{"title":"基于k -最近邻(KNN)算法的心电波分量描述:基于KNN的心电波描述","authors":"I. Saini, Dilbag Singh, A. Khosla","doi":"10.1109/ITNG.2013.76","DOIUrl":null,"url":null,"abstract":"Detection of the boundaries of electrocardiogram (ECG) characteristic waves with a reasonable accuracy has been a difficult task. As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been proposed for locating the waveform boundaries (the onsets and offsets of P, QRS, and T waves) in ECG signals. First, the QRS-complex of each beat is detected from the ECG signal. Next, the onset and offset of each QRS complex are located. The P wave and T wave, relative to each QRS complex along with their onset and offset points, are then identified using this algorithm. Further, QRS duration, heart rate, QT-interval, P-wave duration and PR-interval have also been computed using ECG wave fiducial points. This algorithm is tested on the ECG dataset acquired using ATRIA®6100 ECG machine in our own laboratory. The results obtained using the proposed algorithm presented for the assessment of performance, has been compared with the output of inbuilt software based detector of ATRIA machine.","PeriodicalId":320262,"journal":{"name":"2013 10th International Conference on Information Technology: New Generations","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Delineation of ECG Wave Components Using K-Nearest Neighbor (KNN) Algorithm: ECG Wave Delineation Using KNN\",\"authors\":\"I. Saini, Dilbag Singh, A. Khosla\",\"doi\":\"10.1109/ITNG.2013.76\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Detection of the boundaries of electrocardiogram (ECG) characteristic waves with a reasonable accuracy has been a difficult task. As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been proposed for locating the waveform boundaries (the onsets and offsets of P, QRS, and T waves) in ECG signals. First, the QRS-complex of each beat is detected from the ECG signal. Next, the onset and offset of each QRS complex are located. The P wave and T wave, relative to each QRS complex along with their onset and offset points, are then identified using this algorithm. Further, QRS duration, heart rate, QT-interval, P-wave duration and PR-interval have also been computed using ECG wave fiducial points. This algorithm is tested on the ECG dataset acquired using ATRIA®6100 ECG machine in our own laboratory. The results obtained using the proposed algorithm presented for the assessment of performance, has been compared with the output of inbuilt software based detector of ATRIA machine.\",\"PeriodicalId\":320262,\"journal\":{\"name\":\"2013 10th International Conference on Information Technology: New Generations\",\"volume\":\"83 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 10th International Conference on Information Technology: New Generations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNG.2013.76\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 10th International Conference on Information Technology: New Generations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNG.2013.76","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Delineation of ECG Wave Components Using K-Nearest Neighbor (KNN) Algorithm: ECG Wave Delineation Using KNN
Detection of the boundaries of electrocardiogram (ECG) characteristic waves with a reasonable accuracy has been a difficult task. As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been proposed for locating the waveform boundaries (the onsets and offsets of P, QRS, and T waves) in ECG signals. First, the QRS-complex of each beat is detected from the ECG signal. Next, the onset and offset of each QRS complex are located. The P wave and T wave, relative to each QRS complex along with their onset and offset points, are then identified using this algorithm. Further, QRS duration, heart rate, QT-interval, P-wave duration and PR-interval have also been computed using ECG wave fiducial points. This algorithm is tested on the ECG dataset acquired using ATRIA®6100 ECG machine in our own laboratory. The results obtained using the proposed algorithm presented for the assessment of performance, has been compared with the output of inbuilt software based detector of ATRIA machine.