Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine

Rajesh, Kandala N. V. P. S.; Dhuli, Ravindra

doi:10.1016/j.compbiomed.2017.06.006

Cited by 93 publications

(32 citation statements)

References 42 publications

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“…[32] Then 1D FFT is applied to the selected DC to obtain the Fourier spectrum. Then take logarithm value of the Fourier spectrum IMF sample entropy EMD/EEMD The sample entropy is measured of regularity of a time series [33] used to quantify the complexity of heartbeat dynamics IMF variation coefficient EMD/EEMD The coefficient of variation is a statistical parameter defined as σ 2 / µ 2 . 1 [33] IMF singular values EMD/EEMD The singular value decomposition [33] IMF band power values EMD/EEMD The band power is the average power of each IMF [33] PCA components PCA PCA components for size reduction [82] Pisarenko…”

Section: Methods Description Referencementioning

confidence: 99%

“…The first approach consists of using digital low-pass, high-pass, band-pass, and notch filters to remove the noise. Many studies, such as [30][31][32][33][34][35], use a combination of low-pass and high-pass filters to remove the corresponding noise on an ECG signal. The low-pass filter cut-off frequency is in the range of 11 Hz to 45 Hz, and it mainly suppresses the high-frequency noise.…”

Section: Noise Removalmentioning

confidence: 99%

“…However, because the P and T wave detection works well with normal heartbeats, but not for many abnormal heartbeat types. Many researchers choose manual annotation, such as [71], or a fixed window, such as [32,33,38,71,72], for their heartbeat segmentation.…”

Section: Heartbeat Detection and Segmentationmentioning

confidence: 99%

“…Kandala Rajesh et al used ensemble empirical mode decomposition (EEMD) features to classify normal PVC, PAC, LBBB, and RBBB heartbeats. For the classification tool, a sequential minimal optimization SVM was used to train and classify the different heartbeat types [33]. The experimental results of this research are compared in Table 4.…”

Section: Model Trainingmentioning

confidence: 99%

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A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Boulanger

2020

Sensors

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Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient’s heart conditions have been introduced on the market. Most of these devices can record a patient’s bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems.

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Section: Methods Description Referencementioning

confidence: 99%

Section: Noise Removalmentioning

confidence: 99%

Section: Heartbeat Detection and Segmentationmentioning

confidence: 99%

Section: Model Trainingmentioning

confidence: 99%

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A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Boulanger

2020

Sensors

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“…(iii) irdly, how to deal with physiological signals in the interest of e ectively extracting entropy measures from them has been becoming one of the most key factors that determine the performance on entropy-based pattern learning tasks. e existing studies have shown that using the entropy-based pattern learning for assessment of physiological signals, the feature extraction of entropy measures depends heavily on the decomposition and representation methods of physiological signals [27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Wang

et al. 2020

Complexity

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Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.

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