IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0072854.html
   My bibliography  Save this article

Quantifying Memory in Complex Physiological Time-Series

Author

Listed:
  • Amir H Shirazi
  • Mohammad R Raoufy
  • Haleh Ebadi
  • Michele De Rui
  • Sami Schiff
  • Roham Mazloom
  • Sohrab Hajizadeh
  • Shahriar Gharibzadeh
  • Ahmad R Dehpour
  • Piero Amodio
  • G Reza Jafari
  • Sara Montagnese
  • Ali R Mani

Abstract

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Suggested Citation

  • Amir H Shirazi & Mohammad R Raoufy & Haleh Ebadi & Michele De Rui & Sami Schiff & Roham Mazloom & Sohrab Hajizadeh & Shahriar Gharibzadeh & Ahmad R Dehpour & Piero Amodio & G Reza Jafari & Sara Montag, 2013. "Quantifying Memory in Complex Physiological Time-Series," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-8, September.
    • Handle: RePEc:plo:pone00:0072854
    DOI: 10.1371/journal.pone.0072854
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072854
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0072854&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0072854?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. G. R. Jafari & A. Bahraminasab & P. Norouzzadeh, 2007. "Why Does The Standard Garch(1, 1) Model Work Well?," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1223-1230.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    2. da Cunha, C.R. & da Silva, R., 2020. "Relevant stylized facts about bitcoin: Fluctuations, first return probability, and natural phenomena," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    3. Un, Kuok Sin & Ausloos, Marcel, 2022. "Equity premium prediction: Taking into account the role of long, even asymmetric, swings in stock market behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    4. Park, Jungyeon & Alvarenga, Estêvão & Jeon, Jooyoung & Li, Ran & Petropoulos, Fotios & Kim, Hokyun & Ahn, Kwangwon, 2024. "Probabilistic forecast-based portfolio optimization of electricity demand at low aggregation levels," Applied Energy, Elsevier, vol. 353(PB).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0072854. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.