance", "type": 1 }, { "tag": "action potential", "type": 1 }, { "tag": "autoregressive models", "type": 1 }, { "tag": "beat by beat analysis", "type": 1 }, { "tag": "discrete cosine transform", "type": 1 }, { "tag": "electrocardiography", "type": 1 }, { "tag": "electrodiagnostics", "type": 1 }, { "tag": "heart muscle excitation pattern", "type": 1 }, { "tag": "heart rate variability", "type": 1 }, { "tag": "medical signal detection", "type": 1 }, { "tag": "medical signal processing", "type": 1 }, { "tag": "noisy conditions", "type": 1 }, { "tag": "partial energy spectrum", "type": 1 }, { "tag": "physiological models", "type": 1 }, { "tag": "resonance bandwidth", "type": 1 }, { "tag": "signal to noise ratio", "type": 1 }, { "tag": "spectral analysis", "type": 1 }, { "tag": "ventricular ectopic beats", "type": 1 }, { "tag": "windowed ECG cycle", "type": 1 } ], "collections": [ "VK676MB9" ], "relations": {}, "dateAdded": "2014-02-21T21:45:04Z", "dateModified": "2014-02-21T21:45:04Z" } }, { "key": "QQREMTH9", "version": 71, "library": { "type": "group", "id": 232935, "name": "EHealth-thesis", "links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/QQREMTH9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/QQREMTH9", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/232935/items/C7RKI875", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } } }, "data": { "key": "QQREMTH9", "version": 71, "parentItem": "C7RKI875", "itemType": "attachment", "linkMode": "linked_url", "title": "IEEE Xplore Full Text PDF", "accessDate": "2014-02-21T21:45:01Z", "url": "http://ieeexplore.ieee.org/ielx5/4604641/4620480/04620504.pdf?tp=&arnumber=4620504&isnumber=4620480", "note": "", "contentType": "application/pdf", "charset": "", "tags": [], "relations": {}, "dateAdded": "2014-02-21T21:45:01Z", "dateModified": "2014-02-21T21:45:01Z" } }, { "key": "C7RKI875", "version": 71, "library": { "type": "group", "id": 232935, "name": "EHealth-thesis", "links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/C7RKI875", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/C7RKI875", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } }, "creatorSummary": "Gharaviri et al.", "parsedDate": "2008-07", "numChildren": 2 }, "data": { "key": "C7RKI875", "version": 71, "itemType": "conferencePaper", "title": "Comparison of neural network, ANFIS, and SVM classifiers for PVC arrhythmia detection", "creators": [ { "creatorType": "author", "firstName": "A.", "lastName": "Gharaviri" }, { "creatorType": "author", "firstName": "F.", "lastName": "Dehghan" }, { "creatorType": "author", "firstName": "M.", "lastName": "Teshnelab" }, { "creatorType": "author", "firstName": "H.A.", "lastName": "Moghaddam" } ], "abstractNote": "Premature ventricular contraction (PVC) beats are of great importance in evaluating and predicting life threatening ventricular arrhythmias. The aim of this study is to improve the diagnosis level of detection of PVC arrhythmia from ECG signals. This improvement is based on an appropriate choice of features for the selected task. We extracted fourteen features including, temporal, morphological features from MIT/BIH ECG signals database and then applying LDA algorithm, we reduced them into nine features. Finally we use a Neural Network, an ANFIS, and a SVM as classifiers. Satisfactory result obtained with accuracy rates of 99.8% for Neural Network classifier, 94.8673% for ANFIS classifier, and 97.57 for SVM classifier.", "proceedingsTitle": "2008 International Conference on Machine Learning and Cybernetics", "conferenceName": "2008 International Conference on Machine Learning and Cybernetics", "publisher": "", "place": "", "date": "July 2008", "eventPlace": "", "volume": "2", "issue": "", "numberOfVolumes": "", "pages": "750-755", "series": "", "seriesNumber": "", "DOI": "10.1109/ICMLC.2008.4620504", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "ANFIS", "type": 1 }, { "tag": "ANSIS", "type": 1 }, { "tag": "Databases", "type": 1 }, { "tag": "ECG", "type": 1 }, { "tag": "ECG signals", "type": 1 }, { "tag": "Linear discriminant analysis", "type": 1 }, { "tag": 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"href": "https://api.zotero.org/groups/232935/items/WNH54WSZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/WNH54WSZ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } }, "creatorSummary": "Zhou et al.", "parsedDate": "2011-08", "numChildren": 2 }, "data": { "key": "WNH54WSZ", "version": 71, "itemType": "conferencePaper", "title": "Time-domain ECG signal analysis based on smart-phone", "creators": [ { "creatorType": "author", "firstName": "Shijie", "lastName": "Zhou" }, { "creatorType": "author", "firstName": "Zichen", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "J.", "lastName": "Gu" } ], "abstractNote": "In this paper, a time domain algorithm architecture is presented and implemented on a smart-phone for ECG signal analysis. Using the QRS detection algorithm suggested by Pan-Tompkins and the beat classification method, the heart beats are detected and classified as normal beats and premature ventricular contractions (PVCs). Subsequently, a computationally efficient method is presented to separate ventricular tachycardia (VT) and ventricular fibrillation (VF). This method utilizes Lempel and Ziv complexity analysis combined with K-means algorithm for the coarse-graining process. In addition, a new classification rule is presented to recognize VT and VF in our study. The proposed system provides fairly good performance when applied to the MIT-BIH Database. This algorithm architecture can be efficiently used on the mobile platform.", "proceedingsTitle": "2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society,EMBC", "conferenceName": "2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society,EMBC", "publisher": "", "place": "", "date": "August 2011", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "2582-2585", "series": "", "seriesNumber": "", "DOI": "10.1109/IEMBS.2011.6090713", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithm design and analysis", "type": 1 }, { "tag": "Algorithms", "type": 1 }, { "tag": "Cellular Phone", "type": 1 }, { "tag": "Classification algorithms", "type": 1 }, { "tag": "Complexity theory", "type": 1 }, { "tag": 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"Yu-Jie", "lastName": "Huang" }, { "creatorType": "author", "firstName": "Shey-Shi", "lastName": "Lu" }, { "creatorType": "author", "firstName": "Chii-Wann", "lastName": "Lin" }, { "creatorType": "author", "firstName": "Liang-Gee", "lastName": "Chen" } ], "abstractNote": "Cardiovascular disease remains the main cause of death, and great efforts are spent on the design of ECG body sensors these years. Essential components such as analog frontend and wireless transceivers have been integrated on a compact IC with micro-Watt power consumption. To provide timely warning against the fatal vascular signs, based on the Chaotic Phase Space Differential (CPSD) algorithm, on-sensor processors are implemented to detect the abnormal ECG for VF, VT and PVC. The on-sensor processing reduces 98.0% power of wireless data transmission for raw ECG signals. The application specific processor is designed to accelerate CPSD algorithm with 1.7μW power while the OpenRISC is integrated to provide the system flexibility. The architecture is realized on the FPGA platform to physically demonstrate the detection of the abnormal ECG signals in a real time.", "proceedingsTitle": "2010 IEEE Workshop on Signal Processing Systems (SIPS)", "conferenceName": "2010 IEEE Workshop on Signal Processing Systems (SIPS)", "publisher": "", "place": "", "date": "October 2010", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "42-45", "series": "", "seriesNumber": "", "DOI": "10.1109/SIPS.2010.5624762", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "CPSD algorithm", "type": 1 }, { "tag": "ECG", "type": 1 }, { "tag": "FPGA", "type": 1 }, { "tag": "OpenRISC", "type": 1 }, { "tag": "Program processors", "type": 1 }, { "tag": "Sensors", "type": 1 }, { "tag": "Signal processing algorithms", "type": 1 }, { "tag": "System-on-a-chip", "type": 1 }, { "tag": "Wireless sensor networks", "type": 1 }, { "tag": "analog frontend", "type": 1 }, { "tag": "biomedical electronics", "type": 1 }, { "tag": "biomedical telemetry", "type": 1 }, { "tag": "blood vessels", "type": 1 }, { "tag": "body sensors", "type": 1 }, { "tag": "cardiovascular disease", "type": 1 }, { "tag": "cardiovascular system", "type": 1 }, { "tag": "chaotic phase space differential algorithm", "type": 1 }, { "tag": "digital signal processing chips", "type": 1 }, { "tag": "diseases", "type": 1 }, { "tag": "electrocardiography", "type": 1 }, { "tag": "field programmable gate arrays", "type": 1 }, { "tag": "medical signal detection", "type": 1 }, { "tag": "medical signal processing", "type": 1 }, { "tag": "on-chip digital system", "type": 1 }, { "tag": "on-sensor processors", "type": 1 }, { "tag": "power 1.7 muW", "type": 1 }, { "tag": "premature ventricular contraction", "type": 1 }, { "tag": "reduced instruction set computing", "type": 1 }, { "tag": "system-on-chip", "type": 1 }, { "tag": "transceivers", "type": 1 }, { "tag": "ventricular fibrillation", "type": 1 }, { "tag": "ventricular tachycardia", "type": 1 }, { "tag": "wireless communication", "type": 1 }, { "tag": "wireless data transmission", "type": 1 }, { "tag": "wireless transceivers", "type": 1 } ], "collections": [ "VK676MB9" ], "relations": {}, "dateAdded": "2014-02-21T21:44:57Z", "dateModified": "2014-02-21T21:44:57Z" } }, { "key": "IJVSD6PH", "version": 70, "library": { "type": "group", "id": 232935, "name": "EHealth-thesis", "links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/IJVSD6PH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/IJVSD6PH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } }, "creatorSummary": "Kohler et al.", "parsedDate": "2002-01", "numChildren": 2 }, "data": { "key": "IJVSD6PH", "version": 70, "itemType": "journalArticle", "title": "The principles of software QRS detection", "creators": [ { "creatorType": "author", "firstName": "B.-U.", "lastName": "Kohler" }, { "creatorType": "author", "firstName": "C.", "lastName": "Hennig" }, { "creatorType": "author", "firstName": "R.", "lastName": "Orglmeister" } ], "abstractNote": "The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.", "publicationTitle": "IEEE Engineering in Medicine and Biology Magazine", "publisher": "", "place": "", "date": "January 2002", "volume": "21", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "42-57", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/51.993193", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0739-5175", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Adaptive filters", "type": 1 }, { "tag": "Algorithms", "type": 1 }, { "tag": "Artificial neural networks", "type": 1 }, { "tag": "Band pass filters", "type": 1 }, { "tag": "Bayes methods", "type": 1 }, { "tag": "Bayesian framework", "type": 1 }, { "tag": "Databases, Factual", "type": 1 }, { "tag": 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"links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/FHTKMVQA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/FHTKMVQA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } }, "creatorSummary": "De Chazal et al.", "parsedDate": "2004-07", "numChildren": 2 }, "data": { "key": "FHTKMVQA", "version": 69, "itemType": "journalArticle", "title": "Automatic classification of heartbeats using ECG morphology and heartbeat interval features", "creators": [ { "creatorType": "author", "firstName": "P.", "lastName": "De Chazal" }, { "creatorType": "author", "firstName": "M.", "lastName": "O'Dwyer" }, { "creatorType": "author", "firstName": "R.B.", "lastName": "Reilly" } ], "abstractNote": "A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.", "publicationTitle": "IEEE Transactions on Biomedical Engineering", "publisher": "", "place": "", "date": "July 2004", "volume": "51", "issue": "7", "section": "", "partNumber": "", "partTitle": "", "pages": "1196-1206", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TBME.2004.827359", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0018-9294", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "ANSI standards", "type": 1 }, { "tag": "Algorithms", "type": 1 }, { "tag": "Arrhythmias, Cardiac", "type": 1 }, { "tag": "Artificial Intelligence", "type": 1 }, { "tag": "Cluster Analysis", "type": 1 }, { "tag": "Diagnosis, Computer-Assisted", "type": 1 }, { "tag": "ECG morphology", "type": 1 }, { "tag": "European Union", "type": 1 }, { "tag": "False Positive Reactions", "type": 1 }, { "tag": "Fibrillation", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Linear discriminant analysis", "type": 1 }, { "tag": "MIT-BIH Arrhythmia Database", "type": 1 }, { "tag": "Medical treatment", "type": 1 }, { "tag": "Morphology", "type": 1 }, { "tag": "Pattern Recognition, Automated", "type": 1 }, { "tag": "Reproducibility of Results", "type": 1 }, { "tag": "Rhythm", "type": 1 }, { "tag": "Sensitivity and Specificity", "type": 1 }, { "tag": "Signal Processing, Computer-Assisted", "type": 1 }, { "tag": "Spatial databases", "type": 1 }, { "tag": "Supervised learning", "type": 1 }, { "tag": "United States", "type": 1 }, { "tag": "automated heartbeat classification systems", "type": 1 }, { "tag": "electrocardiogram processing", "type": 1 }, { "tag": "electrocardiography", "type": 1 }, { "tag": "heart rate", "type": 1 }, { "tag": "heart rate variability", "type": 1 }, { "tag": "heartbeat interval features", "type": 1 }, { "tag": "medical signal processing", "type": 1 }, { "tag": "nonpacemaker recordings", "type": 1 }, { "tag": "normal beat", "type": 1 }, { "tag": "signal classification", "type": 1 }, { "tag": "statistical classifier model", "type": 1 }, { "tag": "supraventricular ectopic beat", "type": 1 }, { "tag": "ventricular ectopic beat", "type": 1 } ], "collections": [ "VK676MB9" ], "relations": {}, "dateAdded": "2014-02-20T03:31:16Z", "dateModified": "2014-02-20T03:31:16Z" } }, { "key": "APGBZ4CB", "version": 68, "library": { "type": "group", "id": 232935, "name": "EHealth-thesis", "links": { "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/232935/items/APGBZ4CB", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ehealth-thesis/items/APGBZ4CB", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/232935/items/B6XZZ9Z9", "type": "application/json" } }, "meta": { "createdByUser": { "id": 1111056, "username": "fanzhang", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/fanzhang", "type": "text/html" } } } }, "data": { "key": "APGBZ4CB", "version": 68, "parentItem": "B6XZZ9Z9", "itemType": "attachment", "linkMode": "linked_url", "title": "IEEE Xplore Full Text PDF", "accessDate": "2014-02-20T03:30:23Z", "url": "http://ieeexplore.ieee.org/ielx5/10/29004/01306572.pdf?tp=&arnumber=1306572&isnumber=29004", "note": "", "contentType": "application/pdf", "charset": "", "tags": [], "relations": {}, "dateAdded": "2014-02-20T03:30:23Z", "dateModified": "2014-02-20T03:30:23Z" } } ]