Can statistical and entropy-based features extracted from ECG signals efficiently differentiate the cannabis-consuming women population from the non-consumer?

Majhi, Manoja K.; Pradhan, Bikash K.; Sarkar, Preetam; Sivaraman, J.; Pal, Kunal

doi:10.1016/j.mehy.2022.110952

Cited by 4 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In reference [5] the accuracy was small as compare with the accuracy achieved in proposed technique in this paper, as well as the number of epochs required in this reference were 5000 epochs whereas in our technique, the maximum number of epochs required for the results to be converged are 10 epochs only as in Fig. 1.…”

Section: Comparison Study Of Proposed Classification Framework With P...mentioning

confidence: 65%

“…Dewangan and Shukla [5] classified five types of heartbeats using artificial neural networks (ANN) and discrete wavelet transformation (DWT). The authors concluded that using the wavelet coefficient and morphological features increased ANN's performance in terms of accuracy, achieving 87 % accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Analyzing ECG signal to extract relevant information is the essential step in designing artificially intelligent based Arrhythmia detection techniques. Many researches produced different feature extraction methods based on analysis different characteristics of ECG signal including morphological and time domain analysis [3], feature domain analysis, time-frequency domain [4,5], and statistical methods [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient ECG Beats Classification Techniques for The Cardiac Arrhythmia Detection Based on Wavelet Transformation

2023

IJIES

View full text Add to dashboard Cite

Arrhythmia is one of the cardiovascular disease types that affect humans and often leads to death. generally, ECG signals uses to diagnose the patient's heart state where the ECG illustrates the electrical activities and physiological state of the heart. This paper proposes ECG classification model to classify four types of heartbeats for the early detection of Arrhythmia. Detail wavelet coefficients of ECG were extracted using discrete wavelet transform (DWT) to produce new datasets of ECG with for the dimensions and processed information to ensure the efficiency of proposed classification techniques. In addition, power spectral density (PSD) has been calculated for approximate wavelet coefficients of ECG to extract more relevant features that improve the performance of the classifier. The two classifier models use, the convolution neural network (CNN) utilizes for deep learning networks with artificial neural network (ANN) and the Random forest ensemble method. The experiments and results show that, the proposed model with RF archives 98.5% classification accuracy considering all decomposition levels of DWT. Additionally, the solution with CNN-ANN achieves 96% classification accuracy at the third decomposition level. Therefore, the results show the impact of the proposed solution with high efficiency in terms of fewer dimensions and high accuracy.

show abstract

Section: Comparison Study Of Proposed Classification Framework With P...mentioning

confidence: 65%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient ECG Beats Classification Techniques for The Cardiac Arrhythmia Detection Based on Wavelet Transformation

2023

IJIES

View full text Add to dashboard Cite

show abstract

An Explainable Machine-Learning Model to Analyze the Effects of a PCSK9 Inhibitor on Thrombolysis in STEMI Patients

et al. 2023

View full text Add to dashboard Cite

Automated Detection of Cannabis-Induced Alteration in Cardiac Autonomic Regulation of the Indian Paddy-Field Workers Using Empirical Mode Decomposition, Discrete Wavelet Transform and Wavelet Packet Decomposition Techniques with HRV Signals

et al. 2022

View full text Add to dashboard Cite

Early detection of the dysfunction of the cardiac autonomic regulation (CAR) may help in reducing cannabis-related cardiovascular morbidities. The current study examined the occurrence of changes in the CAR activity that is associated with the consumption of bhang, a cannabis-based product. For this purpose, the heart rate variability (HRV) signals of 200 Indian male volunteers, who were categorized into cannabis consumers and non-consumers, were decomposed by Empirical Mode Decomposition (EMD), Discrete Wavelet transform (DWT), and Wavelet Packet Decomposition (WPD) at different levels. The entropy-based parameters were computed from all the decomposed signals. The statistical significance of the parameters was examined using the Mann–Whitney test and t-test. The results revealed a significant variation in the HRV signals among the two groups. Herein, we proposed the development of machine learning (ML) models for the automatic classification of cannabis consumers and non-consumers. The selection of suitable input parameters for the ML models was performed by employing weight-based parameter ranking and dimension reduction methods. The performance indices of the ML models were compared. The results recommended the Naïve Bayes (NB) model developed from WPD processing (level 8, db02 mother wavelet) of the HRV signals as the most suitable ML model for automatic identification of cannabis users.

show abstract

Can statistical and entropy-based features extracted from ECG signals efficiently differentiate the cannabis-consuming women population from the non-consumer?

Cited by 4 publications

References 21 publications

Efficient ECG Beats Classification Techniques for The Cardiac Arrhythmia Detection Based on Wavelet Transformation

Efficient ECG Beats Classification Techniques for The Cardiac Arrhythmia Detection Based on Wavelet Transformation

An Explainable Machine-Learning Model to Analyze the Effects of a PCSK9 Inhibitor on Thrombolysis in STEMI Patients

Automated Detection of Cannabis-Induced Alteration in Cardiac Autonomic Regulation of the Indian Paddy-Field Workers Using Empirical Mode Decomposition, Discrete Wavelet Transform and Wavelet Packet Decomposition Techniques with HRV Signals

Contact Info

Product

Resources

About