Wavelet Packet Analysis of ECG Signals to Understand the Effect of Cannabis Abuse on Cardiac Electrophysiology of Indian Women Working in Paddy Fields

This chapter is aimed at identifying the variation in the cardiac electrophysiology due to the abuse of the cannabis products (bhang) in a non-invasive manner. ECG signals were acquired from 25 Indian women working in the paddy fields. Amongst them, 10 women regularly abused bhang and the rest 15 women never abused bhang. The ECG signals were preprocessed and subjected to wavelet packet decomposition (WPD) up to the level 3 using db04 wavelet. Ninety-six statistical features were extracted from the wavelet packet coefficients and analyzed using linear and non-linear statistical methods. The results suggested a variation in the cardiac electrophysiology due to the abuse of bhang. Artificial neural networks (ANNs), namely, radial basis function (RBF) and multilayer perceptron (MLP) were able to classify the ECG signals with an accuracy of ≥95%. This supported the hypothesis that abuse of bhang may alter the cardiac electrophysiology. The results of the study may be used to increase awareness among people to avoid the abuse of cannabis products.

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Wavelet Packet Analysis of ECG signals to Understand the Effect of a Motivating Song on Heart of Indian Male Volunteers

Expert System Techniques in Biomedical Science Practice - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-5149-2.ch008 ◽

2018 ◽

pp. 168-192

Author(s):

Gitika Yadu ◽

Suraj Kumar Nayak ◽

Debasisha Panigrahi ◽

Anilesh Dey ◽

Kunal Pal

Keyword(s):

Cardiac Electrophysiology ◽

Wavelet Packet ◽

Regression Tree ◽

Classification And Regression Tree ◽

Wavelet Packet Analysis ◽

Ecg Signals ◽

Male Volunteers ◽

Level 3 ◽

Artificial Neural Network Ann ◽

Music Stimulus

This chapter investigates the effect of a motivational song (acting as a stimulus) on the electrical activity of the heart using wavelet packet analysis of electrocardiogram (ECG) signals. ECG signals were acquired from 18 healthy male volunteers during the pre- and the post-stimulus conditions. Wavelet packet decomposition of the ECG signals was performed up to level 3 using db04 wavelet, which resulted in the formation of 8 wavelet packet coefficients. Linear (t-test) and nonlinear (classification and regression tree [CART], boosted tree [BT], and random forest [RF]) methods were used to identify the statistically significant parameters. The statistically significant parameters were used as categorical inputs for multilayer perceptron (MLP)-based artificial neural network (ANN) classification of the ECG signals. A classification efficiency of ≥ 80% was obtained, suggesting an alteration in the cardiac electrophysiology of the volunteers caused by the music stimulus.

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AUTOMATED IDENTIFICATION OF CORONARY ARTERY DISEASE FROM SHORT-TERM 12 LEAD ELECTROCARDIOGRAM SIGNALS BY USING WAVELET PACKET DECOMPOSITION AND COMMON SPATIAL PATTERN TECHNIQUES

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519417400073 ◽

2017 ◽

Vol 17 (07) ◽

pp. 1740007 ◽

Cited By ~ 7

Author(s):

SHU LIH OH ◽

MUHAMMAD ADAM ◽

JEN HONG TAN ◽

YUKI HAGIWARA ◽

VIDYA K. SUDARSHAN ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Spatial Pattern ◽

Wavelet Packet ◽

Ecg Signal ◽

Wavelet Packet Decomposition ◽

Common Spatial Pattern ◽

Ecg Signals ◽

Computer Aided ◽

Artery Disease

The occlusion of the coronary arteries commonly known as coronary artery disease (CAD) restricts the normal blood circulation required to the heart muscles, thus results in an irreversible myocardial damage or death (myocardial infarction). Clinically, electrocardiogram (ECG) is performed as a primary diagnostic tool to capture these cardiac activities and detect the presence of CAD. However, the use of computer-aided techniques can reduce the visual burden and manual time required for the analysis of complex ECG signals in order to identify the CAD affected subjects from normal ones. Therefore, in this study, a novel computer-aided technique is proposed using 2[Formula: see text]s of 12 lead ECG signals for the identification of CAD affected patients. Each of the 2[Formula: see text]s 12 lead ECG signal beats (3791 normal and 12308 CAD ECG signal beats) are implemented with four levels of wavelet packet decomposition (WPD) to obtain various coefficients. Using the fourth-level coefficients obtained for each lead ECG signal beat, new 2[Formula: see text]s. ECG signal beats are reconstructed. Later, the reconstructed signals are split into two-fold data sets, in which one set is used for acquiring common spatial pattern (CSP) filter and the other for obtaining features vector (vice versa). The obtained features are one by one fed into k-nearest neighbors (KNN) classifier for automated classification. The proposed system yielded maximum average classification results of 99.65% accuracy, 99.64% sensitivity and 99.7% specificity using 10 features. Our proposed algorithm is highly efficient and can be used by the clinicians as an aiding system in their CAD diagnosis, thus, assisting in faster treatment and avoiding the progression of CAD condition.

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The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

Journal of Healthcare Engineering ◽

10.1155/2021/1970769 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Abdulhamit Subasi ◽

Saeed Mian Qaisar

Keyword(s):

Machine Learning ◽

Motor Imagery ◽

Brain Computer Interface ◽

Wavelet Packet ◽

Computer Interface ◽

Learning Approach ◽

Wavelet Packet Decomposition ◽

Statistical Features ◽

Ensemble Machine Learning

The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands for actuators. The potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. The contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. The intended electroencephalogram (EEG) signals are segmented and denoised. The denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. The WT with the 5th level of decomposition is used. Onward, the Wavelet Packet Decomposition (WPD), with the 4th level of decomposition, is used for subbands formation. The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems.

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Chironomid larvae recognition method based on wavelet packet decomposition and fuzzy support vector machine

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00227 ◽

2010 ◽

Vol 30 (1) ◽

pp. 227-229

Author(s):

Jing-ying ZHAO ◽

Hai GUO ◽

Xing-bin SUN ◽

Yun-han JIANG

Keyword(s):

Support Vector Machine ◽

Wavelet Packet ◽

Support Vector ◽

Wavelet Packet Decomposition ◽

Recognition Method ◽

Fuzzy Support Vector Machine ◽

Chironomid Larvae

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Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

npj Digital Medicine ◽

10.1038/s41746-021-00447-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Elisa Mejía-Mejía ◽

James M. May ◽

Mohamed Elgendi ◽

Panayiotis A. Kyriacou

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Heart Rate Variability ◽

Pulse Rate ◽

Pulse Transit Time ◽

Pulse Rate Variability ◽

Physiological Factors ◽

Ecg Signals ◽

Non Invasive ◽

Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽

10.3390/w13151997 ◽

2021 ◽

Vol 13 (15) ◽

pp. 1997

Author(s):

Hua Wang ◽

Wenchuan Wang ◽

Yujin Du ◽

Dongmei Xu

Keyword(s):

Wavelet Packet ◽

Moving Average ◽

Back Propagation ◽

Processing Technique ◽

Annual Rainfall ◽

Wavelet Packet Decomposition ◽

Rainfall Forecasting ◽

Promising Alternative ◽

Forecasting Models ◽

Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

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