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Abid NUH, Lum Cheng In T, Bottaro M, Shen X, Hernaez Sanz I, Yoshida S, Formentin C, Montagnese S, Mani AR. Application of short-term analysis of skin temperature variability in prediction of survival in patients with cirrhosis. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1291491. [PMID: 38250541 PMCID: PMC10796461 DOI: 10.3389/fnetp.2023.1291491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024]
Abstract
Background: Liver cirrhosis is a complex disorder, involving several different organ systems and physiological network disruption. Various physiological markers have been developed for survival modelling in patients with cirrhosis. Reduction in heart rate variability and skin temperature variability have been shown to predict mortality in cirrhosis, with the potential to aid clinical prognostication. We have recently reported that short-term skin temperature variability analysis can predict survival independently of the severity of liver failure in cirrhosis. However, in previous reports, 24-h skin temperature recordings were used, which are often not feasible in the context of routine clinical practice. The purpose of this study was to determine the shortest length of time from 24-h proximal temperature recordings that can accurately and independently predict 12-month survival post-recording in patients with cirrhosis. Methods: Forty individuals diagnosed with cirrhosis participated in this study and wireless temperature sensors (iButtons) were used to record patients' proximal skin temperature. From 24-h temperature recordings, different length of recordings (30 min, 1, 2, 3 and 6 h) were extracted sequentially for temperature variability analysis using the Extended Poincaré plot to quantify both short-term (SD1) and long-term (SD2) variability. These patients were then subsequently followed for a period of 12 months, during which data was gathered concerning any cases of mortality. Results: Cirrhosis was associated with significantly decreased proximal skin temperature fluctuations among individuals who did not survive, across all durations of daytime temperature recordings lasting 1 hour or more. Survival analysis showcased 1-h daytime proximal skin temperature time-series to be significant predictors of survival in cirrhosis, whereby SD2, was found to be independent to the Model for End-Stage Liver Disease (MELD) score and thus, the extent of disease severity. As expected, longer durations of time-series were also predictors of mortality for the majority of the temperature variability indices. Conclusion: Crucially, this study suggests that 1-h proximal skin temperature recordings are sufficient in length to accurately predict 12-month survival in patients with cirrhosis, independent from current prognostic indicators used in the clinic such as MELD.
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Affiliation(s)
- Noor-Ul-Hoda Abid
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
- UCL Medical School, UCL, London, United Kingdom
| | - Travis Lum Cheng In
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | - Matteo Bottaro
- Department of Medicine, University of Padova, Padova, Italy
| | - Xinran Shen
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | - Iker Hernaez Sanz
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | - Satoshi Yoshida
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | | | - Sara Montagnese
- Department of Medicine, University of Padova, Padova, Italy
- Chronobiology Section, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Ali R. Mani
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
- Institute for Liver and Digestive Health (ILDH), Division of Medicine, UCL, London, United Kingdom
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Laghari AA, Sun Y, Alhussein M, Aurangzeb K, Anwar MS, Rashid M. Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection. Sci Rep 2023; 13:15109. [PMID: 37704659 PMCID: PMC10499947 DOI: 10.1038/s41598-023-40343-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.
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Affiliation(s)
- Asif Ali Laghari
- Software College, Shenyang Normal University, Shenyang, 110034, China
| | - Yanqiu Sun
- Liaoning University of Traditional Chinese Medicine, Shenyang, China.
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Kingdom of Saudi Arabia
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Kingdom of Saudi Arabia
| | | | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, 411048, India
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Nonlinear Analyses Distinguish Load Carriage Dynamics in Walking and Standing: A Systematic Review. J Appl Biomech 2022; 38:434-447. [PMID: 36170973 DOI: 10.1123/jab.2022-0062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 08/08/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022]
Abstract
Load carriage experiments are typically performed from a linear perspective that assumes that movement variability is equivalent to error or noise in the neuromuscular system. A complimentary, nonlinear perspective that treats variability as the object of study has generated important results in movement science outside load carriage settings. To date, no systematic review has yet been conducted to understand how load carriage dynamics change from a nonlinear perspective. The goal of this systematic review is to fill that need. Relevant literature was extracted and reviewed for general trends involving nonlinear perspectives on load carriage. Nonlinear analyses that were used in the reviewed studies included sample, multiscale, and approximate entropy; the Lyapunov exponent; fractal analysis; and relative phase. In general, nonlinear tools successfully distinguish between unloaded and loaded conditions in standing and walking, although not in a consistent manner. The Lyapunov exponent and entropy were the most used nonlinear methods. Two noteworthy findings are that entropy in quiet standing studies tends to decrease, whereas the Lyapunov exponent in walking studies tends to increase, both due to added load. Thus, nonlinear analyses reveal altered load carriage dynamics, demonstrating promise in applying a nonlinear perspective to load carriage while also underscoring the need for more research.
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Bottaro M, Abid NUH, El-Azizi I, Hallett J, Koranteng A, Formentin C, Montagnese S, Mani AR. Skin temperature variability is an independent predictor of survival in patients with cirrhosis. Physiol Rep 2021; 8:e14452. [PMID: 32562383 PMCID: PMC7305245 DOI: 10.14814/phy2.14452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/22/2020] [Accepted: 04/27/2020] [Indexed: 12/18/2022] Open
Abstract
Background Cirrhosis is a disease with multisystem involvement. It has been documented that patients with cirrhosis exhibit abnormal patterns of fluctuation in their body temperature. However, the clinical significance of this phenomenon is not well understood. The aim of this study was to determine if temperature variability analysis can predict survival in patients with cirrhosis. Methods Thirty eight inpatients with cirrhosis were enrolled in the study. Wireless temperature sensors were used to record patients’ proximal skin temperature for 24 hr. The pattern of proximal temperature fluctuation was assessed using the extended Poincaré plot to measure short‐term and long‐term proximal temperature variability (PTV). Patients were followed up for 12 months, and information was collected on the occurrence of death/liver transplantation. Results During the follow‐up period, 15 patients (39%) died or underwent transplantation for hepatic decompensation. Basal proximal skin temperature absolute values were comparable in survivors and nonsurvivors. However, nonsurvivors showed a significant reduction in both short‐term and long‐term HRV indices. Cox regression analysis showed that both short‐term and long‐term PTV indices could predict survival in these patients. However, only measures of short‐term PTV were shown to be independent of the severity of hepatic failure in predicting survival. Finally, the prognostic value of short‐term PTV was also independent of heart rate variability, that is, a measure of autonomic dysfunction. Conclusion Changes in the pattern of patients’ temperature fluctuations, rather than their absolute values, hold key prognostic information, suggesting that impaired thermoregulation may play an important role in the pathophysiology of cirrhosis.
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Affiliation(s)
- Matteo Bottaro
- Department of Medicine, University of Padova, Padova, Italy
| | | | - Ilias El-Azizi
- Network Physiology Lab, Division of Medicine, UCL, London, UK
| | - Joseph Hallett
- Network Physiology Lab, Division of Medicine, UCL, London, UK
| | - Anita Koranteng
- Network Physiology Lab, Division of Medicine, UCL, London, UK
| | | | | | - Ali R Mani
- Network Physiology Lab, Division of Medicine, UCL, London, UK
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Mayor D, Panday D, Kandel HK, Steffert T, Banks D. CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals. ENTROPY 2021; 23:e23030321. [PMID: 33800469 PMCID: PMC7998823 DOI: 10.3390/e23030321] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. METHODS Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. RESULTS The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. CONCLUSIONS We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.
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Affiliation(s)
- David Mayor
- School of Health and Social Work, University of Hertfordshire, Hatfield AL10 9AB, UK
- Correspondence:
| | - Deepak Panday
- School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Hari Kala Kandel
- Department of Computing, Goldsmiths College, University of London, New Cross, London SE14 6NW, UK;
| | - Tony Steffert
- MindSpire, Napier House, 14-16 Mount Ephraim Rd, Tunbridge Wells TN1 1EE, UK;
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
| | - Duncan Banks
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
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Cuesta-Frau D, Dakappa PH, Mahabala C, Gupta AR. Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1034. [PMID: 33286803 PMCID: PMC7597093 DOI: 10.3390/e22091034] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 11/16/2022]
Abstract
Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.
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Affiliation(s)
- David Cuesta-Frau
- Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain
| | | | - Chakrapani Mahabala
- Department of Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal 575001, India; (C.M.); (A.R.G.)
| | - Arjun R. Gupta
- Department of Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal 575001, India; (C.M.); (A.R.G.)
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Culver A, Coiffard B, Antonini F, Duclos G, Hammad E, Vigne C, Mege JL, Baumstarck K, Boucekine M, Zieleskiewicz L, Leone M. Circadian disruption of core body temperature in trauma patients: a single-center retrospective observational study. J Intensive Care 2020; 8:4. [PMID: 31921428 PMCID: PMC6945723 DOI: 10.1186/s40560-019-0425-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 12/29/2019] [Indexed: 01/14/2023] Open
Abstract
Background Circadian clock alterations were poorly reported in trauma patients, although they have a critical role in human physiology. Core body temperature is a clinical variable regulated by the circadian clock. Our objective was to identify the circadian temperature disruption in trauma patients and to determine whether these disruptions were associated with the 28-day mortality rate. Methods A retrospective and observational single-center cohort study was conducted. All adult severe trauma patients admitted to the intensive care unit of Aix Marseille University, North Hospital, from November 2013 to February 2018, were evaluated. The variations of core body temperature for each patient were analyzed between days 2 and 3 after intensive care unit admission. Core body temperature variations were defined by three parameters: mesor, amplitude, and period. A logistic regression model was used to determine the variables influencing these three parameters. A survival analysis was performed assessing the association between core body temperature rhythm disruption and 28-day mortality rate. A post hoc subgroup analysis focused on the patients with head trauma. Results Among the 1584 screened patients, 248 were included in this study. The period differed from 24 h in 177 (71%) patients. The mesor value (°C) was associated with body mass index and ketamine use. Amplitude (°C) was associated with ketamine use only. The 28-day mortality rate was 18%. For all trauma patients, age, body mass index, intracranial hypertension, and amplitude were independent risk factors. The patients with a mesor value < 36.9 °C (p < 0.001) and an amplitude > 0.6 °C (p < 0.001) had a higher 28-day mortality rate. Among the patients with head trauma, mesor and amplitude were identified as independent risk factors (HR = 0.40, 95% CI [0.23–0.70], p = 0.001 and HR = 4.73, 95% CI [1.38–16.22], p = 0.01). Conclusions Our results highlight an association between core body temperature circadian alteration and 28-day mortality rate. This association was more pronounced in the head trauma patients than in the non-head trauma patients. Further studies are needed to show a causal link and consider possible interventions.
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Affiliation(s)
- Aurélien Culver
- Service d'Anesthésie et de Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Chemin des Bourrely, 13915 Marseille, France
| | - Benjamin Coiffard
- Médecine Intensive - Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Marseille, France.,3CNRS, IRD, MEPHI, IHU Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - François Antonini
- Service d'Anesthésie et de Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Chemin des Bourrely, 13915 Marseille, France
| | - Gary Duclos
- Service d'Anesthésie et de Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Chemin des Bourrely, 13915 Marseille, France
| | - Emmanuelle Hammad
- Service d'Anesthésie et de Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Chemin des Bourrely, 13915 Marseille, France
| | - Coralie Vigne
- Service d'Anesthésie et de Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Chemin des Bourrely, 13915 Marseille, France
| | - Jean-Louis Mege
- 3CNRS, IRD, MEPHI, IHU Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - Karine Baumstarck
- 4APHM, EA 3279 CEReSS, School of Medicine - La Timone Medical Campus, Health Service Research and Quality of Life Center, Aix Marseille Université, Marseille, France
| | - Mohamed Boucekine
- 4APHM, EA 3279 CEReSS, School of Medicine - La Timone Medical Campus, Health Service Research and Quality of Life Center, Aix Marseille Université, Marseille, France
| | - Laurent Zieleskiewicz
- Service d'Anesthésie et de Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Chemin des Bourrely, 13915 Marseille, France
| | - Marc Leone
- Service d'Anesthésie et de Réanimation, APHM, Hôpital Nord, Aix Marseille Université, Chemin des Bourrely, 13915 Marseille, France.,3CNRS, IRD, MEPHI, IHU Méditerranée Infection, Aix Marseille Université, Marseille, France
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Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy. ENTROPY 2019. [PMCID: PMC7514234 DOI: 10.3390/e21101013] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear IRn mapping into IR, where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures.
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Papaioannou VE, Sertaridou EN, Chouvarda IG, Kolios GC, Pneumatikos IN. Determining rhythmicity and determinism of temperature curves in septic and non-septic critically ill patients through chronobiological and recurrence quantification analysis: a pilot study. Intensive Care Med Exp 2019; 7:53. [PMID: 31486940 PMCID: PMC6728111 DOI: 10.1186/s40635-019-0267-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 08/27/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND A few studies have demonstrated that critically ill patients exhibit circadian deregulation and reduced complexity of different time series, such as temperature. RESULTS In this prospective study, we enrolled 21 patients divided into three groups: group A (N = 10) included subjects who had septic shock at the time of ICU entry, group B (N = 6) included patients who developed septic shock during ICU stay, and group C consisted of 5 non-septic critically ill patients. Core body temperature (CBT) was recorded for 24 h at a rate of one sample per hour (average of CBT for that hour) and during different occasions: upon ICU entry and exit in groups A and C and upon entry, septic shock development, and exit in group B. Markers of circadian rhythmicity included mean values, amplitude that is the difference between peak and mean values, and peak time. Furthermore, recurrence quantification analysis (RQA) was employed for assessing different markers of complexity of temperature signals. Patients from group C exhibited higher temperature amplitude upon entry (0.45 ± 0.19) in relation with both groups A (0.28 ± 0.18, p < 0.05) and B (0.32 ± 0.13, p < 0.05). Circadian features did not differ within all groups. Temperature amplitude in groups B and C upon entry was negatively correlated with SAPS II (r = - 0.72 and - 0.84, p < 0.003) and APACHE II scores (r = - 0.70 and - 0.63, p < 0.003), respectively, as well as duration of ICU and hospital stay in group B (r = - 0.62 and - 0.64, p < 0.003) and entry SOFA score in group C (r = - 0.82, p < 0.003). Increased periodicity of CBT was found for all patients upon exit related to entry in the ICU. Different RQA features indicating periodic patterns of change of entry CBT were negatively correlated with severity of disease and length of ICU stay for all patients. CONCLUSIONS Increased temperature rhythmicity during ICU entry was related with lower severity of disease and better clinical outcomes, whereas the more deterministic CBT patterns were found in less critically ill patients with shorter ICU stay.
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Affiliation(s)
- Vasilios E Papaioannou
- Intensive Care Unit, Alexandroupolis University Hospital, Democritus University of Thrace, Dragana, 68100, Alexandroupolis, Greece
| | - Eleni N Sertaridou
- Intensive Care Unit, Alexandroupolis University Hospital, Democritus University of Thrace, Dragana, 68100, Alexandroupolis, Greece.
| | - Ioanna G Chouvarda
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, Faculty of Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - George C Kolios
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Dragana, 68100, Alexandroupolis, Greece
| | - Ioannis N Pneumatikos
- Intensive Care Unit, Alexandroupolis University Hospital, Democritus University of Thrace, Dragana, 68100, Alexandroupolis, Greece
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Cuesta-Frau D, Miró-Martínez P, Oltra-Crespo S, Jordán-Núñez J, Vargas B, González P, Varela-Entrecanales M. Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. ENTROPY 2018; 20:e20110853. [PMID: 33266577 PMCID: PMC7512415 DOI: 10.3390/e20110853] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 10/31/2018] [Accepted: 11/05/2018] [Indexed: 11/16/2022]
Abstract
Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce subpar results and not even reach the desired level of significance. In order to increase the signal classification accuracy in these suboptimal situations, this paper proposes statistical models created with uncorrelated measures that exploit the possible synergies between them. The methods employed are permutation entropy (PE), approximate entropy (ApEn), and sample entropy (SampEn). Since PE is based on subpattern ordinal differences, whereas ApEn and SampEn are based on subpattern amplitude differences, we hypothesized that a combination of PE with another method would enhance the individual performance of any of them. The dataset was composed of body temperature records, for which we did not obtain a classification accuracy above 80% with a single measure, in this study or even in previous studies. The results confirmed that the classification accuracy rose up to 90% when combining PE and ApEn with a logistic model.
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Affiliation(s)
- David Cuesta-Frau
- Technological Institute of Informatics, Universitat Politècnica de València, 03801 Alcoi Campus, Spain
- Correspondence: ; Tel.: +34-96-652-8505
| | - Pau Miró-Martínez
- Department of Statistics, Universitat Politècnica de València, 03801 Alcoi Campus, Spain
| | - Sandra Oltra-Crespo
- Technological Institute of Informatics, Universitat Politècnica de València, 03801 Alcoi Campus, Spain
| | - Jorge Jordán-Núñez
- Department of Statistics, Universitat Politècnica de València, 03801 Alcoi Campus, Spain
| | - Borja Vargas
- Internal Medicine Department, Teaching Hospital of Móstoles, 28935 Madrid, Spain
| | - Paula González
- Internal Medicine Department, Teaching Hospital of Móstoles, 28935 Madrid, Spain
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Mani AR, Mazloom R, Haddadian Z, Montagnese S. Body temperature fluctuation analysis in cirrhosis. Liver Int 2018; 38:378-379. [PMID: 28782164 DOI: 10.1111/liv.13539] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Ali R Mani
- Division of Medicine, University College London, London, UK
| | - Roham Mazloom
- Department of Physiology, Alborz University of Medical Sciences, Alborz, Iran
| | - Zahra Haddadian
- Department of Physiology, Alborz University of Medical Sciences, Alborz, Iran
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A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:5707162. [PMID: 29359037 PMCID: PMC5735677 DOI: 10.1155/2017/5707162] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Accepted: 10/31/2017] [Indexed: 11/24/2022]
Abstract
Diagnosis of undifferentiated fever is a major challenging task to the physician which often remains undiagnosed and delays the treatment. The aim of the study was to record and analyze a 24-hour continuous tympanic temperature and evaluate its utility in the diagnosis of undifferentiated fevers. This was an observational study conducted in the Kasturba Medical College and Hospitals, Mangaluru, India. A total of ninety-six (n = 96) patients were presented with undifferentiated fever. Their tympanic temperature was recorded continuously for 24 hours. Temperature data were preprocessed and various signal characteristic features were extracted and trained in classification machine learning algorithms using MATLAB software. The quadratic support vector machine algorithm yielded an overall accuracy of 71.9% in differentiating the fevers into four major categories, namely, tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases. The area under ROC curve for tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases was found to be 0.961, 0.801, 0.815, and 0.818, respectively. Good agreement was observed [kappa = 0.618 (p < 0.001, 95% CI (0.498–0.737))] between the actual diagnosis of cases and the quadratic support vector machine learning algorithm. The 24-hour continuous tympanic temperature recording with supervised machine learning algorithm appears to be a promising noninvasive and reliable diagnostic tool.
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Liu X, Li C, Zhang L, Shi X, Wu S. Personalized Identification of Differentially Expressed Modules in Osteosarcoma. Med Sci Monit 2017; 23:774-779. [PMID: 28190021 PMCID: PMC5319443 DOI: 10.12659/msm.899638] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Osteosarcoma (OS), an aggressive malignant neoplasm, is the most common primary bone cancer mainly in adolescents and young adults. Differentially expressed modules tend to distinguish differences integrally. Identifying modules individually has been crucial for understanding OS mechanisms and applications of custom therapeutic decisions in the future. MATERIAL AND METHODS Samples came from individuals were used from control group (n=15) and OS group (n=84). Based on clique-merging, module-identification algorithm was used to identify modules from OS PPI networks. A novel approach - the individualized module aberrance score (iMAS) was performed to distinguish differences, making special use of accumulated normal samples (ANS). We performed biological process ontology to classify functionally modules. Then Support Vector Machine (SVM) was used to test distribution results of normal and OS group with screened modules. RESULTS We identified 83 modules containing 2084 genes from PPI network in which 61 modules were significantly different. Cluster analysis of OS using the iMAS method identified 5 modules clusters. Specificity=1.00 and Sensitivity=1.00 proved the distribution outcomes of screened modules were mainly consistent with that of total data, which suggested the efficiency of 61 modules. CONCLUSIONS We conclude that a novel pipeline that identified the dysregulated modules in individuals of OS. The constructed process is expected to aid in personalized health care, which may present fruitful strategies for medical therapy.
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Affiliation(s)
- Xiaozhou Liu
- Department of Orthopedics, Jinling Hospital affiliated to Nanjing University, Nanjing, Jiangsu, China (mainland)
| | - Chengjun Li
- Department of Orthopedics, Jinling Hospital affiliated to Nanjing University, Nanjing, Jiangsu, China (mainland)
| | - Lei Zhang
- Department of Orthopedics, Jinling Hospital affiliated to Nanjing University, Nanjing, Jiangsu, China (mainland)
| | - Xin Shi
- Department of Orthopedics, Jinling Hospital affiliated to Nanjing University, Nanjing, Jiangsu, China (mainland)
| | - Sujia Wu
- Department of Orthopedics, Jinling Hospital affiliated to Nanjing University, Nanjing, Jiangsu, China (mainland)
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Kiss F, Molnar L, Hajdu E, Deak A, Molnar A, Berhes M, Szabo J, Nemeth N, Fulesdi B. Skin microcirculatory changes reflect early the circulatory deterioration in a fulminant sepsis model in the pig. Acta Cir Bras 2015; 30:470-7. [DOI: 10.1590/s0102-865020150070000004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2015] [Accepted: 06/10/2015] [Indexed: 02/04/2023] Open
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Ma Y, Sun S, Peng CK. Applications of dynamical complexity theory in traditional Chinese medicine. Front Med 2014; 8:279-84. [PMID: 25204292 DOI: 10.1007/s11684-014-0367-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 08/06/2014] [Indexed: 10/24/2022]
Abstract
Traditional Chinese medicine (TCM) has been gradually accepted by the world. Despite its widespread use in clinical settings, a major challenge in TCM is to study it scientifically. This difficulty arises from the fact that TCM views human body as a complex dynamical system, and focuses on the balance of the human body, both internally and with its external environment. As a result, conventional tools that are based on reductionist approach are not adequate. Methods that can quantify the dynamics of complex integrative systems may bring new insights and utilities about the clinical practice and evaluation of efficacy of TCM. The dynamical complexity theory recently proposed and its computational algorithm, Multiscale Entropy (MSE) analysis, are consistent with TCM concepts. This new system level analysis has been successfully applied to many health and disease related topics in medicine. We believe that there could be many promising applications of this dynamical complexity concept in TCM. In this article, we propose some promising applications and research areas that TCM practitioners and researchers can pursue.
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Affiliation(s)
- Yan Ma
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
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