Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction

Hsu, Che Hao; Tsai, Ming Ya; Huang, Go Shine; Lin, Tso Chou; Chen, Kuen Pao; Ho, Shung Tai; Shyu, Liang Yu; Li, Chi Yuan

doi:10.1016/j.aat.2012.03.002

Cited by 93 publications

(85 citation statements)

References 17 publications

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“…Analysis of HRV based on short-term analysis of the Poincaré plot provides a sensitive, non-invasive measurement of autonomic input to the heart [20]. The Poincaré plot is more visually discernible in the detection of autonomic changes, including the potential to evaluate dynamic changes [21].…”

Section: Discussionmentioning

confidence: 99%

The Effect of Pottery Therapy on Heart Rate Variability in College Students with Mental Health Problems

Nakamura¹,

Sonezaki²,

Hayashida³

et al. 2017

J Ment Disord Treat

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Section: Discussionmentioning

confidence: 99%

The Effect of Pottery Therapy on Heart Rate Variability in College Students with Mental Health Problems

Nakamura¹,

Sonezaki²,

Hayashida³

et al. 2017

J Ment Disord Treat

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“…This low dispersion pattern of RR reflects a reduction in HRV, indicating loss of ANS complexity for these individuals (16). In the control group, the visual analysis of Poincaré plot showed a greater beat-to-beat dispersion and long-term RR dispersion which is representative of a normal ANS behaviour (10,16).…”

Section: ) Rr(n) (Ms) On the X-axis And Rr (N+1) (Ms) On The Y-axismentioning

confidence: 99%

“…In the Poincaré plot qualitative analysis, which allows a nonlinear assessment of ANS (16,33,35), was observed a less dispersion of points for the DM1 group compared with the control group. This low dispersion pattern of RR reflects a reduction in HRV, indicating loss of ANS complexity for these individuals (16).…”

Section: ) Rr(n) (Ms) On the X-axis And Rr (N+1) (Ms) On The Y-axismentioning

confidence: 99%

“…Nonlinear methods are based on chaos theory and may enable better discrimination between normal and abnormal ANS physiology (6). For example, the Poincaré plot is a method used for geometric and nonlinear analysis of the autonomic modulation (16)(17)(18). A previous study involving type 2 diabetic individuals assessed different methods of HRV and reported that geometric methods are more reliable for evaluating the presence of an autonomic diabetic disease (19), and studies that analyze this type of indexes in DM1 population were not deeply explored.…”

mentioning

confidence: 99%

“…Also, these studies used linear methods of analysis of frequency and time domains (14,15), which present limitations as stationary requirements, and show more sensitivity to interference and ectopic beats when compared with nonlinear methods (16). Nonlinear methods are based on chaos theory and may enable better discrimination between normal and abnormal ANS physiology (6).…”

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confidence: 99%

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Geometric indexes of heart rate of variability identifies autonomic alterations in young patients with type 1 diabetes mellitus

Souza¹,

Pastre²

2016

Curr Res Cardiol

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ObjeCtiveS:To compare autonomic modulation in young patients with type 1 diabetes mellitus (DM1) (n=20) and healthy control subjects (n=23) using geometric indexes of heart rate variability (HRV) associated with linear domains of time and frequency; and to verify correlations between geometric indexes and those obtained by the time and the frequency domain of diabetic individuals. MethOdS: Heart rate was recorded as beat-to-beat with subjects supine for 30 min. The following indexes were calculated: RRtri, TINN, Poincaré plot, SD1, SD2, SD1/SD2, RMSSD, SDNN, LF and HF (ms²/un) (DM1) (1) because it affects the fibres of the autonomic nerves of the heart and blood vessels, causing heart rate abnormalities and vascular dynamic impairments (2,3), leading to increased mortality (4,5).Changes in the autonomic modulation of DM1 were previously described through heart rate variability (HRV) (6-9), a noninvasive assessment method that measures the oscillation of intervals between consecutive heart beats (RR), which are related to influences from the autonomic nervous system (ANS) on the sinus node (10,11).Most studies that assessed autonomic modulation by HRV included populations with type 2 diabetes mellitus (9,12-15), a population that normally have associated comorbidities that could interfere in autonomic modulation (12). Also, these studies used linear methods of analysis of frequency and time domains (14,15), which present limitations as stationary requirements, and show more sensitivity to interference and ectopic beats when compared with nonlinear methods (16). Nonlinear methods are based on chaos theory and may enable better discrimination between normal and abnormal ANS physiology (6). For example, the Poincaré plot is a method used for geometric and nonlinear analysis of the autonomic modulation (16-18). A previous study involving type 2 diabetic individuals assessed different methods of HRV and reported that geometric methods are more reliable for evaluating the presence of an autonomic diabetic disease (19), and studies that analyze this type of indexes in DM1 population were not deeply explored.Therefore, geometric methods could identify autonomic changes and help to plan a better treatment strategy in DM1 patients. Thus, the aim of the present study was: to compare HRV indexes obtained by geometric methods (RRtri, TINN and Poincaré plot -SD1, SD2 and SD1/SD2), associated to indexes of time domain (RMSSD and SDNN) and frequency domain (LF, HF (ms²/un) and LF/HF), of individuals with DM1 and healthy control subjects; and to verify correlations between geometric indexes and those obtained by the time and the frequency domain of diabetic individuals. MethOdS SubjectsThe present study included 43 young adults who were divided into two groups: the DM1 group (n=20 [nine men, 11 women]) with a mean (± SD) age 22.05±4.35 years, with 10.60±4.81 years of DM1 diagnosis, and a control group with 23 healthy subjects (15 men, eight women) mean age 21.69±2.42 years.The inclusion criteria were absence of cardiorespirato...

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Flexible Wearable Sensors for Cardiovascular Health Monitoring

Chen

Fan

et al. 2021

Adv Healthcare Materials

233

148

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Cardiovascular diseases account for the highest mortality globally, but recent advances in wearable technologies may potentially change how these illnesses are diagnosed and managed. In particular, continuous monitoring of cardiovascular vital signs for early intervention is highly desired. To this end, flexible wearable sensors that can be comfortably worn over long durations are gaining significant attention. In this review, advanced flexible wearable sensors for monitoring cardiovascular vital signals are outlined and discussed. Specifically, the functional materials, configurations, mechanisms, and recent advances of these flexible sensors for heart rate, blood pressure, blood oxygen saturation, and blood glucose monitoring are highlighted. Different mechanisms in bioelectric, mechano‐electric, optoelectric, and ultrasonic wearable sensors are presented to monitor cardiovascular vital signs from different body locations. Present challenges, possible strategies, and future directions of these wearable sensors are also discussed. With rapid development, these flexible wearable sensors will potentially be applicable for both medical diagnosis and daily healthcare use in tackling cardiovascular diseases.

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Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction

Abstract: Poincaré plot and power spectral analysis of HRV during tilt test and sevoflurane induction significantly correlate. Poincaré plot analysis is easier and more sensitive at evaluating the sympathovagal balance and observing the beat-to-beat HRV.

Cited by 93 publications

References 17 publications

The Effect of Pottery Therapy on Heart Rate Variability in College Students with Mental Health Problems

The Effect of Pottery Therapy on Heart Rate Variability in College Students with Mental Health Problems

Geometric indexes of heart rate of variability identifies autonomic alterations in young patients with type 1 diabetes mellitus

Flexible Wearable Sensors for Cardiovascular Health Monitoring

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