Long-range dependence in heart rate variability data: ARFIMA modelling vs detrended fluctuation analysis

Leite, Argentina; Rocha, Ana Paula; Silva, Maria Eduarda; Gouveia, Sónia; Carvalho, João L. A.; Costa, O

doi:10.1109/cic.2007.4745411

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…Similar numbers are observed for the congestive heart failure patients: 95.9% and 85.8%, respectively, see Table III. These results are in concordance with Baillie et al 16 and Leite et al 14,37,38 Moreover, patients suffering from congestive heart failure present increased values ford; during night and day periods, while patients undergoing atrial fibrillation present lower values ford; compared with normal subjects. In fact, only 54.2% of the segments in atrial fibrillation patients present statistically significance values for d as tested with surrogate data, Table III.…”

Section: Resultssupporting

confidence: 89%

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

Leite

Rocha

Silva

2013

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

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Section: Resultssupporting

confidence: 89%

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

Leite

Rocha

Silva

2013

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Em particular, usam-se métodos tradicionais para a caracterização da VFC, tais como métodos no domínio do tempo e no da frequência (Task Force of ESC/NASPE, 1996). Consideram-se, ainda, métodos recentemente referidos na literatura (Leite et al, 2007) baseados em medidas não lineares, tais como DFA e ARFIMA. Opta-se por analisar dados previamente referidos na literatura, a base de dados Noltisalis (Signorini, Sassi, & Cerutti, 2001), a fim de ser possível uma comparação de resultados.…”

Section: Métodounclassified

“…Método DFA (Peng et al, 1995): parâmetros de memória curta α 1 (correspondente ao período de 4 a 11 batimentos; Pikkujämsä et al, 1999) e de memória longa α 2 (correspondente ao período de 64 a 1024 batimentos; Leite et al, 2007); Modelação ARFIMA (Leite et al, 2006): parâmetro de memória longa d (obtido usando o estimador local de Whittle) e componentes espectrais LF ARFIMA e HF ARFIMA (estimadas após remoção da componente de memória longa).…”

Section: Instrumentos E Procedimentosunclassified

“…A segmentação do registo longo em registos curtos pode ser fixa (todos ao segmentos têm a mesma dimensão fixa à partida) ou dita adaptativa se os segmentos têm dimensões variáveis dependendo das suas características. No cálculo dos parâmetros no domínio do tempo assim como na análise pelo método não paramétrico DFA, usam-se segmentos curtos de dimensão fixa (4096 batimentos, para permitir obter estimativas adequadas da memória longa; Leite et al, 2007). Já as metodologias paramétricas ARFIMA (ou AR), permitem que se considere uma segmentação adaptativa seletiva que é baseada no critério AIC.…”

Section: Instrumentos E Procedimentosunclassified

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Análise da variabilidade da frequência cardíaca em indivíduos saudáveis, doentes com insuficiência cardíaca e doentes transplantados

2013

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RESUMO O objetivo deste estudo foi determinar parâmetros de caracterização da variabilidade da frequência cardíaca (VFC) em indivíduos saudáveis e doentes que permitam a sua discriminação. Os parâmetros usados descrevem as diferentes componentes de memória (curta e longa) da VFC e são calculados por métodos paramétricos e não paramétricos. Assim, foram consideradas as componentes espectrais das baixas frequências (LF) e altas frequências (HF) associadas à memória curta e o parâmetro de memória longa (d) obtidos por modelação autoregressiva de médias móveis integrados fracionariamente (ARFIMA). Analogamente, no contexto não paramétrico, foi utilizado o método detrended fluctuation analysis (DFA) no cálculo dos parâmetros de memória curta (α 1) e longa (α 2). A amostra foi constituída por 30 registos de 24 horas da base de dados Noltisalis: 10 indivíduos saudáveis, 10 doentes com insuficiência cardíaca e 10 doentes após transplante cardíaco. Observou-se que os parâmetros que caracterizam a memória curta apresentam valores mais elevados para o grupo dos indivíduos saudáveis, enquanto os parâmetros de memória longa têm valores mais elevados para os grupos dos doentes. Concluiu-se que a modelação ARFIMA permite discriminar os 3 grupos de dados em estudo, apresentando vantagens sobre o DFA. Palavras-chave: análise espectral, memória longa, método DFA, modelos ARFIMA, variabilidade da frequência cardíaca ABSTRACT This study aimed to find parameters to characterize heart rate variability (HRV) and discriminate healthy subjects and patients with heart diseases. The parameters used for discrimination characterize the different components of HRV memory (short and long) and are extracted from HRV recordings using parametric as well as non parametric methods. Thus, the parameters are: spectral components at low frequencies (LH) and high frequencies (HF) which are associated with the short memory of HRV and the long memory parameter (d) obtained from autoregressive fractionally integrated moving average (ARFIMA) models. In the non parametric context, short memory (α 1) and long memory (α 2) parameters are obtained from detrended fluctuation analysis (DFA). The sample used in this study contains 24-hour Holter HRV recordings of 30 subjects: 10 healthy individuals, 10 patients suffering from congestive heart failure and 10 heart transplanted patients from the Noltisalis database. It was found that short memory parameters present higher values for the healthy individuals whereas long memory parameters present higher values for the diseased individuals. Moreover, there is evidence that ARFIMA modeling allows the discrimination between the 3 groups under study, being advantageous over DFA.

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“…The idea of the LTP mechanism was envisioned more than 50 years ago by Hurst (1951) when he was designing water storage facilities for the Aswan Dam. However, its use has been extended to other scientific areas for stochastic modeling purposes such as network traffic modeling (e.g., Liu et al 2006), medical sciences (e.g., Leite et al 2007), among others. Despite these developments, the LTP mechanism has not received overwhelming popularity.…”

Section: Introductionmentioning

confidence: 99%

Pacific Decadal Oscillation Climate Variability and Temporal Pattern of Winter Flows in Northwestern North America

Khaliq

Gachon

2010

Journal of Hydrometeorology

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There is growing concern about the effects of large-scale oceanic atmospheric climate variability, such as the Pacific decadal oscillation (PDO), on regional hydrology and water resources. In this paper, the effects of PDO on temporal patterns of winter (January-March) flow in northwestern North America (NWNA), which is believed to be a PDO-sensitive region, is studied for the period 1943-2007 using daily streamflow data from a much larger set of 179 stations, compared to previous studies in which only smaller subsets of these stations were analyzed. Time series of winter flows were divided into two nonoverlapping blocks corresponding to change points detected in time series of December-March mean monthly PDO indices. Both parametric and nonparametric measures of correlation and average percentage differences and average standardized differences from the period-of-record mean were explored. Like some of the previous studies, it is found that, on average, winter flows tend to be higher (lower) during the warm (cold) phase of the PDO and that establishes the physical link between large-scale climate variability and basin response. It is shown that the serial structure of time series of PDO indices conforms to that of a stochastic process with long-term persistence (LTP). Based on this finding and the climate-streamflow physical link, it is plausible to investigate temporal variations in winter flows with the LTP hypothesis, in addition to assuming merely independence (IND) or short-term persistence (STP). The results of the analysis demonstrate that the LTP mechanism, in combination with the STP, is able to explain more than half of the significant trends noted, with the IND assumption suggesting that the significance of trends reported in previous studies in NWNA may have been overstated. This result has important implications for future planning of regional water resources.

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Long-range dependence in heart rate variability data: ARFIMA modelling vs detrended fluctuation analysis

Cited by 12 publications

References 8 publications

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

Análise da variabilidade da frequência cardíaca em indivíduos saudáveis, doentes com insuficiência cardíaca e doentes transplantados

Pacific Decadal Oscillation Climate Variability and Temporal Pattern of Winter Flows in Northwestern North America

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