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

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.

Effect of a Romantic Song on the Autonomic Nervous System and the Heart of Indian Male Volunteers

2018 ◽  
pp. 120-142
Author(s):  
Soumanti Das ◽  
Suraj Kumar Nayak ◽  
Rohit Kumar Verma ◽  
Anilesh Dey ◽  
Kunal Pal
Keyword(s):  
Heart Rate ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Stimulus Condition ◽  
Ecg Signals ◽  
Autonomic Nervous ◽  
Male Volunteers ◽  
Artificial Neural Network Ann ◽  
Music Stimulus ◽  
Indian Male

In this chapter, the effect of an old generation romantic music (stimulus) on the autonomic nervous system (ANS) activity and the cardiac electrophysiology of Indian male volunteers was investigated. Electrocardiogram (ECG) signals were acquired and the corresponding RR intervals (RRIs) were extracted. The recurrence analysis of the RRI time series suggested a more stable heart rate in the post-stimulus condition. Heart rate variability (HRV) analysis detected a dominant parasympathetic activity in the post-stimulus condition. The time-domain and the wavelet transform analyses of the ECG signals predicted an alteration in the electrical activity of the heart because of the exposure to the music stimulus. The classification of the HRV and the ECG parameters was performed using artificial neural network (ANN), which resulted in an accuracy of ≥80%.

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

2018 ◽  
pp. 257-273
Author(s):  
Suraj Kumar Nayak ◽  
Ashirbad Pradhan ◽  
Salman Siddique Khan ◽  
Shikshya Nayak ◽  
Soumanti Das ◽  
...  
Keyword(s):  
Cardiac Electrophysiology ◽  
Wavelet Packet ◽  
Paddy Fields ◽  
Wavelet Packet Decomposition ◽  
Statistical Features ◽  
Indian Women ◽  
Ecg Signals ◽  
Non Invasive ◽  
Cannabis Abuse ◽  
Level 3

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.

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

2022 ◽  
pp. 1246-1262
Author(s):  
Suraj Kumar Nayak ◽  
Ashirbad Pradhan ◽  
Salman Siddique Khan ◽  
Shikshya Nayak ◽  
Soumanti Das ◽  
...  
Keyword(s):  
Cardiac Electrophysiology ◽  
Wavelet Packet ◽  
Paddy Fields ◽  
Wavelet Packet Decomposition ◽  
Statistical Features ◽  
Indian Women ◽  
Ecg Signals ◽  
Non Invasive ◽  
Cannabis Abuse ◽  
Level 3

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.

Effect of Odia and Tamil Music on the ANS and the Conduction Pathway of Heart of Odia Volunteers

2017 ◽  
pp. 240-263 ◽  
Author(s):  
Suraj Kumar Nayak ◽  
Utkarsh Srivastava ◽  
D. N. Tibarewala ◽  
Goutam Thakur ◽  
Biswajit Mohapatra ◽  
...  
Keyword(s):  
Mother Tongue ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Cardiac Conduction ◽  
Linear Classifiers ◽  
Ecg Signals ◽  
Conduction Pathway ◽  
Non Linear ◽  
Boosted Tree ◽  
Classification And Regression

The current study delineates the effect of Odia and Tamil music on the Autonomic Nervous System (ANS) and cardiac conduction pathway of Odia volunteers. The analysis of the ECG signals using Analysis of Variance (ANOVA) showed that the features obtained from the HRV domain, time-domain and wavelet transform domain were statistically insignificant. But non-linear classifiers like Classification and Regression Tree (CART), Boosted Tree (BT) and Random Forest (RF) indicated the presence of important features. A classification efficiency of more than 85% was achieved when the important features, obtained from the non-linear classifiers, were used. The results suggested that there is an increase in the parasympathetic activity when music is heard in the mother tongue. If a person is made to listen to music in the language with which he is not conversant, an increase in the sympathetic activity is observed. It is also expected that there might be a difference in the cardiac conduction pathway.

Dental Data Mining: Potential Pitfalls and Practical Issues

2003 ◽  
Vol 17 (1) ◽  
pp. 109-114 ◽  
Author(s):  
S.A. Gansky
Keyword(s):  
Data Mining ◽  
Operating Characteristic ◽  
Regression Tree ◽  
Careful Analysis ◽  
Classification And Regression Tree ◽  
Receiver Operating Characteristic Curves ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Sensitivity Specificity ◽  
Better Than

Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports ( e.g., Kattan et al., 1998 ) show that ANN and CART models can perform better than classic regression models: CART models excel at covariate interactions, while ANN models excel at nonlinear covariates. Model prediction performance is examined with the use of validation procedures and evaluating concordance, sensitivity, specificity, and likelihood ratio. To aid interpretation, various plots of predicted probabilities are utilized, such as lift charts, receiver operating characteristic curves, and cumulative captured-response plots. A dental caries study is used as an illustrative example. This paper compares the performance of logistic regression with KDD methods of CART and ANN in analyzing data from the Rochester caries study. With careful analysis, such as validation with sufficient sample size and the use of proper competitors, problems of naïve KDD analyses ( Schwarzer et al., 2000 ) can be carefully avoided.

scholarly journals A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 344
Author(s):  
Hyunsuk Kim ◽  
Woojin Kim ◽  
Jungsook Kim ◽  
Seung-Jun Lee ◽  
Daesub Yoon ◽  
...  
Keyword(s):  
Human Factors ◽  
Response Times ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Traffic Density ◽  
Automated Driving ◽  
Stabilization Time ◽  
Factors Affecting ◽  
Cart Analysis ◽  
Level 3

In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.

Analysis of ECG Signals to Investigate the Effect of a Humorous Audio-Visual Stimulus on Autonomic Nervous System and Heart of Females

2018 ◽  
pp. 239-256
Author(s):  
Karan Pande ◽  
Seemadri Subhadarshini ◽  
Deepanjali Gaur ◽  
Suraj Kumar Nayak ◽  
Kunal Pal
Keyword(s):  
Nervous System ◽  
Autonomic Nervous System ◽  
Visual Stimulus ◽  
Cardiac Electrophysiology ◽  
Cardiac Activity ◽  
Arma Model ◽  
Ecg Signals ◽  
Autonomic Nervous ◽  
Rr Interval ◽  
Artificial Neural Network Ann

This chapter is an attempt to understand the effect of audio-visual stimulus with a humorous content on the cardiac electrophysiology. Electrocardiogram (ECG) signals were acquired from 11 female volunteers under the pre- and the post-stimulus conditions. Artificial neural network (ANN)-based classification of the ARMA model coefficients computed from the RR interval signals suggested significant variation in the autonomic nervous system activity. Analysis of the Gabor denoised ECG signals indicated a change in the electrical activity of the heart in the post-stimulus condition, which was confirmed by the ANN-based classification. Recurrence analysis of the RR interval suggested plausible differences of the cardiac activity amongst both the conditions. The audio-visual stimulus has resulted in significant alterations in the ANS and the cardiac physiology.

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