ECG-based heartbeat classification for arrhythmia detection: A survey

Luz, Eduardo; Schwartz, William Robson; Cámara-Chávez, Guillermo; Menotti, David

doi:10.1016/j.cmpb.2015.12.008

Cited by 733 publications

(403 citation statements)

References 140 publications

(274 reference statements)

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“…After preprocessing, segmentation divides the signal into smaller segments, which can better express the electrical activity of the heart [1]. Nowadays, the researchers can get good results from preprocessing and segmentation by some popular techniques or tools [2]. Therefore, most of the literature focuses upon the last two phases.…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Using Wavelet Packet Entropy and Random Forests

Zhou

2016

Entropy

410

200

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Abstract:The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much existing research did not consider the separation of training and testing samples from the same set of patients (so called inter-patient scheme). To cope with these issues, in this paper, we propose a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme. Specifically, we firstly decompose the ECG signals by wavelet packet decomposition (WPD), and then calculate entropy from the decomposed coefficients as representative features, and finally use RF to build an ECG classification model. To the best of our knowledge, it is the first time that WPE and RF are used to classify ECG following the AAMI recommendations and the inter-patient scheme. Extensive experiments are conducted on the publicly available MIT-BIH Arrhythmia database and influence of mother wavelets and level of decomposition for WPD, type of entropy and the number of base learners in RF on the performance are also discussed. The experimental results are superior to those by several state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification.

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Section: Introductionmentioning

confidence: 99%

ECG Classification Using Wavelet Packet Entropy and Random Forests

Zhou

2016

Entropy

410

200

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“…It is disturb and able to made a misinterpretation of ECG signal analysis. We used cubic spline filtering as developed by Badilini [19]. The study used cubic spline interpolation to reduce the baseline noise signal, because this interpolation can remove the wondering of isoelectric with no significant influent to the ECG signal.…”

Section: Signal Preprocessingmentioning

confidence: 99%

Online Kernel Amglvq for Arrhythmia Hearbeats Classification

Imah

Sulaiman

2017

kursor

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“…The important features were: Age, Gender, the ECG signal amplitude for each sample (millivolts), RR interval (inter-beat interval in milliseconds), WABP (arterial blood pressure in millivolts) and instantaneous heart rate measured at the 'instance' when the abnormal heart beat annotation occurred in ECG recording. These feature vectors were derived for 4 annotation types: V (Premature Ventricular Contraction: PVC), A (Atrial Premature Beat: APB), L (Left bundle branch block beat), R (Right bundle branch block beat) [6] [7] [29]. It has been observed that these 4 annotation types do occur in ECG recordings of healthy subjects as well and can go unnoticed without showing any symptoms [30].…”

Section: A Ecg Waveform Dataset Preparation and Analysismentioning

confidence: 99%

ECG classification and prognostic approach towards personalized healthcare

Walinjkar

Woods

2017

2017 International Conference on Social Media, Wearable and Web Analytics (Social Media)

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Abstract-A very important aspect of personalized healthcare is to continuously monitor an individual's health using wearable biomedical devices and essentially to analyse and if possible to predict potential health hazards that may prove fatal if not treated in time. The prediction aspect embedded in the system helps in avoiding delays in providing timely medical treatment, even before an individual reaches a critical condition. Despite of the availability of modern wearable health monitoring devices, the real-time analyses and prediction component seems to be missing with these devices. The research work illustrated in this paper, at an outset, focussed on constantly monitoring an individual's ECG readings using a wearable 3-lead ECG kit and more importantly focussed on performing real-time analyses to detect arrhythmia to be able to identify and predict heart risk. Also, current research shows extensive use of heart rate variability (HRV) analysis and machine learning for arrhythmia classification, which however depends on the morphology of the ECG waveforms and the sensitivity of the ECG equipment. Since a wearable 3-lead ECG kit was used, the accuracy of classification had to be dealt with at the machine learning phase, so a unique feature extraction method was developed to increase the accuracy of classification. As a case study a very widely used Arrhythmia database (MIT-BIH, Physionet) was used to develop learning, classification and prediction models. Neuralnet fitting models on the extracted features showed mean-squared error of as low as 0.0085 and regression value as high as 0.99. Current experiments show 99.4% accuracy using k-NN Classification models and show values of Cross-Entropy Error of 7.6 and misclassification error value of 1.2 on test data using scaled conjugate gradient pattern matching algorithms. Software components were developed for wearable devices that took ECG readings from a 3-Lead ECG data acquisition kit in real time, de-noised, filtered and relayed the sample readings to the telehealth analytical server. The analytical server performed the classification and prediction tasks based on the trained classification models and could raise appropriate alarms if ECG abnormalities of V (Premature Ventricular Contraction: PVC), A (Atrial Premature Beat: APB), L (Left bundle branch block beat), R (Right bundle branch block beat) type annotations in MITDB were detected. The instruments were networked using IoT (Internet of Things) devices and abnormal ECG events related to arrhythmia, from analytical server could be logged using an FHIR web service implementation, according to a SNOMED coding system and could be accessed in Electronic Health Record by the concerned medic to take appropriate and timely decisions. The system focussed on 'preventive care rather than remedial cure' which has become a major focus of all the health care and cure institutions across the globe.

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ECG-based heartbeat classification for arrhythmia detection: A survey

Cited by 733 publications

References 140 publications

ECG Classification Using Wavelet Packet Entropy and Random Forests

ECG Classification Using Wavelet Packet Entropy and Random Forests

Online Kernel Amglvq for Arrhythmia Hearbeats Classification

ECG classification and prognostic approach towards personalized healthcare

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