Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection
With advances in portable and wearable devices, it should be possible to analyze and interpret the collected biosignals from those devices to tailor a psychological intervention to help patients. This study focuses on detecting anxiety by using a portable device that collects electrocardiogram (ECG)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/11/711 |
| _version_ | 1797465859337224192 |
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| author | Florian Ritsert Mohamed Elgendi Valeria Galli Carlo Menon |
| author_facet | Florian Ritsert Mohamed Elgendi Valeria Galli Carlo Menon |
| author_sort | Florian Ritsert |
| collection | DOAJ |
| description | With advances in portable and wearable devices, it should be possible to analyze and interpret the collected biosignals from those devices to tailor a psychological intervention to help patients. This study focuses on detecting anxiety by using a portable device that collects electrocardiogram (ECG) and respiration (RSP) signals. The feature extraction focused on heart-rate variability (HRV) and breathing-rate variability (BRV). We show that a significant change in these signals occurred between the non-anxiety-induced and anxiety-induced states. The HRV biomarkers were the mean heart rate (MHR; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.04), the standard deviation of the heart rate (SD; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.01), and the standard deviation of NN intervals (SDNN; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.03) for ECG signals, and the mean breath rate (MBR; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.002), the standard deviation of the breath rate (SD; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> < 0.0001), the root mean square of successive differences (RMSSD; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> < 0.0001) and SDNN (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> < 0.0001) for RSP signals. This work extends the existing literature on the relationship between stress and HRV/BRV by being the first to introduce a transitional phase. It contributes to systematically processing mental and emotional impulse data in humans measured via ECG and RSP signals. On the basis of these identified biomarkers, artificial-intelligence or machine-learning algorithms, and rule-based classification, the automated biosignal-based psychological assessment of patients could be within reach. This creates a broad basis for detecting and evaluating psychological abnormalities in individuals upon which future psychological treatment methods could be built using portable and wearable devices. |
| first_indexed | 2024-03-09T18:28:32Z |
| format | Article |
| id | doaj.art-9a5fd9a9f21949b2836fd87af7106883 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T18:28:32Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-9a5fd9a9f21949b2836fd87af71068832023-11-24T07:44:51ZengMDPI AGBioengineering2306-53542022-11-0191171110.3390/bioengineering9110711Heart and Breathing Rate Variations as Biomarkers for Anxiety DetectionFlorian Ritsert0Mohamed Elgendi1Valeria Galli2Carlo Menon3Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, SwitzerlandBiomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, SwitzerlandBiomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, SwitzerlandBiomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, SwitzerlandWith advances in portable and wearable devices, it should be possible to analyze and interpret the collected biosignals from those devices to tailor a psychological intervention to help patients. This study focuses on detecting anxiety by using a portable device that collects electrocardiogram (ECG) and respiration (RSP) signals. The feature extraction focused on heart-rate variability (HRV) and breathing-rate variability (BRV). We show that a significant change in these signals occurred between the non-anxiety-induced and anxiety-induced states. The HRV biomarkers were the mean heart rate (MHR; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.04), the standard deviation of the heart rate (SD; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.01), and the standard deviation of NN intervals (SDNN; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.03) for ECG signals, and the mean breath rate (MBR; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> = 0.002), the standard deviation of the breath rate (SD; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> < 0.0001), the root mean square of successive differences (RMSSD; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> < 0.0001) and SDNN (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>p</mi><mo>¯</mo></mover></semantics></math></inline-formula> < 0.0001) for RSP signals. This work extends the existing literature on the relationship between stress and HRV/BRV by being the first to introduce a transitional phase. It contributes to systematically processing mental and emotional impulse data in humans measured via ECG and RSP signals. On the basis of these identified biomarkers, artificial-intelligence or machine-learning algorithms, and rule-based classification, the automated biosignal-based psychological assessment of patients could be within reach. This creates a broad basis for detecting and evaluating psychological abnormalities in individuals upon which future psychological treatment methods could be built using portable and wearable devices.https://www.mdpi.com/2306-5354/9/11/711digital healthwearable technologyheart rate variabilityrespiration rate variabilitybreathing rate variabilityanxiety assessment |
| spellingShingle | Florian Ritsert Mohamed Elgendi Valeria Galli Carlo Menon Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection Bioengineering digital health wearable technology heart rate variability respiration rate variability breathing rate variability anxiety assessment |
| title | Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection |
| title_full | Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection |
| title_fullStr | Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection |
| title_full_unstemmed | Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection |
| title_short | Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection |
| title_sort | heart and breathing rate variations as biomarkers for anxiety detection |
| topic | digital health wearable technology heart rate variability respiration rate variability breathing rate variability anxiety assessment |
| url | https://www.mdpi.com/2306-5354/9/11/711 |
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