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Zhang N, Zhai Y, Li Y, Zhou J, Zhai M, Tang C, Xie K. Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study. PLoS One 2024; 19:e0305872. [PMID: 39074072 DOI: 10.1371/journal.pone.0305872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 06/05/2024] [Indexed: 07/31/2024] Open
Abstract
In the analysis of electroencephalography (EEG), entropy can be used to quantify the rate of generation of new information. Entropy has long been known to suffer from variance that arises from its calculation. From a sensor's perspective, calculation of entropy from a period of EEG recording can be treated as physical measurement, which suffers from measurement noise. We showed the feasibility of using Kalman filtering to reduce the variance of entropy for simulated signals as well as real-world EEG recordings. In addition, we also manifested that Kalman filtering was less time-consuming than moving average, and had better performance than moving average and exponentially weighted moving average. In conclusion, we have treated entropy as a physical measure and successfully applied the conventional Kalman filtering with fixed hyperparameters. Kalman filtering is expected to be used to reduce measurement noise when continuous entropy estimation (for example anaesthesia monitoring) is essential with high accuracy and low time-consumption.
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Affiliation(s)
- Nan Zhang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Yawen Zhai
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Yan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Jiayu Zhou
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Mingming Zhai
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Chi Tang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Kangning Xie
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
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Lucero-Orozco NB, Reyes-Lagos JJ, Ortíz-Pedroza MDR, Talavera-Peña AK, Abarca-Castro EA, Mendieta-Zerón H, Pliego-Carrillo AC, Rodríguez-Arce J, Zúñiga-Avilés LA, Santiago-Fuentes LM, Ledesma-Ramírez CI, Peña-Castillo MÁ. Analysis of fetal heart rate fluctuations in women diagnosed with preeclampsia during the latent phase of labor. Front Physiol 2024; 15:1340441. [PMID: 38846420 PMCID: PMC11154906 DOI: 10.3389/fphys.2024.1340441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/19/2024] [Indexed: 06/09/2024] Open
Abstract
Introduction Fetal heart rate variability (fHRV) is a tool used to investigate the functioning of the fetal autonomic nervous system. Despite the significance of preeclampsia, fHRV during the latent phase of labor has not been extensively studied. This study aimed to evaluate fetal cardiac autonomic activity by using linear and nonlinear indices of fHRV analysis in women diagnosed with preeclampsia without hypertensive treatment during gestation, compared to normotensive women during the latent phase of labor. Methods A cross-sectional and exploratory study was conducted among pregnant women in the latent phase of labor, forming three study groups: normotensive or control (C, 38.8 ± 1.3 weeks of pregnancy, n = 22), preeclampsia with moderate features (P, 37.6 ± 1.4 weeks of pregnancy n = 10), and preeclampsia with severe features (SP, 36.9 ± 1.2 weeks of pregnancy, n = 12). None of the participants received anti-hypertensive treatment during their pregnancy. Linear and nonlinear features of beat-to-beat fHRV, including temporal, frequency, symbolic dynamics, and entropy measures, were analyzed to compare normotensive and preeclamptic groups. Results Significantly lower values of multiscale entropy (MSE) and short-term complexity index (Ci) were observed in the preeclamptic groups compared to the C group (p < 0.05). Additionally, higher values of SDNN (standard deviation of R-R intervals) and higher values of low-frequency power (LF) were found in the P group compared to the C group. Conclusion Our findings indicate that changes in the complexity of fetal heart rate fluctuations may indicate possible disruptions in the autonomic nervous system of fetuses in groups affected by undiagnosed preeclampsia during pregnancy. Reduced complexity and shifts in fetal autonomic cardiac activity could be associated with preeclampsia's pathophysiological mechanisms during the latent phase of labor.
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Affiliation(s)
- Nancy B. Lucero-Orozco
- División de Ciencias Básicas e Ingeniería, Universidad Autónoma Metropolitana-Iztapalapa (UAM-I), Ciudad de México, Mexico
| | | | - María del Rocío Ortíz-Pedroza
- División de Ciencias Básicas e Ingeniería, Universidad Autónoma Metropolitana-Iztapalapa (UAM-I), Ciudad de México, Mexico
| | - Ana Karen Talavera-Peña
- Departamento de Ciencias de la Salud, Universidad Autónoma Metropolitana-Lerma (UAM-L), Lerma de Villada, Mexico
| | - Eric Alonso Abarca-Castro
- Departamento de Ciencias de la Salud, Universidad Autónoma Metropolitana-Lerma (UAM-L), Lerma de Villada, Mexico
| | - Hugo Mendieta-Zerón
- Facultad de Medicina, Universidad Autónoma del Estado de México (UAEMéx), Toluca, Mexico
| | | | - Jorge Rodríguez-Arce
- Facultad de Ingeniería, Universidad Autónoma del Estado de México (UAEMéx), Toluca, Mexico
| | - Luis Adrián Zúñiga-Avilés
- Facultad de Medicina, Universidad Autónoma del Estado de México (UAEMéx), Toluca, Mexico
- Facultad de Ingeniería, Universidad Autónoma del Estado de México (UAEMéx), Toluca, Mexico
| | - Laura Mercedes Santiago-Fuentes
- Facultad de Medicina, Universidad Autónoma del Estado de México (UAEMéx), Toluca, Mexico
- Departamento de Ciencias de la Salud, Universidad Autónoma Metropolitana-Iztapalapa (UAM-I), Iztapalapa, Mexico
| | | | - Miguel Ángel Peña-Castillo
- División de Ciencias Básicas e Ingeniería, Universidad Autónoma Metropolitana-Iztapalapa (UAM-I), Ciudad de México, Mexico
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Averna A, Coelli S, Ferrara R, Cerutti S, Priori A, Bianchi AM. Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease. J Neural Eng 2023; 20:051001. [PMID: 37746822 DOI: 10.1088/1741-2552/acf8fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 09/12/2023] [Indexed: 09/26/2023]
Abstract
Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.
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Affiliation(s)
- Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Rosanna Ferrara
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Sergio Cerutti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Alberto Priori
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Zhao X, Gong Y, Xu L, Xia L, Zhang J, Zheng D, Yao Z, Zhang X, Wei H, Jiang J, Liu H, Mao J. Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13061-13085. [PMID: 37501478 DOI: 10.3934/mbe.2023582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
PURPOSE Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD. AIM To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD. METHODS Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme. RESULTS ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8. CONCLUSIONS Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.
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Affiliation(s)
- Xiaoye Zhao
- School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China
- School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China
- Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan 750001, Ningxia, China
| | - Yinlan Gong
- Institute of Wenzhou, Zhejiang University, Wenzhou 325000, Zhejiang, China
| | - Lihua Xu
- Hangzhou Linghua Biotech Ltd, Hangzhou 310009, Zhejiang, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Hangzhou 310009, Zhejiang, China
- Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310009, Zhejiang, China
| | - Jucheng Zhang
- Department of Clinical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, United Kingdom
| | - Zongbi Yao
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan 750021, Ningxia, China
| | - Xinjie Zhang
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan 750021, Ningxia, China
| | - Haicheng Wei
- School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China
| | - Jun Jiang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, United Kingdom
| | - Jiandong Mao
- School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China
- School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China
- Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan 750001, Ningxia, China
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Dynamic touch induces autonomic changes in preterm infants as measured by changes in heart rate variability. Brain Res 2023; 1799:148169. [PMID: 36410429 DOI: 10.1016/j.brainres.2022.148169] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/29/2022] [Accepted: 11/14/2022] [Indexed: 11/22/2022]
Abstract
Preterm birth significantly increases the risk of developing various long-term health problems and developmental disabilities. While touch is a crucial component of many perinatal care strategies, the neurobiological underpinnings are rarely considered. C-tactile fibers (CTs) are unmyelinated nerve fibers that are activated by low-force, dynamic touch. Touch directed specifically at CTs activates the posterior insular cortex, consistent with an interoceptive function, and has been shown to reduce heart rate and increase oxygen saturation. The current research compared the effect of five minutes of CT optimal velocity stroking touch versus five minutes of static touch on autonomic markers of preterm infants between 28 and 37 weeks gestational age. CT touch induces a higher increase in heart rate variability metrics related to the parasympathetic system, which persisted for a 5-minute post-touch period. Conversely, there was no such increase in infants receiving static touch. The present findings confirmed that CTs signal the affective quality of nurturing touch, thereby arguing an additional neurobiological substrate for the evident valuable impacts of neonatal tactile interventions and improving the effectiveness of such interventions.
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Azami H, Sanei S, Rajji TK. Ensemble entropy: A low bias approach for data analysis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Zuo X, Zhang C, Hämäläinen T, Gao H, Fu Y, Cong F. Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1281. [PMID: 36141167 PMCID: PMC9497745 DOI: 10.3390/e24091281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/04/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human-computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper, we propose a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network. Features including approximate entropy (AE), fuzzy entropy (FE), Rényi entropy (RE), differential entropy (DE), and multi-scale entropy (MSE) are first calculated to study dynamic emotional information. Then, we train a BiLSTM classifier with the inputs of entropy features to identify different emotions. Our results show that MSE of EEG is more efficient than other single-entropy features in recognizing emotions. The performance of BiLSTM is further improved with an accuracy of 70.05% using fused entropy features compared with that of single-type feature.
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Affiliation(s)
- Xin Zuo
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Chi Zhang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
- Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian 116024, China
| | - Timo Hämäläinen
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Hanbing Gao
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Yu Fu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
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8
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Manzotti A, Cerritelli F, Lombardi E, Monzani E, Savioli L, Esteves JE, Galli M, La Rocca S, Biasi P, Chiera M, Lista G. Osteopathic Manipulative Treatment Regulates Autonomic Markers in Preterm Infants: A Randomized Clinical Trial. Healthcare (Basel) 2022; 10:813. [PMID: 35627950 PMCID: PMC9141319 DOI: 10.3390/healthcare10050813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
Osteopathic manipulative treatment (OMT) has been found to be effective in the context of premature infants. Nonetheless, no studies have investigated the immediate effects of OMT on heart rate variability (HRV). As altered HRV reflects poor or worsening newborn's clinical conditions and neurodevelopment, should OMT improve HRV fluctuations, it could become a relevant intervention for improving the care of preterm newborns. Therefore, this study aimed to evaluate whether OMT could affect HRV. The study was carried out at the Buzzi Hospital in Milan. From the neonatal intensive care unit, ninety-six preterm infants (41 males) were enrolled and were randomly assigned to one of two treatment groups: OMT or Static Touch. The infants were born at 33.5 weeks (±4.3) and had a mean birth weight of 2067 g (±929). The study had as primary outcome the change in the beat-to-beat variance in heart rate measured through root mean square of consecutive RR interval differences (RMSSD); other metrics were used as secondary and exploratory analyses. Despite the lack of statistically significant results regarding the primary outcomeand some study limitations, compared to static touch, OMT seemed to favor a parasympathetic modulation and improved HRV, which could reflect improvement in newborn's clinical conditions and development.
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Affiliation(s)
- Andrea Manzotti
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
- Division of Neonatology, “V. Buzzi” Children’s Hospital, ASST-FBF-Sacco, 20157 Milan, Italy;
- Research Department, SOMA, Istituto Osteopatia Milano, 20126 Milan, Italy
| | - Francesco Cerritelli
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
| | - Erica Lombardi
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
- Research Department, SOMA, Istituto Osteopatia Milano, 20126 Milan, Italy
| | - Elena Monzani
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
| | - Luca Savioli
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
| | - Jorge E. Esteves
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
- Research Department, Malta ICOM Educational, GZR 1071 Gzira, Malta
| | - Matteo Galli
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
- Research Department, SOMA, Istituto Osteopatia Milano, 20126 Milan, Italy
| | - Simona La Rocca
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
- Research Department, SOMA, Istituto Osteopatia Milano, 20126 Milan, Italy
| | - Pamela Biasi
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
- Research Department, SOMA, Istituto Osteopatia Milano, 20126 Milan, Italy
| | - Marco Chiera
- RAISE Lab, Foundation COME Collaboration, 65121 Pescara, Italy; (A.M.); (E.L.); (E.M.); (L.S.); (J.E.E.); (M.G.); (S.L.R.); (P.B.); (M.C.)
| | - Gianluca Lista
- Division of Neonatology, “V. Buzzi” Children’s Hospital, ASST-FBF-Sacco, 20157 Milan, Italy;
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Theeranaew W, Wang F, Ghasia FF, Wilmot G, Shaikh AG. Gaze-holding and anti-GAD antibody: prototypic heterogeneous motor dysfunction in immune disease. CEREBELLUM (LONDON, ENGLAND) 2022; 21:55-63. [PMID: 33977497 DOI: 10.1007/s12311-021-01272-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
The variability in motor dysfunction is not uncommon in autoimmune disorders. Antibody-mediated system-wide malfunction or effects on the neural network shared by two independent pathophysiological processes can cause such heterogeneity. We tested this prediction for motor dysfunction during gaze holding in 11 patients with increased titers of glutamic acid decarboxylase (anti-GAD) antibody. High-resolution oculography measured horizontal and vertical eye positions. The analysis was performed with customized signal processing algorithms. Downbeat and gaze-evoked nystagmus commonly coexisted; one patient had a combination of upbeat and gaze-evoked nystagmus. The nystagmus was associated with saccadic intrusions in 10 patients; all had squarewaves, but five also had saccadic oscillations. The nystagmus and saccadic intrusions, both in the same axis of eye rotations, were not uncommon. Tandem appearance of the episodes of nystagmus and saccadic intrusions suggested a coupling between the two abnormalities. We speculated a unifying framework where the anti-GAD antibody inhibited (GAD mediated) conversion of glutamate to gamma-aminobutyric acid (GABA). Paucity GABA and excess of glutamate cause nystagmus (less GABA) and high-frequency saccadic oscillations (excessive glutamate).
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Affiliation(s)
- Wanchat Theeranaew
- Department of Neurology, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44110, USA
| | - Fajun Wang
- Department of Neurology, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44110, USA
| | | | - George Wilmot
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Aasef G Shaikh
- Department of Neurology, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44110, USA.
- Neurology Service and Daroff-Dell' Osso Ocular Motility Laboratory, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
- Departments of Neurology and Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
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Ribeiro M, Monteiro-Santos J, Castro L, Antunes L, Costa-Santos C, Teixeira A, Henriques TS. Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review. Front Med (Lausanne) 2021; 8:661226. [PMID: 34917624 PMCID: PMC8669823 DOI: 10.3389/fmed.2021.661226] [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: 01/30/2021] [Accepted: 11/04/2021] [Indexed: 12/19/2022] Open
Abstract
The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.
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Affiliation(s)
- Maria Ribeiro
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.,Computer Science Department, Faculty of Sciences, University of Porto, Porto, Portugal
| | - João Monteiro-Santos
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Luísa Castro
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal.,School of Health of Polytechnic of Porto, Porto, Portugal
| | - Luís Antunes
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.,Computer Science Department, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Cristina Costa-Santos
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Andreia Teixeira
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal.,Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
| | - Teresa S Henriques
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
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11
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Ponsiglione AM, Cosentino C, Cesarelli G, Amato F, Romano M. A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6136. [PMID: 34577342 PMCID: PMC8469481 DOI: 10.3390/s21186136] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/04/2021] [Accepted: 09/10/2021] [Indexed: 02/07/2023]
Abstract
The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.
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Affiliation(s)
- Alfonso Maria Ponsiglione
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.M.P.); (F.A.)
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine ‘Gaetano Salvatore’, University Magna Graecia of Catanzaro, Viale Tommaso Campanella 185, 88100 Catanzaro, Italy;
| | - Giuseppe Cesarelli
- Department of Chemical, Materials and Production Engineering (DICMaPI), University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy;
| | - Francesco Amato
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.M.P.); (F.A.)
| | - Maria Romano
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.M.P.); (F.A.)
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12
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Nieto-del-Amor F, Beskhani R, Ye-Lin Y, Garcia-Casado J, Diaz-Martinez A, Monfort-Ortiz R, Diago-Almela VJ, Hao D, Prats-Boluda G. Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. SENSORS 2021; 21:s21186071. [PMID: 34577278 PMCID: PMC8471282 DOI: 10.3390/s21186071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/06/2021] [Accepted: 09/08/2021] [Indexed: 11/16/2022]
Abstract
One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.
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Affiliation(s)
- Félix Nieto-del-Amor
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Raja Beskhani
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
- Correspondence:
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Alba Diaz-Martinez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
| | - Rogelio Monfort-Ortiz
- Servicio de Obstetricia, H.U.P. La Fe, 46026 Valencia, Spain; (R.M.-O.); (V.J.D.-A.)
| | | | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China;
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (F.N.-d.-A.); (R.B.); (J.G.-C.); (A.D.-M.); (G.P.-B.)
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13
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Vargas-Calixto J, Warrick P, Kearney R. Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures. Front Artif Intell 2021; 4:674238. [PMID: 34490419 PMCID: PMC8417534 DOI: 10.3389/frai.2021.674238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 07/27/2021] [Indexed: 11/20/2022] Open
Abstract
Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03–0.15 Hz), movement frequency power (MF: 0.15–0.5 Hz), high frequency power (HF: 0.5–1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.
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Affiliation(s)
| | - Philip Warrick
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.,PeriGen Inc., Montreal, QC, Canada
| | - Robert Kearney
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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14
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Zeng R, Lu Y, Long S, Wang C, Bai J. Cardiotocography signal abnormality classification using time-frequency features and Ensemble Cost-sensitive SVM classifier. Comput Biol Med 2021; 130:104218. [PMID: 33484945 DOI: 10.1016/j.compbiomed.2021.104218] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/10/2021] [Accepted: 01/11/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Cardiotocography (CTG) signal abnormality classification plays an important role in the diagnosis of abnormal fetuses. This classification problem is made difficult by the non-stationary nature of CTG and the dataset imbalance. This paper introduces a novel application of Time-frequency (TF) features and Ensemble Cost-sensitive Support Vector Machine (ECSVM) classifier to tackle these problems. METHODS Firstly, CTG signals are converted into TF-domain representations by Continuous Wavelet Transform (CWT), Wavelet Coherence (WTC), and Cross-wavelet Transform (XWT). From these representations, a novel image descriptor is used to extract the TF features. Then, the linear feature is derived from the time-domain representation of the CTG signal. The linear and TF features are fed to the ECSVM classifier for prediction and classification of fetal outcome. RESULTS The TF features show the significant difference (p-value<0.05) in distinguishing abnormal CTG signals, but not for traditional nonlinear features. In ECSVM abnormality classification, using only linear features, the sensitivity, specificity, and quality index are 59.3%, 78.3%, and 68.1%, respectively, whereas more effective results (sensitivity: 85.2%, specificity: 66.1%, and quality index: 75.0%) are obtained using a combination of linear and TF features, with a performance improvement index of 10.1%. Especially, the area under the receiver operating characteristic curve (0.77 vs. 0.64) is significantly increased with the ECSVM vs. SVM. CONCLUSION Our method can greatly improve the classification results, especially for sensitivity. It improves the true positive rate of CTG abnormality classification and reduces the false positive rate, which may help detect and treat abnormal fetuses during labor.
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Affiliation(s)
- Rongdan Zeng
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaosheng Lu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Shun Long
- Department of Computer Science, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Chuan Wang
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jieyun Bai
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China.
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15
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Chiera M, Cerritelli F, Casini A, Barsotti N, Boschiero D, Cavigioli F, Corti CG, Manzotti A. Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review. Front Neurosci 2020; 14:561186. [PMID: 33071738 PMCID: PMC7544983 DOI: 10.3389/fnins.2020.561186] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/28/2020] [Indexed: 12/18/2022] Open
Abstract
Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby’s overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby’s stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant’s HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant’s clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients’ health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.
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Affiliation(s)
- Marco Chiera
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Research Commission on Manual Therapies and Mind-Body Disciplines, Societ Italiana di Psico Neuro Endocrino Immunologia, Rome, Italy
| | - Francesco Cerritelli
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy
| | - Alessandro Casini
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy
| | - Nicola Barsotti
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Research Commission on Manual Therapies and Mind-Body Disciplines, Societ Italiana di Psico Neuro Endocrino Immunologia, Rome, Italy
| | | | - Francesco Cavigioli
- Neonatal Intensive Care Unit, "V. Buzzi" Children's Hospital, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy
| | - Carla G Corti
- Pediatric Cardiology Unit-Pediatric Department, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy
| | - Andrea Manzotti
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Neonatal Intensive Care Unit, "V. Buzzi" Children's Hospital, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy.,Research Department, SOMA, Istituto Osteopatia Milano, Milan, Italy
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16
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Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9245421] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.
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17
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Saleem S, Naqvi SS, Manzoor T, Saeed A, ur Rehman N, Mirza J. A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings. Front Physiol 2019; 10:246. [PMID: 30941054 PMCID: PMC6433745 DOI: 10.3389/fphys.2019.00246] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 02/25/2019] [Indexed: 11/13/2022] Open
Abstract
We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.
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Affiliation(s)
- Saqib Saleem
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal, Pakistan
| | - Syed Saud Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Tareq Manzoor
- Energy Research Center, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ahmed Saeed
- School of Computing, Ulster University, Newtownabbey, United Kingdom
| | - Naveed ur Rehman
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Jawad Mirza
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
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18
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Buszko K, Piątkowska A, Koźluk E, Fabiszak T, Opolski G. The complexity of hemodynamic response to the tilt test with and without nitroglycerine provocation in patients with vasovagal syncope. Sci Rep 2018; 8:14554. [PMID: 30266992 PMCID: PMC6162241 DOI: 10.1038/s41598-018-32718-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 09/10/2018] [Indexed: 11/09/2022] Open
Abstract
The paper presents a comparison of vasovagal syndrome occurrence in a head up tilt table test between patients with a positive result of passive tilt test and those with a positive result after pharmacological provocation. The study group consisted of 80 patients: 57 patients who experienced syncope in the passive phase of the test (43 women (aged: 35.6 ± 16.2) and 14 men (aged: 41.7 ± 15.6) and 23 patients who experienced syncope after pharmacological provocation (17 women (age: 32.3 ± 12) and 6 men (age: 43 ± 15). The main investigation was based on the assessment of monitored signals complexity: heart rate, blood pressure and stroke volume. The analysis of complexity in chosen measurement phases was performed with Sample Entropy. The investigation showed that the reactions of autonomic nervous system during tilt test and before syncope are similar for positive result of passive tilt test and positive result of tilt test with provocation. The differences in supine position occurred only in analysis based on impedance measurement (SV: p = 0.01). Significant differences were denoted for all signals just before the syncope (RRI, sBP, dBP: p = 0,00001 and SV: p = 0.01). In analysis of signals complexity the significant differences occurred just before the syncope for Sample Entropy of blood pressure (SampEn (sBP): p = 0.0008, SampEn (dBP): p = 0,0001).
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Affiliation(s)
- Katarzyna Buszko
- Department of Theoretical Foundations of Bio-Medical Science and Medical Informatics, Collegium Medicum, Nicolaus Copernicus University, 85-067, Bydgoszcz, Poland.
| | - Agnieszka Piątkowska
- Department and Clinic of Emergency Medicine, Wroclaw Medical University, Wroclaw, 50-556, Poland
- 1st Chair and Department of Cardiology, Medical University of Warsaw, Warsaw, 02-091, Poland
| | - Edward Koźluk
- 1st Chair and Department of Cardiology, Medical University of Warsaw, Warsaw, 02-091, Poland
| | - Tomasz Fabiszak
- Department of Cardiology and Internal Diseases, Collegium Medicum, Nicolaus Copernicus University, 85-067, Bydgoszcz, Poland
| | - Grzegorz Opolski
- 1st Chair and Department of Cardiology, Medical University of Warsaw, Warsaw, 02-091, Poland
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19
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Zhao Z, Zhang Y, Deng Y. A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State. J Clin Med 2018; 7:jcm7080223. [PMID: 30127256 PMCID: PMC6111566 DOI: 10.3390/jcm7080223] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 11/16/2022] Open
Abstract
Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.
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Affiliation(s)
- Zhidong Zhao
- Hangdian Smart City Research Center of Zhejiang Province, Hangzhou Dianzi University, 310018 Hangzhou, China.
| | - Yang Zhang
- Hangdian Smart City Research Center of Zhejiang Province, Hangzhou Dianzi University, 310018 Hangzhou, China.
| | - Yanjun Deng
- Hangdian Smart City Research Center of Zhejiang Province, Hangzhou Dianzi University, 310018 Hangzhou, China.
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20
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Azami H, Escudero J. Amplitude- and Fluctuation-Based Dispersion Entropy. ENTROPY 2018; 20:e20030210. [PMID: 33265301 PMCID: PMC7512725 DOI: 10.3390/e20030210] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 02/05/2018] [Accepted: 03/13/2018] [Indexed: 11/16/2022]
Abstract
Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel–Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326.
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21
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Garcia-Casado J, Ye-Lin Y, Prats-Boluda G, Mas-Cabo J, Alberola-Rubio J, Perales A. Electrohysterography in the diagnosis of preterm birth: a review. Physiol Meas 2018; 39:02TR01. [PMID: 29406317 DOI: 10.1088/1361-6579/aaad56] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries. OBJECTIVE A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context. APPROACH This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization. MAIN RESULTS Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results. SIGNIFICANCE This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.
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Affiliation(s)
- J Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Camino de Vera SN, 46022, Valencia, Spain
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Granero-Belinchon C, Roux SG, Garnier NB, Abry P, Doret M. Mutual information for intrapartum fetal heart rate analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:2014-2017. [PMID: 29060291 DOI: 10.1109/embc.2017.8037247] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The analysis of the temporal dynamics in intrapartum fetal heart rate (FHR), aiming at early detection of fetal acidosis, constitutes an intricate signal processing task, that continuously receives significant research efforts. Entropy and entropy rates, envisaged as measures of complexity, often computed via popular implementations referred to as Approximate Entropy (ApEn) or Sample Entropy (SampEn), have regularly been reported as significant features for intrapartum FHR analysis. The present contribution aims to show how mutual information enhances characterization of FHR temporal dynamics and improves fetal acidosis detection performance. To that end, mutual information is first connected to ApEn and SampEn both conceptually and with respect to estimation procedure. Second, mutual information, ApEn and SampEn are computed on a large (≃ 1000 subjects) and documented database of FHR data, collected in a French academic hospital. Reported results show that the use of mutual information permits to significantly outperform ApEn and SampEn for acidosis detection, during any stage of labor.
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Malaina I, Martinez L, Matorras R, Bringas C, Aranburu L, Fernández-Llebrez L, Gonzalez L, Arana I, Pérez MB, Martínez de la Fuente I. Estimation of preterm labor immediacy by nonlinear methods. PLoS One 2017; 12:e0178257. [PMID: 28570658 PMCID: PMC5453438 DOI: 10.1371/journal.pone.0178257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 05/10/2017] [Indexed: 11/19/2022] Open
Abstract
Preterm delivery affects about one tenth of human births and is associated with an increased perinatal morbimortality as well as with remarkable costs. Even if there are a number of predictors and markers of preterm delivery, none of them has a high accuracy. In order to find quantitative indicators of the immediacy of labor, 142 cardiotocographies (CTG) recorded from women consulting because of suspected threatened premature delivery with gestational ages comprehended between 24 and 35 weeks were collected and analyzed. These 142 samples were divided into two groups: the delayed labor group (n = 75), formed by the women who delivered more than seven days after the tocography was performed, and the anticipated labor group (n = 67), which corresponded to the women whose labor took place during the seven days following the recording. As a means of finding significant differences between the two groups, some key informational properties were analyzed by applying nonlinear techniques on the tocography recordings. Both the regularity and the persistence levels of the delayed labor group, which were measured by Approximate Entropy (ApEn) and Generalized Hurst Exponent (GHE) respectively, were found to be significantly different from the anticipated labor group. As delivery approached, the values of ApEn tended to increase while the values of GHE tended to decrease, suggesting that these two methods are sensitive to labor immediacy. On this paper, for the first time, we have been able to estimate childbirth immediacy by applying nonlinear methods on tocographies. We propose the use of the techniques herein described as new quantitative diagnosis tools for premature birth that significantly improve the current protocols for preterm labor prediction worldwide.
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Affiliation(s)
- Iker Malaina
- Department of Mathematics, University of the Basque Country UPV/EHU, Leioa, Spain
- * E-mail:
| | - Luis Martinez
- Department of Mathematics, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Roberto Matorras
- Cruces University Hospital, Obstetrics and Gynecology Department, Barakaldo, Spain
- Department of medical-surgical specialties, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Carlos Bringas
- Department of Cell Biology and Histology, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Larraitz Aranburu
- Department of Applied Mathematics, Statistics and Operation Research, University of the Basque Country UPV/EHU, Leioa, Spain
| | | | - Leire Gonzalez
- Cruces University Hospital, Obstetrics and Gynecology Department, Barakaldo, Spain
| | - Itziar Arana
- Cruces University Hospital, Obstetrics and Gynecology Department, Barakaldo, Spain
| | - Martín-Blas Pérez
- Department of Mathematics, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Ildefonso Martínez de la Fuente
- Department of Mathematics, University of the Basque Country UPV/EHU, Leioa, Spain
- Department of Nutrition, CEBAS-CSIC Institute, Espinardo University Campus, Murcia, Spain
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Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis. Med Biol Eng Comput 2017; 55:2037-2052. [PMID: 28462498 PMCID: PMC5644759 DOI: 10.1007/s11517-017-1647-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 04/01/2017] [Indexed: 11/30/2022]
Abstract
Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFEσ) and mean (RCMFEμ) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFEσ and RCMFEμ, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFEσ and RCMFEμ values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFEμ cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFEσ may do so, and vice versa. The results showed that RCMFEσ-based features lead to higher classification accuracies in comparison with the RCMFEμ-based ones. We also made freely available all the Matlab codes used in this study at 10.7488/ds/1477.
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Azami H, Rostaghi M, Abasolo D, Escudero J. Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals. IEEE Trans Biomed Eng 2017; 64:2872-2879. [PMID: 28287954 DOI: 10.1109/tbme.2017.2679136] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and MSE is slow for real-time applications. METHODS We introduce multiscale dispersion entropy (DisEn-MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N2) for sample entropy used in MSE. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE. RESULTS We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and three biomedical datasets. The MDE, RCMDE, and RCMSE methods show similar results, although the MDE and RCMDE are faster, lead to more stable results, and discriminate different types of physiological signals better than MSE and RCMSE. CONCLUSION For noisy short and long time series, MDE and RCMDE are noticeably more stable than MSE and RCMSE, respectively. For short signals, MDE and RCMDE, unlike MSE and RCMSE, do not lead to undefined values. The proposed MDE and RCMDE are significantly faster than MSE and RCMSE, especially for long signals, and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings. SIGNIFICANCE MDE and RCMDE are expected to be useful for the analysis of physiological signals thanks to their ability to distinguish different types of dynamics. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/1982.
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Giuliano N, Annunziata ML, Esposito FG, Tagliaferri S, Di Lieto A, Magenes G, Signorini MG, Campanile M, Arduini D. Computerised analysis of antepartum foetal heart parameters: New reference ranges. J OBSTET GYNAECOL 2016; 37:296-304. [PMID: 27923290 DOI: 10.1080/01443615.2016.1239069] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
We selected 4012 cCTG records (one trace for each patient) performed in healthy pregnancies from 30th to 42nd gestational week using foetal heart rate (FHR), short-term variability (STV), long-term irregularity (LTI), Delta, approximate entropy (ApEn), spectral components as low frequency (LF), median frequency (MF), high frequency (HF) and LF/(HF + MF) ratio were analysed. Reference nomograms were created and sensitivity and specificity for the prediction of foetal compromise were calculated which were 90% and 89%, respectively. Changes of cCTG parameters according to gestational week were evaluated: FHR (r = -.65) and LF (r = -.87) showed a statistically significant reduction (p < .05) with gestational age. STV (r = .59), LTI (r = .69), Delta (r = .67), and MF (r = .88) showed a statistically significant increase (p < .05) with gestational age. In contrast, for ApEn (r = -.098), HF (r = .14) and LF/(HF + MF) ratio (r = -.47) a non-statistically significant change was found (p > .05). The identification of reference ranges for cCTG indexes in according to gestational age could provide a more objective examination of cCTG trace.
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Affiliation(s)
- Natascia Giuliano
- a Department of Obstetrical-Gynaecological, Urological Science and Reproductive Medicine , Federico II University , Naples , Italy
| | - Maria Laura Annunziata
- a Department of Obstetrical-Gynaecological, Urological Science and Reproductive Medicine , Federico II University , Naples , Italy
| | - Francesca Giovanna Esposito
- a Department of Obstetrical-Gynaecological, Urological Science and Reproductive Medicine , Federico II University , Naples , Italy
| | - Salvatore Tagliaferri
- a Department of Obstetrical-Gynaecological, Urological Science and Reproductive Medicine , Federico II University , Naples , Italy
| | - Andrea Di Lieto
- a Department of Obstetrical-Gynaecological, Urological Science and Reproductive Medicine , Federico II University , Naples , Italy
| | - Giovanni Magenes
- b Department of Electrical, Computer and Biomedical Engineering , University of Pavia , Pavia , Italy
| | | | - Marta Campanile
- a Department of Obstetrical-Gynaecological, Urological Science and Reproductive Medicine , Federico II University , Naples , Italy
| | - Domenico Arduini
- d Department of Obstetrics and Gynaecology , Foetal Medicine Centre, University of Rome "Tor Vergata" , Rome , Italy
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Silva LEV, Lataro RM, Castania JA, da Silva CAA, Valencia JF, Murta LO, Salgado HC, Fazan R, Porta A. Multiscale entropy analysis of heart rate variability in heart failure, hypertensive, and sinoaortic-denervated rats: classical and refined approaches. Am J Physiol Regul Integr Comp Physiol 2016; 311:R150-6. [PMID: 27225948 DOI: 10.1152/ajpregu.00076.2016] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 05/04/2016] [Indexed: 11/22/2022]
Abstract
The analysis of heart rate variability (HRV) by nonlinear methods has been gaining increasing interest due to their ability to quantify the complexity of cardiovascular regulation. In this study, multiscale entropy (MSE) and refined MSE (RMSE) were applied to track the complexity of HRV as a function of time scale in three pathological conscious animal models: rats with heart failure (HF), spontaneously hypertensive rats (SHR), and rats with sinoaortic denervation (SAD). Results showed that HF did not change HRV complexity, although there was a tendency to decrease the entropy in HF animals. On the other hand, SHR group was characterized by reduced complexity at long time scales, whereas SAD animals exhibited a smaller short- and long-term irregularity. We propose that short time scales (1 to 4), accounting for fast oscillations, are more related to vagal and respiratory control, whereas long time scales (5 to 20), accounting for slow oscillations, are more related to sympathetic control. The increased sympathetic modulation is probably the main reason for the lower entropy observed at high scales for both SHR and SAD groups, acting as a negative factor for the cardiovascular complexity. This study highlights the contribution of the multiscale complexity analysis of HRV for understanding the physiological mechanisms involved in cardiovascular regulation.
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Affiliation(s)
- Luiz Eduardo Virgilio Silva
- Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Renata Maria Lataro
- Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Jaci Airton Castania
- Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Carlos Alberto Aguiar da Silva
- Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | - Luiz Otavio Murta
- Department of Computing and Mathematics, School of Philosophy, Sciences and Letters, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Helio Cesar Salgado
- Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Rubens Fazan
- Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil;
| | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy; and Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, Milan, Italy
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Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy. Med Biol Eng Comput 2016; 55:191-205. [PMID: 27108288 DOI: 10.1007/s11517-016-1476-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/23/2016] [Indexed: 10/21/2022]
Abstract
Multiscale entropy (MSE) and refined multiscale entropy (RMSE) techniques are being widely used to evaluate the complexity of a time series across multiple time scales 't'. Both these techniques, at certain time scales (sometimes for the entire time scales, in the case of RMSE), assign higher entropy to the HRV time series of certain pathologies than that of healthy subjects, and to their corresponding randomized surrogate time series. This incorrect assessment of signal complexity may be due to the fact that these techniques suffer from the following limitations: (1) threshold value 'r' is updated as a function of long-term standard deviation and hence unable to explore the short-term variability as well as substantial variability inherited in beat-to-beat fluctuations of long-term HRV time series. (2) In RMSE, entropy values assigned to different filtered scaled time series are the result of changes in variance, but do not completely reflect the real structural organization inherited in original time series. In the present work, we propose an improved RMSE (I-RMSE) technique by introducing a new procedure to set the threshold value by taking into account the period-to-period variability inherited in a signal and evaluated it on simulated and real HRV database. The proposed I-RMSE assigns higher entropy to the age-matched healthy subjects than that of patients suffering from atrial fibrillation, congestive heart failure, sudden cardiac death and diabetes mellitus, for the entire time scales. The results strongly support the reduction in complexity of HRV time series in female group, old-aged, patients suffering from severe cardiovascular and non-cardiovascular diseases, and in their corresponding surrogate time series.
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Warmerdam GJJ, Vullings R, Van Laar JOEH, Van der Hout-Van der Jagt MB, Bergmans JWM, Schmitt L, Oei SG. Using uterine activity to improve fetal heart rate variability analysis for detection of asphyxia during labor. Physiol Meas 2016; 37:387-400. [PMID: 26862891 DOI: 10.1088/0967-3334/37/3/387] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
During labor, uterine contractions can cause temporary oxygen deficiency for the fetus. In case of severe and prolonged oxygen deficiency this can lead to asphyxia. The currently used technique for detection of asphyxia, cardiotocography (CTG), suffers from a low specificity. Recent studies suggest that analysis of fetal heart rate variability (HRV) in addition to CTG can provide information on fetal distress. However, interpretation of fetal HRV during labor is difficult due to the influence of uterine contractions on fetal HRV. The aim of this study is therefore to investigate whether HRV features differ during contraction and rest periods, and whether these differences can improve the detection of asphyxia. To this end, a case-control study was performed, using 14 cases with asphyxia that were matched with 14 healthy fetuses. We did not find significant differences for individual HRV features when calculated over the fetal heart rate without separating contractions and rest periods (p > 0.30 for all HRV features). Separating contractions from rest periods did result in a significant difference. In particular the ratio between HRV features calculated during and outside contractions can improve discrimination between fetuses with and without asphyxia (p < 0.04 for three out of four ratio HRV features that were studied in this paper).
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Affiliation(s)
- G J J Warmerdam
- Faculty of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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30
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Azami H, Escudero J. Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2015.08.004] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Ribes S, Girault JM, Perrotin F, Kouamé D. Multidimensional Ultrasound Doppler Signal Analysis for Fetal Activity Monitoring. ULTRASOUND IN MEDICINE & BIOLOGY 2015; 41:3172-3181. [PMID: 26365925 DOI: 10.1016/j.ultrasmedbio.2015.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Revised: 07/22/2015] [Accepted: 07/27/2015] [Indexed: 06/05/2023]
Abstract
Fetal activity parameters such as movements, heart rate and the related parameters are essential indicators of fetal wellbeing, and no device provides simultaneous access to and sufficient estimation of all of these parameters to evaluate fetal health. This work was aimed at collecting these parameters to automatically separate healthy from compromised fetuses. To achieve this goal, we first developed a multi-sensor-multi-gate Doppler system. Then we recorded multidimensional Doppler signals and estimated the fetal activity parameters via dedicated signal processing techniques. Finally, we combined these parameters into four sets of parameters (or four hyper-parameters) to determine the set of parameters that is able to separate healthy from other fetuses. To validate our system, a data set consisting of two groups of fetal signals (normal and compromised) was established and provided by physicians. From the estimated parameters, an instantaneous Manning-like score, referred to as the ultrasonic score, was calculated and was used together with movements, heart rate and the associated parameters in a classification process employing the support vector machine method. We investigated the influence of the sets of parameters and evaluated the performance of the support vector machine using the computation of sensibility, specificity, percentage of support vectors and total classification error. The sensitivity of the four sets ranged from 79% to 100%. Specificity was 100% for all sets. The total classification error ranged from 0% to 20%. The percentage of support vectors ranged from 33% to 49%. Overall, the best results were obtained with the set of parameters consisting of fetal movement, short-term variability, long-term variability, deceleration and ultrasound score. The sensitivity, specificity, percentage of support vectors and total classification error of this set were respectively 100%, 100%, 35% and 0%. This indicated our ability to separate the data into two sets (normal fetuses and pathologic fetuses), and the results highlight the excellent match with the clinical classification performed by the physicians. This work indicates the feasibility of detecting compromised fetuses and also represents an interesting method of close fetal monitoring during the entire pregnancy.
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Affiliation(s)
- Sophie Ribes
- University of Toulouse III, IRIT UMR CNRS 5505, Toulouse, France
| | | | - Franck Perrotin
- CHU Bretonneau, Tours, service de Gynecologie Obstétrique, INSERM U930, Tours, France
| | - Denis Kouamé
- University of Toulouse III, IRIT UMR CNRS 5505, Toulouse, France.
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32
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Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations. ENTROPY 2015. [DOI: 10.3390/e17127849] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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33
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Marwaha P, Sunkaria RK. Optimal Selection of Threshold Value 'r' for Refined Multiscale Entropy. Cardiovasc Eng Technol 2015; 6:557-76. [PMID: 26577486 DOI: 10.1007/s13239-015-0242-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 08/20/2015] [Indexed: 11/25/2022]
Abstract
Refined multiscale entropy (RMSE) technique was introduced to evaluate complexity of a time series over multiple scale factors 't'. Here threshold value 'r' is updated as 0.15 times SD of filtered scaled time series. The use of fixed threshold value 'r' in RMSE sometimes assigns very close resembling entropy values to certain time series at certain temporal scale factors and is unable to distinguish different time series optimally. The present study aims to evaluate RMSE technique by varying threshold value 'r' from 0.05 to 0.25 times SD of filtered scaled time series and finding optimal 'r' values for each scale factor at which different time series can be distinguished more effectively. The proposed RMSE was used to evaluate over HRV time series of normal sinus rhythm subjects, patients suffering from sudden cardiac death, congestive heart failure, healthy adult male, healthy adult female and mid-aged female groups as well as over synthetic simulated database for different datalengths 'N' of 3000, 3500 and 4000. The proposed RMSE results in improved discrimination among different time series. To enhance the computational capability, empirical mathematical equations have been formulated for optimal selection of threshold values 'r' as a function of SD of filtered scaled time series and datalength 'N' for each scale factor 't'.
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Affiliation(s)
- Puneeta Marwaha
- Department of Electronics and Communication Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India.
| | - Ramesh Kumar Sunkaria
- Department of Electronics and Communication Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India.
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Characterization of Complex Fractionated Atrial Electrograms by Sample Entropy: An International Multi-Center Study. ENTROPY 2015. [DOI: 10.3390/e17117493] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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An alternative approach to approximate entropy threshold value (r) selection: application to heart rate variability and systolic blood pressure variability under postural challenge. Med Biol Eng Comput 2015; 54:723-32. [PMID: 26253284 DOI: 10.1007/s11517-015-1362-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Accepted: 07/21/2015] [Indexed: 10/23/2022]
Abstract
This study presents an alternative approach to approximate entropy (ApEn) threshold value (r) selection. There are two limitations of traditional ApEn algorithm: (1) the occurrence of undefined conditional probability (CPu) where no template match is found and (2) use of a crisp tolerance (radius) threshold 'r'. To overcome these limitations, CPu is substituted with optimum bias setting ɛ opt which is found by varying ɛ from (1/N - m) to 1 in the increments of 0.05, where N is the length of the series and m is the embedding dimension. Furthermore, an alternative approach for selection of r based on binning the distance values obtained by template matching to calculate ApEnbin is presented. It is observed that ApEnmax, ApEnchon and ApEnbin converge for ɛ opt = 0.6 in 50 realizations (n = 50) of random number series of N = 300. Similar analysis suggests ɛ opt = 0.65 and ɛ opt = 0.45 for 50 realizations each of fractional Brownian motion and MIX(P) series (Lu et al. in J Clin Monit Comput 22(1):23-29, 2008). ɛ opt = 0.5 is suggested for heart rate variability (HRV) and systolic blood pressure variability (SBPV) signals obtained from 50 young healthy subjects under supine and upright position. It is observed that (1) ApEnbin of HRV is lower than SBPV, (2) ApEnbin of HRV increases from supine to upright due to vagal inhibition and (3) ApEnbin of BPV decreases from supine to upright due to sympathetic activation. Moreover, merit of ApEnbin is that it provides an alternative to the cumbersome ApEnmax procedure.
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Karmakar C, Kimura Y, Palaniswami M, Khandoker A. Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.05.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zaylaa A, Oudjemia S, Charara J, Girault JM. n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals. Comput Biol Med 2015; 64:323-33. [PMID: 25824414 DOI: 10.1016/j.compbiomed.2015.03.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 03/04/2015] [Accepted: 03/06/2015] [Indexed: 11/29/2022]
Abstract
This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.
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Affiliation(s)
- Amira Zaylaa
- University François Rabelais of Tours, UMR Brain-Imaging, INSERM U930, Tours, France; Department of Physics and Electronics, Faculty of Sciences, Lebanese University, Beirut, Lebanon.
| | | | - Jamal Charara
- Department of Physics and Electronics, Faculty of Sciences, Lebanese University, Beirut, Lebanon.
| | - Jean-Marc Girault
- University François Rabelais of Tours, UMR Brain-Imaging, INSERM U930, Tours, France.
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38
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Entropy Measures in the Assessment of Heart Rate Variability in Patients with Cardiodepressive Vasovagal Syncope. ENTROPY 2015. [DOI: 10.3390/e17031007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Chudacek V, Talmon R, Anden J, Mallat S, Coifman RR, Abry P, Doret M. Low dimensional manifold embedding for scattering coefficients of intrapartum fetale heart rate variability. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:6373-6. [PMID: 25571454 DOI: 10.1109/embc.2014.6945086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Intrapartum fetal surveillance for early detection of fetal acidosis in clinical practice focuses on reducing neonatal morbidity via early detection. It is the subject of on going research studies attempting notably to improve detection performance by reducing false positive rate. In that context, the present contribution tailors to fetal heart rate variability analysis a graph-based dimensionality reduction procedure performed on scattering coefficients. Applied to a high quality and well-documented database constituted by obstetricians from a French academic hospital, the low dimensional embedding enables to distinguish between the temporal dynamics of healthy and acidotic fetuses, as well as to achieve satisfactory detection performance detection compared to those obtained by the clinical-benchmark FIGO criteria.
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Silva LEV, Rodrigues FL, de Oliveira M, Salgado HC, Fazan R. Heart rate complexity in sinoaortic-denervated mice. Exp Physiol 2015; 100:156-63. [PMID: 25398712 DOI: 10.1113/expphysiol.2014.082222] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Accepted: 11/07/2014] [Indexed: 11/08/2022]
Abstract
NEW FINDINGS What is the central question of this study? New measurements for cardiovascular complexity, such as detrended fluctuation analysis (DFA) and multiscale entropy (MSE), have been shown to predict cardiovascular outcomes. Given that cardiovascular diseases are accompanied by autonomic imbalance and decreased baroreflex sensitivity, the central question is: do baroreceptors contribute to cardiovascular complexity? What is the main finding and its importance? Sinoaortic denervation altered both DFA scaling exponents and MSE, indicating that both short- and long-term mechanisms of complexity are altered in sinoaortic denervated mice, resulting in a loss of physiological complexity. These results suggest that the baroreflex is a key element in the complex structures involved in heart rate variability regulation. Recently, heart rate (HR) oscillations have been recognized as complex behaviours derived from non-linear processes. Physiological complexity theory is based on the idea that healthy systems present high complexity, i.e. non-linear, fractal variability at multiple scales, with long-range correlations. The loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. Based on the idea that most cardiovascular diseases are accompanied by autonomic imbalance and a decrease in baroreflex sensitivity, we hypothesize that the baroreflex plays an important role in complex cardiovascular behaviour. Mice that had been subjected to sinoaortic denervation (SAD) were implanted with catheters in the femoral artery and jugular vein 5 days prior to the experiment. After recording the baseline arterial pressure (AP), pulse interval time series were generated from the intervals between consecutive values of diastolic pressure. The complexity of the HRV was determined using detrended fluctuation analysis and multiscale entropy. The detrended fluctuation analysis α1 scaling exponent (a short-term index) was remarkably decreased in the SAD mice (0.79 ± 0.06 versus 1.13 ± 0.04 for the control mice), whereas SAD slightly increased the α2 scaling exponent (a long-term index; 1.12 ± 0.03 versus 1.04 ± 0.02 for control mice). In the SAD mice, the total multiscale entropy was decreased (13.2 ± 1.3) compared with the control mice (18.9 ± 1.4). In conclusion, fractal and regularity structures of HRV are altered in SAD mice, affecting both short- and long-term mechanisms of complexity, suggesting that the baroreceptors play a considerable role in the complex structure of HRV.
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Affiliation(s)
- Luiz Eduardo V Silva
- Department of Physiology, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, SP, Brazil
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Chudáček V, Andén J, Mallat S, Abry P, Doret M. Scattering transform for intrapartum fetal heart rate variability fractal analysis: a case-control study. IEEE Trans Biomed Eng 2014; 61:1100-8. [PMID: 24658235 DOI: 10.1109/tbme.2013.2294324] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Intrapartum fetal heart rate monitoring, aiming at early acidosis detection, constitutes an important public health stake. Scattering transform is proposed here as a new tool to analyze intrapartum fetal heart rate (FHR) variability. It consists of a nonlinear extension of the underlying wavelet transform, that thus preserves its multiscale nature. Applied to an FHR signal database constructed in a French academic hospital, the scattering transform is shown to permit to efficiently measure scaling exponents characterizing the fractal properties of intrapartum FHR temporal dynamics, that relate not only to the sole covariance (correlation scaling exponent), but also to the full dependence structure of data (intermittency scaling exponent). Such exponents are found to satisfactorily discriminate temporal dynamics of healthy subjects (from that of nonhealthy ones) and to emphasize the role of the highest frequencies (around and above 1 Hz) in intrapartum FHR variability. This permits us to achieve satisfactory classification performance that improves on those obtained from the analysis of International Federation of Gynecology and Obstetrics (FIGO) criteria, notably by classifying as healthy a number of subjects that were incorrectly classified as nonhealthy by classical clinically used FIGO criteria. Combined to obstetrician annotations, these scaling exponents enable us to sketch a typology of these FIGO-false positive subjects. Also, they permit us to monitor the evolution along time of the intrapartum health status of the fetuses and to estimate an optimal detection time-frame.
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Measuring Instantaneous and Spectral Information Entropies by Shannon Entropy of Choi-Williams Distribution in the Context of Electroencephalography. ENTROPY 2014. [DOI: 10.3390/e16052530] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zhang X, Chen X, Barkhaus PE, Zhou P. Multiscale entropy analysis of different spontaneous motor unit discharge patterns. IEEE J Biomed Health Inform 2014; 17:470-6. [PMID: 24235117 DOI: 10.1109/jbhi.2013.2241071] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This study explores a novel application of multiscale entropy (MSE) analysis for characterizing different patterns of spontaneous electromyogram (EMG) signals including sporadic, tonic and repetitive spontaneous motor unit discharges, and normal surface EMG baseline. Two algorithms for MSE analysis, namely, the standard MSE and the intrinsic mode entropy (IMEn) (based on the recently developed multivariate empirical mode decomposition method), were applied to different patterns of spontaneous EMG. Significant differences were observed in multiple scales of the standard MSE and IMEn analyses (<;i>p<;/i> <; 0.001) for any two of the spontaneous EMG patterns, while such significance may not be observed from the single-scale entropy analysis. Compared to the standard MSE, the IMEn analysis facilitates usage of a relatively low scale number to discern entropy difference among various patterns of spontaneous EMG signals. The findings from this study contribute to our understanding of the nonlinear dynamic properties of different spontaneous EMG patterns, which may be related to spinal motoneuron or motor unit health.
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Hannah Inbarani H, Nizar Banu PK, Azar AT. Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1552-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Monitoring fetal heart rate during pregnancy: contributions from advanced signal processing and wearable technology. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:707581. [PMID: 24639886 PMCID: PMC3930181 DOI: 10.1155/2014/707581] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2013] [Revised: 10/29/2013] [Accepted: 11/10/2013] [Indexed: 12/04/2022]
Abstract
Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their conditions, thus providing necessary interventions in time. Both these two objectives can be achieved by integrating technological development with methodological tools, thus allowing accurate classification and extraction of useful diagnostic information.
The paper is focused on monitoring procedures applied to fetal heart rate variability (FHRV) signals, collected during pregnancy, in order to assess fetal well-being. The use of linear time and frequency techniques as well as the computation of non linear indices can contribute to enhancing the diagnostic power and reliability of fetal monitoring. The paper shows how advanced signal processing approaches can contribute to developing new diagnostic and classification indices. Their usefulness is evaluated by comparing two selected populations: normal fetuses and intra uterine growth restricted (IUGR) fetuses. Results show that the computation of different indices on FHRV signals, either linear and nonlinear, gives helpful indications to describe pathophysiological mechanisms involved in the cardiovascular and neural system controlling the fetal heart. As a further contribution, the paper briefly describes how the introduction of wearable systems for fetal ECG recording could provide new technological solutions improving the quality and usability of prenatal monitoring.
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New estimators and guidelines for better use of fetal heart rate estimators with Doppler ultrasound devices. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:784862. [PMID: 24624224 PMCID: PMC3926313 DOI: 10.1155/2014/784862] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Revised: 10/25/2013] [Accepted: 11/04/2013] [Indexed: 11/17/2022]
Abstract
Characterizing fetal wellbeing with a Doppler ultrasound device requires computation of a score based on fetal parameters. In order to analyze the parameters derived from the fetal heart rate correctly, an accuracy of 0.25 beats per minute is needed. Simultaneously with the lowest false negative rate and the highest sensitivity, we investigated whether various Doppler techniques ensure this accuracy. We found that the accuracy was ensured if directional Doppler signals and autocorrelation estimation were used. Our best estimator provided sensitivity of 95.5%, corresponding to an improvement of 14% compared to the standard estimator.
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Abstract
OBJECTIVE This study explored the feasibility of detecting hidden muscle activity in surface electromyogram (EMG) baseline. APPROACH Power spectral density (PSD) analysis and multi-scale entropy (MSE) analysis were used. Both analyses were applied to computer simulations of surface EMG baseline with the presence (representing activity data) or absence (representing reference data) of hidden muscle activity, as well as surface electrode array EMG baseline recordings of healthy control and amyotrophic lateral sclerosis (ALS) subjects. MAIN RESULTS Although the simulated reference data and the activity data yielded no distinguishable difference in the time domain, they demonstrated a significant difference in the frequency and signal complexity domains with the PSD and MSE analyses. For a comparison using pooled data, such a difference was also observed when the PSD and MSE analyses were applied to surface electrode array EMG baseline recordings of healthy control and ALS subjects, which demonstrated no distinguishable difference in the time domain. Compared with the PSD analysis, the MSE analysis appeared to be more sensitive for detecting the difference in surface EMG baselines between the two groups. SIGNIFICANCE The findings implied the presence of a hidden muscle activity in surface EMG baseline recordings from the ALS subjects. To promote the presented analysis as a useful diagnostic or investigatory tool, future studies are necessary to assess the pathophysiological nature or origins of the hidden muscle activity, as well as the baseline difference at the individual subject level.
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Affiliation(s)
- Xu Zhang
- Biomedical Engineering Program, University of Science and Technology of China, Hefei, People's Republic of China
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Coarse-grained multifractality analysis based on structure function measurements to discriminate healthy from distressed foetuses. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:152828. [PMID: 24454527 PMCID: PMC3877591 DOI: 10.1155/2013/152828] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Revised: 11/06/2013] [Accepted: 11/22/2013] [Indexed: 11/18/2022]
Abstract
This paper proposes a combined coarse-grained multifractal method to discriminate between distressed and normal foetuses. The coarse-graining operation was performed by means of a coarse-grained procedure and the multifractal operation was based on a structure function. The proposed method was evaluated by one hundred recordings including eighty normal foetuses and twenty distressed foetuses. We found that it was possible to discriminate between distressed and normal foetuses using the Hurst exponent, singularity, and Holder spectra.
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Fanelli A, Magenes G, Campanile M, Signorini MG. Quantitative Assessment of Fetal Well-Being Through CTG Recordings: A New Parameter Based on Phase-Rectified Signal Average. IEEE J Biomed Health Inform 2013; 17:959-66. [DOI: 10.1109/jbhi.2013.2268423] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Steinisch M, Torke PR, Haueisen J, Hailer B, Grönemeyer D, Van Leeuwen P, Comani S. Early detection of coronary artery disease in patients studied with magnetocardiography: an automatic classification system based on signal entropy. Comput Biol Med 2012; 43:144-53. [PMID: 23260570 DOI: 10.1016/j.compbiomed.2012.11.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2012] [Revised: 11/20/2012] [Accepted: 11/22/2012] [Indexed: 01/18/2023]
Abstract
We propose an automatic system for the classification of coronary artery disease (CAD) based on entropy measures of MCG recordings. Ten patients with coronary artery narrowing ≥ or ≤ 50% were categorized by a multilayer perceptron (MLP) neural network based on Linear Discriminant Analysis (LDA). Best results were obtained with MCG at rest: 99% sensitivity, 97% specificity, 98% accuracy, 96% and 99% positive and negative predictive values for single heartbeats. At patient level, these results correspond to a correct classification of all patients. The classifier's suitability to detect CAD-induced changes on the MCG at rest was validated with surrogate data.
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Affiliation(s)
- Martin Steinisch
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University, Via dei Vestini 33, 66013 Chieti, Italy
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