Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals

Ebrahimi, Farideh; Setarehdan, Seyed Kamaledin; Ayala-Moyeda, Jose; Nazeran, Homer

doi:10.1016/j.cmpb.2013.06.007

Cited by 69 publications

(42 citation statements)

References 21 publications

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“…Typically, HRV is distinguished into three frequency bands, i.e., very low frequency (VLF), low frequency (LF), and high frequency (HF). A study [10] has investigated that the computation of the nonlinear HRV indexes needs even longer segments (at least five minutes), and it may reduce the resolution of the estimated sleep staging results. On the other hand, the Pan-Tompkins algorithm has been widely used for the pre-processing stage of the ECG signal [11] due to its effectiveness in detecting the position of the QRS complex.…”

Section: Related Workmentioning

confidence: 99%

Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features

2020

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Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in sleep disorder diagnosis is a high-cost process. Therefore, we propose an efficient classification method of sleep disorder by merely using electrocardiography (ECG) signals to simplify the sleep disorders diagnosis process. Different from many current related studies that applied a five-minute epoch to observe the main frequency band of the ECG signal, we perform a pre-processing technique that suitable for the 30-seconds epoch of the ECG signal. By this simplification, the proposed method has a low computational cost so that suitable to be implemented in an embedded hardware device. Structurally, the proposed method consists of five stages: (1) pre-processing, (2) spectral features extraction, (3) sleep stage detection using the Decision-Tree-Based Support Vector Machine (DTB-SVM), (4) assess the sleep quality features, and (5) sleep disorders classification using ensemble of bagged tree classifiers. We evaluate the effectiveness of the proposed method in the task of classifying the sleep disorders into four classes (insomnia, Sleep-Disordered Breathing (SDB), REM Behavior Disorder (RBD), and healthy subjects) from the 51 patients of the Cyclic Alternating Pattern (CAP) sleep data. Based on experimental results, the proposed method presents 84.01% of sensitivity, 94.17% of specificity, 86.27% of overall accuracy, and 0.70 of Cohen’s kappa. This result indicates that the proposed method able to reliably classify the sleep disorders merely using the 30-seconds epoch ECG in order to address the issue of a multichannel signal such as the PSG.

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Section: Related Workmentioning

confidence: 99%

Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features

2020

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“…In works of many authors the Discriminant analysis is used as the classifying method: in tasks of automatic sleep staging (Ebrahimi et al, 2013), mental load estimation (Cinaz et al, 2013), arrhythmia detection (Sivanantham and Shenbaga Devi, 2014), real-life stress detection (Melillo et al, 2011) and for automatic assessment of heart failure severity (Melillo et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of the Discriminant Analysis for Diagnostics of the Arterial Hypertension - Analysis of Short-Term Heart Rate Variability Signals

Kublanov

Dolganov

Borisov

2016

Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics

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The investigation of the diagnostic possibilities for the arterial hypertension is presented. The 41 features of the statistical, geometric, spectral and nonlinear methods during functional loads were considered for two groups: healthy volunteers and patients suffering from the arterial hypertension of the II-III degree. Application of the linear and quadratic discriminant analysis showed particular features that have high classification efficiency.

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“…Kutlu and Kuntalp [12] derived features by using the higher order statistics of wavelet packet decomposition (WPD) coefficients and used them as inputs into a classifier based on the k-nearest neighbor algorithm. Nonlinear features, including approximate entropy (ApEn), were extracted from heart rate variability (HRV) and ECG signals with a high classification accuracy [5]. Nonlinear features were used as feature sets to be placed into several classifiers [1].…”

Section: Introductionmentioning

confidence: 99%

A New ECG Signal Classification Based on WPD and ApEn Feature Extraction

Feng

Cao

et al. 2015

Circuits Syst Signal Process

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Electrocardiogram (ECG) signal classification is an important diagnosis tool wherein feature extraction plays a crucial function. This paper proposes a novel method for the nonlinear feature extraction of ECG signals by combining wavelet packet decomposition (WPD) and approximate entropy (ApEn). The proposed method first uses WPD to decompose ECG signals into different frequency bands and then calculates the ApEn of each wavelet packet coefficient as a feature vector. A support vector machine (SVM) classifier is used for the classification. The particle swarm optimization algorithm is used to optimize the SVM parameters. The proposed method does not require dimensionality reduction, has fast calculation speed, and requires sim-B Hongqiang Li Circuits Syst Signal Process ple computations. The classification of the signals into five beats yields an acceptable accuracy of 97.78 %.

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Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals

Cited by 69 publications

References 21 publications

Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features

Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features

Application of the Discriminant Analysis for Diagnostics of the Arterial Hypertension - Analysis of Short-Term Heart Rate Variability Signals

A New ECG Signal Classification Based on WPD and ApEn Feature Extraction

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