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
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/20/10371 |
| _version_ | 1797475598898036736 |
|---|---|
| author | Suraj Kumar Nayak Maciej Jarzębski Anna Gramza-Michałowska Kunal Pal |
| author_facet | Suraj Kumar Nayak Maciej Jarzębski Anna Gramza-Michałowska Kunal Pal |
| author_sort | Suraj Kumar Nayak |
| collection | DOAJ |
| description | 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 <i>t</i>-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. |
| first_indexed | 2024-03-09T20:46:26Z |
| format | Article |
| id | doaj.art-2f04514d191549969d704b714536de32 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:46:26Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2f04514d191549969d704b714536de322023-11-23T22:43:31ZengMDPI AGApplied Sciences2076-34172022-10-0112201037110.3390/app122010371Automated 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 SignalsSuraj Kumar Nayak0Maciej Jarzębski1Anna Gramza-Michałowska2Kunal Pal3Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, IndiaDepartment of Physics and Biophysics, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 38/42, 60-637 Poznan, PolandDepartment of Gastronomy Science and Functional Foods, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandDepartment of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela 769008, IndiaEarly 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 <i>t</i>-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.https://www.mdpi.com/2076-3417/12/20/10371cannabiscardiac autonomic regulationHRV signalsignal decompositionmachine learning |
| spellingShingle | Suraj Kumar Nayak Maciej Jarzębski Anna Gramza-Michałowska Kunal Pal 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 Applied Sciences cannabis cardiac autonomic regulation HRV signal signal decomposition machine learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | cannabis cardiac autonomic regulation HRV signal signal decomposition machine learning |
| url | https://www.mdpi.com/2076-3417/12/20/10371 |
| work_keys_str_mv | AT surajkumarnayak automateddetectionofcannabisinducedalterationincardiacautonomicregulationoftheindianpaddyfieldworkersusingempiricalmodedecompositiondiscretewavelettransformandwaveletpacketdecompositiontechniqueswithhrvsignals AT maciejjarzebski automateddetectionofcannabisinducedalterationincardiacautonomicregulationoftheindianpaddyfieldworkersusingempiricalmodedecompositiondiscretewavelettransformandwaveletpacketdecompositiontechniqueswithhrvsignals AT annagramzamichałowska automateddetectionofcannabisinducedalterationincardiacautonomicregulationoftheindianpaddyfieldworkersusingempiricalmodedecompositiondiscretewavelettransformandwaveletpacketdecompositiontechniqueswithhrvsignals AT kunalpal automateddetectionofcannabisinducedalterationincardiacautonomicregulationoftheindianpaddyfieldworkersusingempiricalmodedecompositiondiscretewavelettransformandwaveletpacketdecompositiontechniqueswithhrvsignals |