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Abdullahi A, Etoom M, Badaru UM, Elibol N, Abuelsamen AA, Alawneh A, Zakari UU, Saeys W, Truijen S. Vagus nerve stimulation for the treatment of epilepsy: things to note on the protocols, the effects and the mechanisms of action. Int J Neurosci 2024; 134:560-569. [PMID: 36120993 DOI: 10.1080/00207454.2022.2126776] [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: 06/27/2022] [Revised: 08/19/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
Epilepsy is a chronic brain disorder that is characterized by repetitive un-triggered seizures that occur severally within 24 h or more. Non-pharmacological methods for the management of epilepsy were discussed. The non-pharmacological methods include the vagus nerve stimulation (VNS) which is subdivided into invasive and non-invasive techniques. For the non-invasive techniques, the auricular VNS, stimulation of the cervical branch of vagus nerve in the neck, manual massage of the neck, and respiratory vagal nerve stimulation were discussed. Similarly, the stimulation parameters used and the mechanisms of actions through which VNS improves seizures were also discussed. Use of VNS to reduce seizure frequency has come a long way. However, considering the cost and side effects of the invasive method, non-invasive techniques should be given a renewed attention. In particular, respiratory vagal nerve stimulation should be considered. In doing this, the patients should for instance carry out slow-deep breathing exercise 6 to 8 times every 3 h during the waking hours. Slow-deep breathing can be carried out by the patients on their own; therefore this can serve as a form of self-management.HIGHLIGHTSEpilepsy can interfere with the patients' ability to carry out their daily activities and ultimately affect their quality of life.Medications are used to manage epilepsy; but they often have their serious side effects.Vagus nerve stimulation (VNS) is gaining ground especially in the management of refractory epilepsy.The VNS is administered through either the invasive or the non-invasive methodsThe invasive method of VNS like the medication has potential side effects, and can be costly.The non-invasive method includes auricular VNS, stimulation of the neck muscles and skin and respiratory vagal nerve stimulation via slow-deep breathing exercises.The respiratory vagal nerve stimulation via slow-deep breathing exercises seems easy to administer even by the patients themselves.Consequently, it is our opinion that patients with epilepsy be made to carry out slow-deep breathing exercise 6-8 times every 3 h during the waking hours.
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Affiliation(s)
- Auwal Abdullahi
- Department of Physiotherapy, Bayero University Kano, Nigeria
- Department of Rehabilitation Sciences and Physiotherapy, University of Antwerp, Antwerp, Belgium
| | - Mohammad Etoom
- Department of Physiotherapy, Aqaba University of Technology, Aqaba, Jordan
| | | | - Nuray Elibol
- Department of Physiotherapy and Rehabilitation Sciences, Ege University, Izmir, Turkey
| | | | - Anoud Alawneh
- Department of Physiotherapy, Aqaba University of Technology, Aqaba, Jordan
| | - Usman Usman Zakari
- Department of Physiotherapy, Federal Medical Center, Birnin Kudu, Jigawa State, Nigeria
| | - Wim Saeys
- Department of Rehabilitation Sciences and Physiotherapy, University of Antwerp, Antwerp, Belgium
| | - Steven Truijen
- Department of Rehabilitation Sciences and Physiotherapy, University of Antwerp, Antwerp, Belgium
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Behbahani S, Jafarnia Dabanloo N, Nasrabadi AM, Dourado A. Epileptic seizure prediction based on features extracted from lagged Poincaré plots. Int J Neurosci 2024; 134:381-397. [PMID: 35892226 DOI: 10.1080/00207454.2022.2106435] [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: 06/24/2022] [Accepted: 07/14/2022] [Indexed: 10/16/2022]
Abstract
OBJECTIVE The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR). METHODS In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points. RESULTS The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor. CONCLUSION The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.
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Affiliation(s)
- Soroor Behbahani
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Antonio Dourado
- Center for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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Yang Y, Qin X, Wen H, Li F, Lin X. Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction. Front Comput Neurosci 2023; 17:1172987. [PMID: 37216065 PMCID: PMC10192566 DOI: 10.3389/fncom.2023.1172987] [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: 02/24/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction.
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Affiliation(s)
- Yong Yang
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| | - Xiaolin Qin
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
| | - Han Wen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, Sichuan, China
| | - Feng Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoguang Lin
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
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Karasmanoglou A, Antonakakis M, Zervakis M. ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5000. [PMID: 36981911 PMCID: PMC10049350 DOI: 10.3390/ijerph20065000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/17/2023] [Accepted: 03/08/2023] [Indexed: 06/18/2023]
Abstract
Epilepsy is one of the most common brain diseases, characterized by frequent recurrent seizures or "ictal" states. A patient experiences uncontrollable muscular contractions, inducing loss of mobility and balance, which may result in injury or even death during these ictal states. Extensive investigation is vital to establish a systematic approach for predicting and informing patients about oncoming seizures ahead of time. Most methodologies developed are focused on the detection of abnormalities using mostly electroencephalogram (EEG) recordings. In this regard, research has indicated that certain pre-ictal alterations in the Autonomic Nervous System (ANS) can be detected in patient electrocardiogram (ECG) signals. The latter could potentially provide the basis for a robust seizure prediction approach. The recently proposed ECG-based seizure warning systems utilize machine learning models to classify a patient's condition. Such approaches require the incorporation of large, diverse, and thoroughly annotated ECG datasets, limiting their application potential. In this work, we investigate anomaly detection models in a patient-specific context with low supervision requirements. Specifically, we consider One-Class SVM (OCSVM), Minimum Covariance Determinant (MCD) Estimator, and Local Outlier Factor (LOF) models to quantify the novelty or abnormality of pre-ictal short-term (2-3 min) Heart Rate Variability (HRV) features of patients, trained on a reference interval considered to contain stable heart rate as the only form of supervision. Our models are evaluated against labels that were either hand-picked or automatically generated (weak labels) by a two-phase clustering procedure for samples of the "Post-Ictal Heart Rate Oscillations in Partial Epilepsy" (PIHROPE) dataset recorded by the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, achieving detection in 9 out of 10 cases, with average AUCs of over 93% across all models and warning times ranging from 6 to 30 min prior to seizure. The proposed anomaly detection and monitoring approach can potentially pave the way for early detection and warning of seizure incidents based on body sensor inputs.
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Bauer J, Devinsky O, Rothermel M, Koch H. Autonomic dysfunction in epilepsy mouse models with implications for SUDEP research. Front Neurol 2023; 13:1040648. [PMID: 36686527 PMCID: PMC9853197 DOI: 10.3389/fneur.2022.1040648] [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: 09/09/2022] [Accepted: 12/12/2022] [Indexed: 01/09/2023] Open
Abstract
Epilepsy has a high prevalence and can severely impair quality of life and increase the risk of premature death. Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in drug-resistant epilepsy and most often results from respiratory and cardiac impairments due to brainstem dysfunction. Epileptic activity can spread widely, influencing neuronal activity in regions outside the epileptic network. The brainstem controls cardiorespiratory activity and arousal and reciprocally connects to cortical, diencephalic, and spinal cord areas. Epileptic activity can propagate trans-synaptically or via spreading depression (SD) to alter brainstem functions and cause cardiorespiratory dysfunction. The mechanisms by which seizures propagate to or otherwise impair brainstem function and trigger the cascading effects that cause SUDEP are poorly understood. We review insights from mouse models combined with new techniques to understand the pathophysiology of epilepsy and SUDEP. These techniques include in vivo, ex vivo, invasive and non-invasive methods in anesthetized and awake mice. Optogenetics combined with electrophysiological and optical manipulation and recording methods offer unique opportunities to study neuronal mechanisms under normal conditions, during and after non-fatal seizures, and in SUDEP. These combined approaches can advance our understanding of brainstem pathophysiology associated with seizures and SUDEP and may suggest strategies to prevent SUDEP.
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Affiliation(s)
- Jennifer Bauer
- Department of Epileptology and Neurology, RWTH Aachen University, Aachen, Germany,Institute for Physiology and Cell Biology, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany
| | - Orrin Devinsky
- Departments of Neurology, Neurosurgery and Psychiatry, NYU Langone School of Medicine, New York, NY, United States
| | - Markus Rothermel
- Institute for Physiology and Cell Biology, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany
| | - Henner Koch
- Department of Epileptology and Neurology, RWTH Aachen University, Aachen, Germany,*Correspondence: Henner Koch ✉
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Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings. SENSORS 2022; 22:s22082900. [PMID: 35458883 PMCID: PMC9025176 DOI: 10.3390/s22082900] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 12/24/2022]
Abstract
Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients’ health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages.
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Heart Rate Variability Analysis for Seizure Detection in Neonatal Intensive Care Units. Bioengineering (Basel) 2022; 9:bioengineering9040165. [PMID: 35447725 PMCID: PMC9031489 DOI: 10.3390/bioengineering9040165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/28/2022] [Accepted: 04/05/2022] [Indexed: 12/03/2022] Open
Abstract
In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely clinical intervention. Over the years, several neonatal seizure detection systems were proposed to detect neonatal seizures automatically and speed up seizure diagnosis, most based on the EEG signal analysis. Recently, research has focused on other possible seizure markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system to investigate the usefulness of heart rate variability (HRV) analysis to detect neonatal seizures in the NICUs. HRV analysis is performed considering time-domain, frequency-domain, entropy and multiscale entropy features. The performance is evaluated on a dataset of ECG signals from 51 full-term babies, 29 seizure-free. The proposed system gives results comparable to those reported in the literature: Area Under the Receiver Operating Characteristic Curve = 62%, Sensitivity = 47%, Specificity = 67%. Moreover, the system’s performance is evaluated in a real clinical environment, inevitably affected by several artefacts. To the best of our knowledge, our study proposes for the first time a multi-feature ECG-based NSD system that also offers a comparative analysis between babies suffering from seizures and seizure-free ones.
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Hamdy RM, Abdel-Tawab H, Abd Elaziz OH, Sobhy El attar R, Kotb FM. Evaluation of Heart Rate Variability Parameters During Awake and Sleep in Refractory and Controlled Epileptic Patients. Int J Gen Med 2022; 15:3865-3877. [PMID: 35422653 PMCID: PMC9004725 DOI: 10.2147/ijgm.s354895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/25/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Rehab M Hamdy
- Department of Cardiology, Faculty of Medicine (for Girls), Al-Azhar University, Cairo, Egypt
- Correspondence: Rehab M Hamdy, Department of Cardiology, Faculty for Medicine (for Girls), Al-Azhar University, Cairo, Egypt, Tel +201003022726, Email
| | - Hayam Abdel-Tawab
- Department of Neurology, Faculty of Medicine (for Girls), Al-Azhar University, Cairo, Egypt
| | - Ola H Abd Elaziz
- Department of Cardiology, Faculty of Medicine (for Girls), Al-Azhar University, Cairo, Egypt
| | - Rasha Sobhy El attar
- Department of Neurology, Faculty of Medicine (for Girls), Al-Azhar University, Cairo, Egypt
| | - Fatma M Kotb
- Department of Internal Medicine, Faculty of Medicine (for Girls), Al-Azhar University, Cairo, Egypt
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9
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Lee H, Jeon SB, Lee KS. Continuous heart rate variability and electroencephalography monitoring in severe acute brain injury: a preliminary study. Acute Crit Care 2021; 36:151-161. [PMID: 33730778 PMCID: PMC8182164 DOI: 10.4266/acc.2020.00703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 01/15/2021] [Indexed: 11/30/2022] Open
Abstract
Background Decreases in heart rate variability have been shown to be associated with poor outcomes in severe acute brain injury. However, it is unknown whether the changes in heart rate variability precede neurological deterioration in such patients. We explored the changes in heart rate variability measured by electrocardiography in patients who had neurological deterioration following severe acute brain injury, and examined the relationship between heart rate variability and electroencephalography parameters. Methods Retrospective analysis of 25 patients who manifested neurological deterioration after severe acute brain injury and underwent simultaneous electroencephalography plus electrocardiography monitoring. Results Eighteen electroencephalography channels and one simultaneously recorded electrocardiography channel were segmented into epochs of 120-second duration and processed to compute 10 heart rate variability parameters and three quantitative electroencephalography parameters. Raw electroencephalography of the epochs was also assessed by standardized visual interpretation and categorized based on their background abnormalities and ictalinterictal continuum patterns. The heart rate variability and electroencephalography parameters showed consistent changes in the 2-day period before neurological deterioration commenced. Remarkably, the suppression ratio and background abnormality of the electroencephalography parameters had significant reverse correlations with all heart rate variability parameters. Conclusions We observed a significantly progressive decline in heart rate variability from the day before the neurological deterioration events in patients with severe acute brain injury were first observed.
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Affiliation(s)
- Hyunjo Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sang-Beom Jeon
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kwang-Soo Lee
- Department of Neurology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Leal A, Pinto MF, Lopes F, Bianchi AM, Henriques J, Ruano MG, de Carvalho P, Dourado A, Teixeira CA. Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy. Sci Rep 2021; 11:5987. [PMID: 33727606 PMCID: PMC7966782 DOI: 10.1038/s41598-021-85350-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 02/02/2021] [Indexed: 11/08/2022] Open
Abstract
Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure's clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.
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Affiliation(s)
- Adriana Leal
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
| | - Mauro F Pinto
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
| | - Fábio Lopes
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
| | - Anna M Bianchi
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
| | - Jorge Henriques
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
| | - Maria G Ruano
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
- University of Algarve, Department of Electronics and Informatics Engineering, Faculty of Science and Technology, Faro, Portugal
| | - Paulo de Carvalho
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
| | - António Dourado
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
| | - César A Teixeira
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
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Khajehali N, Khajehali Z, Tarokh MJ. The prediction of mortality influential variables in an intensive care unit: a case study. PERSONAL AND UBIQUITOUS COMPUTING 2021; 27:203-219. [PMID: 33654479 PMCID: PMC7907311 DOI: 10.1007/s00779-021-01540-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 02/10/2021] [Indexed: 06/12/2023]
Abstract
The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients' demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.
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Gagliano L, Assi EB, Toffa DH, Nguyen DK, Sawan M. Unsupervised Clustering of HRV Features Reveals Preictal Changes in Human Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:698-701. [PMID: 33018083 DOI: 10.1109/embc44109.2020.9175739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Over a third of patients suffering from epilepsy continue to live with recurrent disabling seizures and would greatly benefit from personalized seizure forecasting. While electroencephalography (EEG) remains most popular for studying subject-specific epileptic precursors, dysfunctions of the autonomous nervous system, notably cardiac activity measured in heart rate variability (HRV), have also been associated with epileptic seizures. This work proposes an unsupervised clustering technique which aims to automatically identify preictal HRV changes in 9 patients who underwent simultaneous electrocardiography (ECG) and intracranial EEG presurgical monitoring at the University of Montreal Hospital Center. A 2-class k-means clustering combined with a quantitative preictal HRV change detection technique were adopted in a subject- and seizure-specific manner. Results indicate inter and intra-patient variability in preictal HRV changes (between 3.5 and 6.5 min before seizure onset) and a statistically significant negative correlation between the time of change in HRV state and the duration of seizures (p<0.05). The presented findings show promise for new avenues of research regarding multimodal seizure prediction and unsupervised preictal time assessment.Clinical Relevance- This study proposed an unsupervised technique for quantitatively identifying preictal HRV changes which can be eventually used to implement an ECG-based seizure forecasting algorithm.
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Moridani M, Abdi Zadeh M, Shahiazar Mazraeh Z. An Efficient Automated Algorithm for Distinguishing Normal and Abnormal ECG Signal. Ing Rech Biomed 2019. [DOI: 10.1016/j.irbm.2019.09.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Bigelow MD, Kouzani AZ. Neural stimulation systems for the control of refractory epilepsy: a review. J Neuroeng Rehabil 2019; 16:126. [PMID: 31665058 PMCID: PMC6820988 DOI: 10.1186/s12984-019-0605-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 10/10/2019] [Indexed: 12/18/2022] Open
Abstract
Epilepsy affects nearly 1% of the world's population. A third of epilepsy patients suffer from a kind of epilepsy that cannot be controlled by current medications. For those where surgery is not an option, neurostimulation may be the only alternative to bring relief, improve quality of life, and avoid secondary injury to these patients. Until recently, open loop neurostimulation was the only alternative for these patients. However, for those whose epilepsy is applicable, the medical approval of the responsive neural stimulation and the closed loop vagal nerve stimulation systems have been a step forward in the battle against uncontrolled epilepsy. Nonetheless, improvements can be made to the existing systems and alternative systems can be developed to further improve the quality of life of sufferers of the debilitating condition. In this paper, we first present a brief overview of epilepsy as a disease. Next, we look at the current state of biomarker research in respect to sensing and predicting epileptic seizures. Then, we present the current state of open loop neural stimulation systems. We follow this by investigating the currently approved, and some of the recent experimental, closed loop systems documented in the literature. Finally, we provide discussions on the current state of neural stimulation systems for controlling epilepsy, and directions for future studies.
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Affiliation(s)
- Matthew D Bigelow
- School of Engineering, Deakin University, Geelong, Victoria, 3216, Australia
| | - Abbas Z Kouzani
- School of Engineering, Deakin University, Geelong, Victoria, 3216, Australia.
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MORIDANI MOHAMMADKARIMI, POULADIAN MAJID. A NOVEL METHOD TO ISCHEMIC HEART DISEASE DETECTION BASED ON NON-INVASIVE ECG IMAGING. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419500027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electrocardiogram (ECG) signals containing very important information about the cardiac are one of the most common tools for physicians in the diagnosis of various types of cardiac diseases. Low accuracy in positioning, limitation of time accuracy, the similarity of signals between some diseases and normal signals and probability of missing some aspect of data are the defect aspects of this method. Importance of cardiac signals and defects of current methods in diagnosis show the need of substituting a new method to show the activity of cardiac. One of the most dangerous defections is ischemia, which corrects and on time diagnose could avoid the latter effect of it. Each of common methods for diagnosis of this illness has their own advantages and disadvantages. In this paper, we consider describing a non-invasive method for ischemic episode detection based on mapping of ECG signals. With this method, we can present the signals with virtual colors and facilitate the diagnosis of ischemic disease. So, a new method of 12-lead cardiac presentation is described that in fact present the 12-lead signals in two images. The result of this paper will present the indicators of sensitivity, specificity and accuracy in the context of disease diagnosis. This paper proposed a novel ECG imaging algorithm for classifying the normal and ischemic signals and 95.35% specificity, 96.79% sensitivity and 95.76% accuracy were achieved which are very much promising compared to the other methods and doctor’s accuracy.
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Affiliation(s)
- MOHAMMAD KARIMI MORIDANI
- Department of Biomedical Engineering, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran
| | - MAJID POULADIAN
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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16
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Ictal autonomic changes as a tool for seizure detection: a systematic review. Clin Auton Res 2018; 29:161-181. [PMID: 30377843 PMCID: PMC6459795 DOI: 10.1007/s10286-018-0568-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 10/07/2018] [Indexed: 12/05/2022]
Abstract
Purpose Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters.
Methods The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. Results Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h). Conclusions The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection. Electronic supplementary material The online version of this article (10.1007/s10286-018-0568-1) contains supplementary material, which is available to authorized users.
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17
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Presenting an efficient approach based on novel mapping for mortality prediction in intensive care unit cardiovascular patients. MethodsX 2018; 5:1291-1298. [PMID: 30364735 PMCID: PMC6197790 DOI: 10.1016/j.mex.2018.10.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 10/05/2018] [Indexed: 01/08/2023] Open
Abstract
Intensive care unit (ICU) experienced and skillful people in this field should be employed because the equipment, facilities, and admitted patients have more special conditions than other departments. Our goal provides the best quality according to the condition each patient and prevent many unnecessary costs for preventive treatment. In this paper, the proposed system will first receive the patient's vital signs, which are recorded by the ICU monitoring. After the necessary processing, in case of observing changes in the normal state, risk alarms are transmitted to the nursing station so that nurses become aware of this condition and take all equipment to return the patient to normal condition and prevent his death. The applied graph in this study examines patients at any moment and displays the patient's future condition in a schematic manner after precise analyses. In this algorithm, after calculating the R-R intervals in the electrocardiogram signal, RRIs are thrown into a risk plot (RP) by a projectile. Given the amount of projectile RRI, one of the stairs can host that amount. After a few moments by springs embedded under the stairs, the drain of RRIs is done by the kinetic energy stored in the springs towards the valley of life. If the accumulation of quantities in a stair is too much, the spring will not be able to project those RRIs. By examining this situation, we will introduce an index to determine the risk of death for all patients. The results of this paper show that when a person is in normal condition, there is no density in a certain stair and the ball or the projected RRIs are not limited to a stair. In general, the results of this paper show that the lower amount of RRI dispersion in the RP leads to greater risk of entry into the death range and as this amount decrease, an immediate consideration is required. In conclusion, if the precise prediction of the future condition of ICU patients is available to nurses and doctors, more facilities and equipment could be provided to save their lives. •We focused on nonlinear methods with new aspects to extract mentioned dynamics.•This method can reduce the number of ICU nurses and give the special facilities for high-risk patients.•Our results confirm that it is possible to predict mortality based on the dynamical characteristics of HRV.
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18
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Billeci L, Marino D, Insana L, Vatti G, Varanini M. Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis. PLoS One 2018; 13:e0204339. [PMID: 30252915 PMCID: PMC6155519 DOI: 10.1371/journal.pone.0204339] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 09/05/2018] [Indexed: 12/24/2022] Open
Abstract
Epilepsy is often associated with modifications in autonomic nervous system, which usually precede the onset of seizures of several minutes. Thus, there is a great interest in identifying these modifications enough time in advance to prevent a dangerous effect and to intervene. In addition, these changes can be a risk factor for epileptic patients and can increase the possibility of death. Notably autonomic changes associated to seizures are highly depended of seizure type, localization and lateralization. The aim of this study was to develop a patient-specific approach to predict seizures using electrocardiogram (ECG) features. Specifically, from the RR series, both time and frequency variables and features obtained by the recurrence quantification analysis were used. The algorithm was applied in a dataset of 15 patients with 38 different types of seizures. A feature selection step, was used to identify those features that were more significant in discriminating preictal and interictal phases. A preictal interval of 15 minutes was selected. A support vector machine (SVM) classifier was then built to classify preictal and interictal phases. First, a classifier was set up to classify preictal and interictal segments of each patient and an average sensibility of 89.06% was obtained, with a number of false positive per hour (FP/h) of 0.41. Then, in those patients who had at least 3 seizures, a double-cross-validation approach was used to predict unseen seizures on the basis of a training on previous ones. The results were quite variable according to seizure type, achieving the best performance in patients with more stereotypical seizure. The results of the proposed approach show that it is feasible to predict seizure in advance, considering patient-specific, and possible seizure specific, characteristics.
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Affiliation(s)
- Lucia Billeci
- Institute of Clinical Physiology, National Research Council of Italy (CNR), Pisa, Italy
- * E-mail:
| | - Daniela Marino
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Laura Insana
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Giampaolo Vatti
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Maurizio Varanini
- Institute of Clinical Physiology, National Research Council of Italy (CNR), Pisa, Italy
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19
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A Brain-Heart Biomarker for Epileptogenesis. J Neurosci 2018; 38:8473-8483. [PMID: 30150365 DOI: 10.1523/jneurosci.1130-18.2018] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 07/17/2018] [Accepted: 08/08/2018] [Indexed: 12/19/2022] Open
Abstract
Postinjury epilepsy is an potentially preventable sequela in as many as 20% of patients with brain insults. For these cases biomarkers of epileptogenesis are critical to facilitate identification of patients at high-risk of developing epilepsy and to introduce effective anti-epileptogenic interventions. Here, we demonstrate that delayed brain-heart coincidences serve as a reliable biomarker. In a murine model of post-infection acquired epilepsy, we used long-term simultaneous measurements of the brain activity via electroencephalography and autonomic cardiac activity via electrocardiography, in male mice, to quantitatively track brain-heart interactions during epileptogenesis. We find that abnormal cortical discharges precede abnormal fluctuations in the cardiac rhythm at the resolution of single beat-to-beat intervals. The delayed brain-heart coincidence is detectable as early as the onset of chronic measurements, 2-14 weeks before the first seizure, only in animals that become epileptic, and increases during epileptogenesis. Therefore, delayed brain-heart coincidence serves as a biomarker of epileptogenesis and could be used for phenotyping, diagnostic, and therapeutic purposes.SIGNIFICANCE STATEMENT No biomarker that readily predicts and tracks epileptogenesis currently exists for the wide range of human acquired epilepsies. Here, we used long-term measurements of brain and heart activity in a mouse model of post-infection acquired epilepsy to investigate the potential of brain-heart interaction as a biomarker of epileptogenesis. We found that delayed coincidences from brain to heart can clearly separate the mice that became epileptic from those that did not weeks before development of epilepsy. Our findings allow for phenotyping and tracking of epileptogenesis in this and likely other models of acquired epilepsy. Such capability is critical for efficient adjunctive treatment development and for tracking the efficacy of such treatments.
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Karuppiah Ramachandran VR, Alblas HJ, Le DV, Meratnia N. Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1698. [PMID: 29795031 PMCID: PMC6022213 DOI: 10.3390/s18061698] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 05/09/2018] [Accepted: 05/20/2018] [Indexed: 02/07/2023]
Abstract
In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.
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Affiliation(s)
| | - Huibert J Alblas
- Pervasive Systems Research Group, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
| | - Duc V Le
- Pervasive Systems Research Group, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
| | - Nirvana Meratnia
- Pervasive Systems Research Group, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
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21
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Bruno E, Biondi A, Richardson MP. Pre-ictal heart rate changes: A systematic review and meta-analysis. Seizure 2018; 55:48-56. [PMID: 29367145 DOI: 10.1016/j.seizure.2018.01.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 12/11/2017] [Accepted: 01/03/2018] [Indexed: 12/19/2022] Open
Abstract
PURPOSE To estimate the incidence of pre-ictal heart rate (HR) manifestations and to identify clinical and study-related factors modulating the estimate. METHODS We searched articles recording concurrent pre-ictal EEG and HR in adults and children with epilepsy. Pre-ictal HR changes were classified as HR reduction (HRR) or increase (HRI). Studies reporting the total number of seizures and the number of seizures with pre-ictal HR changes were included in a random-effects meta-analysis. A random-effects meta-regression was used to identify variables affecting study heterogeneity. RESULTS Thirty studies, including 1110 participants and 2957 seizures, were included. The meta-analysis showed a pooled incidence of pre-ictal HRI of 36/100 seizures (95% CI 22-50). The pre-ictal HRI incidence was 44/100 seizures (95% CI 33-55) in studies including temporal lobe epilepsy, 55/100 seizures (95% CI 41-68) in studies enrolling adults and 35/100 seizures (95% CI 16-58) when patients on antiepileptic drugs were included. The meta-regression showed that the age group, the length of the pre-ictal period, the incidence of ictal tachycardia and the time of onset of the pre-ictal HRI had a significant impact on estimates variability. The pooled incidence of pre-ictal HRR was 0/100 seizures (95% CI 0-1). CONCLUSION Review of bias evaluation and methods assessment disclosed several major limitations in the evidence-base. HR monitoring could be valuable to identify seizures prior to their apparent onset, opening the possibility to early interventions. Additional effort is necessary to delineate the target population who might benefit from its use and the mechanisms sustaining the pre-ictal cardiac changes.
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Affiliation(s)
- Elisa Bruno
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College London, UK
| | - Andrea Biondi
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College London, UK
| | - Mark P Richardson
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College London, UK.
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- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College London, UK
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