Dataset Information

Multiscale based nonlinear dynamics analysis of heart rate variability signals.

ABSTRACT: Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.

SUBMITTER: Kazmi SZH

PROVIDER: S-EPMC7746153 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Multiscale based nonlinear dynamics analysis of heart rate variability signals.

Kazmi Syed Zaki Hassan SZH Habib Nazneen N Riaz Rabia R Rizvi Sanam Shahla SS Abbas Syed Ali SA Chung Tae-Sun TS

PloS one 20201217 12

Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach mu ...[more]

PMID: 33332361

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Project description:ObjectiveTo evaluate the heart rate variability (HRV) in patients with multibacillary leprosy using dynamic linear and nonlinear analysis.Material and methodsTwenty-one leprosy patients (mean age: 39.14 ±10.58 years) and 21 healthy subjects (mean age: 36.24 ± 12.64 years) completed the sample. Heart rate variability recording was performed by a Polar RS800 CX heart monitor during a period of 15 min in the supine position and 15 min in a sitting position. Analysis of HRV was performed by frequency domain from high frequency (HF) and low frequency (LF) spectral indexes in absolute and normalized units. The nonlinear analysis of HRV was calculated using symbolic analysis (0V%, 1V%, 2LV% and 2UV% indexes), Shannon entropy (SE) and normalized complexity index (NCI).ResultsLinear analysis: both groups showed higher HF values (p < 0.05) and smaller LF values (p < 0.05) in supine than in sitting position. The leprosy patients showed higher LF values (p < 0.05) and smaller HF values (p < 0.05) compared to the controls on supine position. Symbolic analysis: leprosy patients had higher 0V% values (p < 0.05), smaller 2LV% values (p < 0.05) and 2UV % values compared to healthy subjects on both positions. The 1V % had higher values (p < 0.05) for leprosy patients than for controls in the sitting position. The control subjects had smaller 0V % values (p < 0.05), and higher 2UV % values (p < 0.05) in the supine position compared to the sitting position. Leprosy patients had higher 2UV index values (p < 0.05) in the supine position compared to the sitting position. In the complexity analysis, leprosy patients had smaller SE and NCI values (p < 0.05) than the control in the supine position. There was no difference between the SE and NCI values of leprosy and the control subjects in the sitting position. The control subjects had higher SE and NCI values (p < 0.05) in the supine position than in the sitting position.ConclusionLeprosy patients had higher sympathetic modulation and smaller vagal modulation than controls, indicating less HRV and cardiac modulation with lower complexity. The control group displayed significant HRV differences in response to position changes while leprosy patients had fewer HRV differences after the same postural change. An analysis of HRV with linear and non-linear dynamics proved to be a reliable method and promising for the investigation of autonomic dysfunction in patients with multibacillary leprosy.

Project description:OBJECTIVE:Changes in heart rate (HR) and locomotor activity reflect changes in autonomic physiology, behavior, and mood. These systems may involve interrelated neural circuits that are altered in psychiatric illness, yet their interactions are poorly understood. We hypothesized interactions between HR and locomotor activity could be used to discriminate patients with schizophrenia from controls, and would be less able to discriminate non-psychiatric patients from controls. APPROACH:HR and locomotor activity were recorded via wearable patches in 16 patients with schizophrenia and 19 healthy controls. Measures of signal complexity and interactions were calculated over multiple time scales, including sample entropy, mutual information, and transfer entropy. A support vector machine was trained on these features to discriminate patients from controls. Additionally, time series were converted into a network with nodes comprised of HR and locomotor activity states, and edges representing state transitions. Graph properties were used as features. Leave-one-out cross validation was performed. To compare against non-psychiatric illness, the same approach was repeated in 41 patients with atrial fibrillation (AFib) and 53 controls. MAIN RESULTS:Network features enabled perfect discrimination of schizophrenia patients from controls with an areas under the receiver operating characteristic curve (AUC) of 1.00 for training and test data. Other bivariate measures of interaction achieved lower AUCs (train 0.98, test 0.96), and univariate measures of complexity achieved the lowest performance. Conversely, interaction features did not improve discrimination of AFib patients from controls beyond univariate approaches. SIGNIFICANCE:Interactions between HR and locomotor activity enabled perfect discrimination of subjects with schizophrenia from controls, but these features were less performant in a non-psychiatric illness. This is the first quantitative evaluation of interactions between physiology and behavior in patients with psychiatric illness.

Dataset Information

Multiscale based nonlinear dynamics analysis of heart rate variability signals.

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Multiscale based nonlinear dynamics analysis of heart rate variability signals.

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