Park, Chanki Youn, In chan Han, Sungmin 2024-01-12T02:34:03Z 2024-01-12T02:34:03Z 2022-12-29 2022-12 2045-2322 https://pubs.kist.re.kr/handle/201004/75880 We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual’s breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p？<？0.000005) and decreased (p？<？0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity. English Nature Publishing Group Single-lead ECG based autonomic nervous system assessment for meditation monitoring Article 10.1038/s41598-022-27121-x 1 Scientific Reports, v.12, no.1 Scientific Reports 12 1 Y scie scopus 000971885800052 Multidisciplinary Sciences Science & Technology - Other Topics Article HEART-RATE-VARIABILITY RESPIRATORY SINUS ARRHYTHMIA MINDFULNESS MEDITATION BLOOD-PRESSURE BAROREFLEX SENSITIVITY STRESS REDUCTION RR-INTERVAL HUMANS SLOW METAANALYSIS