Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy
Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop <i>Complexity and Entropy in Physiological Signals</i> (CEPS) as an open access MATLAB<sup>®</sup> GUI (graphical user inter...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/2/301 |
| _version_ | 1797621159578042368 |
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| author | David Mayor Tony Steffert George Datseris Andrea Firth Deepak Panday Harikala Kandel Duncan Banks |
| author_facet | David Mayor Tony Steffert George Datseris Andrea Firth Deepak Panday Harikala Kandel Duncan Banks |
| author_sort | David Mayor |
| collection | DOAJ |
| description | Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop <i>Complexity and Entropy in Physiological Signals</i> (CEPS) as an open access MATLAB<sup>®</sup> GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates <i>and</i> were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data. |
| first_indexed | 2024-03-11T08:51:43Z |
| format | Article |
| id | doaj.art-4e8ffe77bfd74fd0a22ad82c038229f0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T08:51:43Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4e8ffe77bfd74fd0a22ad82c038229f02023-11-16T20:23:36ZengMDPI AGEntropy1099-43002023-02-0125230110.3390/e25020301Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation EntropyDavid Mayor0Tony Steffert1George Datseris2Andrea Firth3Deepak Panday4Harikala Kandel5Duncan Banks6School of Health and Social Work, University of Hertfordshire, Hatfield AL10 9AB, UKMindSpire, Napier House, 14–16 Mount Ephraim Rd., Tunbridge Wells TN1 1EE, UKDepartment of Mathematics and Statistics, University of Exeter, North Park Road, Exeter EX4 4QF, UKUniversity Campus Football Business, Wembley HA9 0WS, UKSchool of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UKDepartment of Computer Science and Information Systems, Birkbeck, University of London, Malet Street, London WC1E 7HX, UKSchool of Life, Health and Chemical Sciences, STEM, Walton Hall, The Open University, Milton Keynes MK7 6AA, UKBackground: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop <i>Complexity and Entropy in Physiological Signals</i> (CEPS) as an open access MATLAB<sup>®</sup> GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates <i>and</i> were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.https://www.mdpi.com/1099-4300/25/2/301fractal dimensionheart rate asymmetrypermutation entropyparameter tuningpaced breathingresonant breathing |
| spellingShingle | David Mayor Tony Steffert George Datseris Andrea Firth Deepak Panday Harikala Kandel Duncan Banks Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy Entropy fractal dimension heart rate asymmetry permutation entropy parameter tuning paced breathing resonant breathing |
| title | Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy |
| title_full | Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy |
| title_fullStr | Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy |
| title_full_unstemmed | Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy |
| title_short | Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy |
| title_sort | complexity and entropy in physiological signals ceps resonance breathing rate assessed using measures of fractal dimension heart rate asymmetry and permutation entropy |
| topic | fractal dimension heart rate asymmetry permutation entropy parameter tuning paced breathing resonant breathing |
| url | https://www.mdpi.com/1099-4300/25/2/301 |
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