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Das S, Maharatna K. Filtering property of myelinated internode can change neural information representability and might trigger a compensatory action during demyelination. Sci Rep 2023; 13:22227. [PMID: 38097640 PMCID: PMC10721845 DOI: 10.1038/s41598-023-49208-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023] Open
Abstract
In this paper, for the first time, we showed that an Internode Segment (INS) of a myelinated axon acts as a lowpass filter, and its filter characteristics depend on the number of myelin turns. Consequently, we showed how the representability of a neural signal could be altered with myelin loss in pathological conditions involving demyelinating diseases. Contrary to the traditionally held viewpoint that myelin geometry of an INS is optimised for maximising Conduction Velocity (CV) of Action Potential (AP), our theory provides an alternative viewpoint that myelin geometry of an INS is optimised for maximizing representability of the stimuli a fibre is meant to carry. Subsequently, we show that this new viewpoint could explain hitherto unexplained experimentally observed phenomena such as, shortening of INS length during demyelination and remyelination, and non-uniform distribution of INS in the central nervous system fibres and associated changes in diameter of nodes of ranvier along an axon. Finally, our theory indicates that a compensatory action could take place during demyelination up to a certain number of loss of myelin turns to preserve the neural signal representability by simultaneous linear scaling of the length of an INS and the inner radius of the fibre.
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Affiliation(s)
- Sarbani Das
- School of Electronics and Computer Science, University of Southampton, University Road, Southampton, SO17 1BJ, UK
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, University Road, Southampton, SO17 1BJ, UK.
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Alotaibi N, Bakheet D, Konn D, Vollmer B, Maharatna K. Cognitive Outcome Prediction in Infants With Neonatal Hypoxic-Ischemic Encephalopathy Based on Functional Connectivity and Complexity of the Electroencephalography Signal. Front Hum Neurosci 2022; 15:795006. [PMID: 35153702 PMCID: PMC8830486 DOI: 10.3389/fnhum.2021.795006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/10/2021] [Indexed: 12/03/2022] Open
Abstract
Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error (RMSE), 12.07 mean absolute error (MAE), and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.
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Affiliation(s)
- Noura Alotaibi
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Dalal Bakheet
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Daniel Konn
- Clinical Neurophysiology, University Hospital Southampton, Southampton, United Kingdom
| | - Brigitte Vollmer
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Paediatric Neurology, Southampton Children’s Hospital, Southampton, United Kingdom
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
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Wiles BM, Roberts PR, Allavatam V, Acharyya A, Vemishetty N, ElRefai M, Wilson DG, Maharatna K, Chen H, Morgan JM. Personalized subcutaneous implantable cardioverter-defibrillator sensing vectors generated by mathematical rotation increase device eligibility whilst preserving device performance. Europace 2022; 24:1267-1275. [PMID: 35022725 DOI: 10.1093/europace/euab310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 11/30/2021] [Indexed: 11/14/2022] Open
Abstract
AIMS Approximately 5.7% of potential subcutaneous implantable cardioverter-defibrillator (S-ICD) recipients are ineligible by virtue of their vector morphology, with higher rates of ineligibility observed in some at-risk groups. Mathematical vector rotation is a novel technique that can generate a personalized sensing vector, one with maximal R:T ratio, using electrocardiogram (ECG) signal recorded from the present S-ICD location. METHODS AND RESULTS A cohort of S-ICD ineligible patients were identified through ECG screening of ICD patients with no ventricular pacing requirement and their personalized vectors were generated using ECG signal from a Holter monitor. Subcutaneous ICD eligibility in this cohort was then recalculated. In a separate cohort, episodes of arrhythmia were recorded in patients undergoing arrhythmia induction, and arrhythmia detection in standard S-ICD vectors was compared to rotated vectors using an S-ICD simulator. Ninety-two participants (mean age 64.9 ± 2.7 years) underwent screening and 5.4% were found to be S-ICD ineligible. Personalized vector generation increased the R:T ratio in these vectors from 2.21 to 7.21 (4.54-9.88, P < 0.001) increasing the cohort eligibility from 94.6% to 100%. Rotated S-ICD vectors also showed high ventricular fibrillation (VF) detection sensitivity (97.8%), low time to VF detection (6.1 s), and excellent tachycardia discrimination (sensitivity 96%, specificity 88%), with no significant differences between rotated and standard vectors. CONCLUSION In S-ICD ineligible patients, mathematical vector rotation can generate a personalized vector that is associated with a significant increase in R:T ratio, resulting in universal device eligibility in our cohort. Ventricular fibrillation detection efficacy, time to VF detection, and tachycardia discrimination were not affected by vector rotation.
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Affiliation(s)
- Benedict M Wiles
- Cardiac Rhythm Management Research Department, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.,Faculty of Medicine, University of Southampton, Southampton, UK
| | - Paul R Roberts
- Cardiac Rhythm Management Research Department, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.,Faculty of Medicine, University of Southampton, Southampton, UK
| | | | - Amit Acharyya
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Naresh Vemishetty
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Mohamed ElRefai
- Cardiac Rhythm Management Research Department, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.,Faculty of Medicine, University of Southampton, Southampton, UK
| | - David G Wilson
- Cardiology Department, Worcestershire Acute Hospitals NHS Foundation Trust, Worcester, UK
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Hanjie Chen
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | | | - John M Morgan
- Faculty of Medicine, University of Southampton, Southampton, UK.,Cardiac Rhythm Management, Boston Scientific, Marlborough, MA, USA
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Chen H, Das S, Morgan JM, Maharatna K. Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering. Comput Biol Med 2021; 142:105180. [PMID: 35026575 DOI: 10.1016/j.compbiomed.2021.105180] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 12/24/2021] [Accepted: 12/24/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND OBJECTIVE Prediction and classification of Ventricular Arrhythmias (VA) may allow clinicians sufficient time to intervene for stopping its escalation to Sudden Cardiac Death (SCD). This paper proposes a novel method for predicting VA and classifying its type, in particular, the fatal VA even before the event occurs. METHODS A statistical index J based on the combination of phase-space reconstruction (PSR) and box counting has been used to predict VA. The fuzzy c-means (FCM) clustering technique is applied for the classification of impending VA. RESULTS 32 healthy and 32 arrhythmic subjects from two open databases - PTB Diagnostic database (PTBDB) and CU Ventricular Tachyarrhythmia (CUDB) database respectively; were used to validate our proposed method. Our method showed average prediction time of approximately 5 min (4.97 min) for impending VA in the tested dataset while classifying four types of VA (VA without ventricular premature beats (VPBs), ventricular fibrillation (VF), ventricular tachycardia (VT), and VT followed by VF) with an average 4 min (approximately) before the VA onset, i.e., after 1 min of the prediction time point with average accuracy of 98.4%, a sensitivity of 97.5% and specificity of 99.1%. CONCLUSIONS The results obtained can be used in clinical practice after rigorous clinical trial to advance technologies such as implantable cardioverter defibrillator (ICD) that can help to preempt the occurrence of fatal ventricular arrhythmia - a main cause of SCD.
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Affiliation(s)
- Hanjie Chen
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Saptarshi Das
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Penryn, TR10 9FE, UK; Institute for Data Science and Artificial Intelligence, University of Exeter, North Park Road, Exeter, Devon, EX4 4QE, UK.
| | - John M Morgan
- Faculty of Medicine, University of Southampton, Tremona Road, Southampton, SO17 1BJ, UK.
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
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Chen H, Wiles BM, Roberts PR, Morgan JM, Maharatna K. A new algorithm to reduce T-wave over-sensing based on phase space reconstruction in S-ICD system. Comput Biol Med 2021; 137:104804. [PMID: 34478924 DOI: 10.1016/j.compbiomed.2021.104804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND AND OBJECTIVE The subcutaneous implantable cardioverter defibrillator (S-ICD) reduces mortality in individuals at high risk of sudden arrhythmic death, by rapid defibrillation of life-threatening arrhythmia. Unfortunately, S-ICD recipients are also at risk of inappropriate shock therapies, which themselves are associated with increased rates of mortality and morbidity. The commonest cause of inappropriate shock therapies is T wave oversensing (TWOS), where T waves are incorrectly counted as R waves leading to an overestimation of heart rate. It is important to develop a method to reduce TWOS and improve the accuracy of R-peak detection in S-ICD system. METHODS This paper introduces a novel algorithm to reduce TWOS based on phase space reconstruction (PSR); a common method used to analyse the chaotic characteristics of non-linear signals. RESULTS The algorithm was evaluated against 34 records from University Hospital Southampton (UHS) and all 48 records from the MIT-BIH arrhythmia database. In the UHS analysis we demonstrated a sensitivity of 99.88%, a positive predictive value of 99.99% and an accuracy of 99.88% with reductions in TWOS episodes (from 166 to 0). Whilst in the MIT-BIH analysis we demonstrated a sensitivity of 99.87%, a positive predictive value of 99.99% and an accuracy of 99.91% for R wave detection. The average processing time for 1 min ECG signals from all records is 2.9 s. CONCLUSIONS Our algorithm is sensitive for R-wave detection and can effectively reduce the TWOS with low computational complexity, and it would therefore have the potential to reduce inappropriate shock therapies in S-ICD recipients, which would significantly reduce shock related morbidity and mortality, and undoubtedly improving patient's quality of life.
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Affiliation(s)
- Hanjie Chen
- School of Electronics and Computer Science, University of Southampton, Southampton, UK.
| | - Benedict M Wiles
- Cardiac Rhythm Management Research, University Hospital Southampton NHS Foundation Trust, Southampton, UK; Faculty of Medicine, University of Southampton, Southampton, UK
| | - Paul R Roberts
- Cardiac Rhythm Management Research, University Hospital Southampton NHS Foundation Trust, Southampton, UK; Faculty of Medicine, University of Southampton, Southampton, UK
| | - John M Morgan
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
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Alotaibi N, Maharatna K. Classification of Autism Spectrum Disorder From EEG-Based Functional Brain Connectivity Analysis. Neural Comput 2021; 33:1914-1941. [PMID: 34411269 DOI: 10.1162/neco_a_01394] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 02/04/2021] [Indexed: 11/04/2022]
Abstract
Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.
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Affiliation(s)
- Noura Alotaibi
- Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Koushik Maharatna
- Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
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Gutierrez Nuno RA, Chung CHR, Maharatna K. Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction. J Neural Eng 2020; 18. [PMID: 33326940 DOI: 10.1088/1741-2552/abd462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 12/16/2020] [Indexed: 11/11/2022]
Abstract
In this work, we proposed a novel architecture for real-time quantitative characterization of functional brain connectivity networks derived from Electroencephalogram (EEG). It consists of two main parts - calculation of Phase Lag Index (PLI) to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131µs, utilizes 71% logic resources, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS Application Specific Integrated Circuit (ASIC) technology.
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Affiliation(s)
- Rafael Angel Gutierrez Nuno
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Chi Hang Raphael Chung
- University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Koushik Maharatna
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Das S, Maharatna K. An automated toolchain for quantitative characterisation of structural connectome from MRI based on non-anatomical cortical parcellation. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:5653-5656. [PMID: 33019259 DOI: 10.1109/embc44109.2020.9176642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain connectivity analysis is a new multidisciplinary approach in neuroscience for determining neurological disorders from brain imaging data. But, there is no end-to-end toolchain that processes raw MRI data and extracts brain connectivity network metrics. Again, the existing method of cortical parcellation from MRI data is mainly based on fixed Brodmann atlas; which does not support neonate's brain or adult's brain with neuroplasticity anomalies. In this work, we design an end-to-end toolchain that processes raw MRI data and generates network metrics for brain connectivity analysis using non-anatomical equal-area parcellation. We process the structural and diffusion MRI data to generate the parcellated and segmented image, extract white matter tracks and build structural connectome and then interface it with Brain Connectivity Toolbox to extract graph theory measures.Clinical relevance An automated tool for end-to-end processing of MRI data to brain connectivity pattern extraction and its quantitative characterisation for diagnosing brain disorder.
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Chen H, Maharatna K. An Automatic R and T Peak Detection Method Based on the Combination of Hierarchical Clustering and Discrete Wavelet Transform. IEEE J Biomed Health Inform 2020; 24:2825-2832. [DOI: 10.1109/jbhi.2020.2973982] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Gutierrez Nuno RA, Maharatna K. A phase lag index hardware calculation for real-time electroencephalography studies. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:644-647. [PMID: 31945980 DOI: 10.1109/embc.2019.8857652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Among the different techniques used for the analysis of electroencephalograms, the phase lag index has become an important method for the calculation of the functional brain connectivity. Currently, this method is implemented offline due to its high computational complexity restricting it from real-time applications that would require an online neurofeedback. In this paper, we propose a new architecture to calculate the phase lag index of electroencephalograms in real-time. As a proof of concept, a 32 bit and 16-channel system running at 188.32 MHz was synthesized on a Stratix IV GX FPGA. The system was tested and the simulations demonstrated that the system could perform the calculation of the Phase lag index at least 66 times faster than the MATLAB software with a mean square error of less than 5.72×10-6.
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Panwar M, Biswas D, Bajaj H, Jobges M, Turk R, Maharatna K, Acharyya A. Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation. IEEE Trans Biomed Eng 2019; 66:3026-3037. [DOI: 10.1109/tbme.2019.2899927] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Vemishetty N, Gunukula RL, Acharyya A, Puddu PE, Das S, Maharatna K. Phase Space Reconstruction Based CVD Classifier Using Localized Features. Sci Rep 2019; 9:14593. [PMID: 31601877 PMCID: PMC6787214 DOI: 10.1038/s41598-019-51061-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 08/23/2019] [Indexed: 01/06/2023] Open
Abstract
This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients’ data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.
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Affiliation(s)
- Naresh Vemishetty
- Department of Electrical Engineering, IIT Hyderabad, Hyderabad, 502285, India
| | | | - Amit Acharyya
- Department of Electrical Engineering, IIT Hyderabad, Hyderabad, 502285, India.
| | - Paolo Emilio Puddu
- Department of Cardiovascular Sciences, Sapienza University of Rome, Viale del Policlinico 155, I-00161, Rome, Italy
| | - Saptarshi Das
- Department of Mathematics, University of Exeter, Cornwall, TR10 9FE, UK
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
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Acharyya A, Jadhav PN, Bono V, Maharatna K, Naik GR. Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG. Comput Methods Programs Biomed 2018; 158:123-133. [PMID: 29544778 DOI: 10.1016/j.cmpb.2018.02.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 12/31/2017] [Accepted: 02/02/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode. METHODS In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik für Epileptologie, Universität Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform. RESULTS Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware design based on the proposed methodology consumes 75 micro-Watt power. CONCLUSIONS The automated methodology proposed in this paper, unlike the state of the art methods, can remove blink and muscular artifacts real time without the need of any extra electrode. Its reliability and robustness is also established after exhaustive simulation study and analysis on both simulated and real data. We believe the proposed methodology would be useful in next generation personalized pervasive healthcare for Brain Computer Interface and neuro-developmental disorder diagnosis and treatment.
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Affiliation(s)
- Amit Acharyya
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India.
| | - Pranit N Jadhav
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India.
| | - Valentina Bono
- School of Electronic & Computer Science, University of Southampton, Southampton, UK.
| | - Koushik Maharatna
- School of Electronic & Computer Science, University of Southampton, Southampton, UK.
| | - Ganesh R Naik
- MARCS Institute Western Sydney University Kingswood, NSW - 2747, Australia.
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Panwar M, Dyuthi SR, Chandra Prakash K, Biswas D, Acharyya A, Maharatna K, Gautam A, Naik GR. CNN based approach for activity recognition using a wrist-worn accelerometer. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:2438-2441. [PMID: 29060391 DOI: 10.1109/embc.2017.8037349] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.
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Jouen AL, Narzisi A, Xavier J, Tilmont E, Bodeau N, Bono V, Ketem-Premel N, Anzalone S, Maharatna K, Chetouani M, Muratori F, Cohen D. GOLIAH (Gaming Open Library for Intervention in Autism at Home): a 6-month single blind matched controlled exploratory study. Child Adolesc Psychiatry Ment Health 2017; 11:17. [PMID: 28344643 PMCID: PMC5361849 DOI: 10.1186/s13034-017-0154-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 03/07/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To meet the required hours of intensive intervention for treating children with autism spectrum disorder (ASD), we developed an automated serious gaming platform (11 games) to deliver intervention at home (GOLIAH) by mapping the imitation and joint attention (JA) subset of age-adapted stimuli from the Early Start Denver Model (ESDM) intervention. Here, we report the results of a 6-month matched controlled exploratory study. METHODS From two specialized clinics, we included 14 children (age range 5-8 years) with ASD and 10 controls matched for gender, age, sites, and treatment as usual (TAU). Participants from the experimental group received in addition to TAU four 30-min sessions with GOLIAH per week at home and one at hospital for 6 months. Statistics were performed using Linear Mixed Models. RESULTS Children and parents participated in 40% of the planned sessions. They were able to use the 11 games, and participants trained with GOLIAH improved time to perform the task in most JA games and imitation scores in most imitation games. GOLIAH intervention did not affect Parental Stress Index scores. At end-point, we found in both groups a significant improvement for Autism Diagnostic Observation Schedule scores, Vineland socialization score, Parental Stress Index total score, and Child Behavior Checklist internalizing, externalizing and total problems. However, we found no significant change for by time × group interaction. CONCLUSIONS Despite the lack of superiority of TAU + GOLIAH versus TAU, the results are interesting both in terms of changes by using the gaming platform and lack of parental stress increase. A large randomized controlled trial with younger participants (who are the core target of ESDM model) is now discussed. This should be facilitated by computing GOLIAH for a web platform. Trial registration Clinicaltrials.gov NCT02560415.
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Affiliation(s)
- Anne-Lise Jouen
- 0000 0001 1955 3500grid.5805.8Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, 75005 Paris, France
| | - Antonio Narzisi
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Viale del Tirreno, 331, 56018 Calambrone, Pisa, Italy
| | - Jean Xavier
- 0000 0001 1955 3500grid.5805.8Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie, 75013 Paris, France
| | - Elodie Tilmont
- 0000 0001 1955 3500grid.5805.8Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, 75005 Paris, France ,0000 0001 1955 3500grid.5805.8Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie, 75013 Paris, France
| | - Nicolas Bodeau
- 0000 0001 1955 3500grid.5805.8Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie, 75013 Paris, France
| | - Valentina Bono
- 0000 0004 1936 9297grid.5491.9School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ UK
| | - Nabila Ketem-Premel
- 0000 0001 1955 3500grid.5805.8Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie, 75013 Paris, France
| | - Salvatore Anzalone
- 0000 0001 1955 3500grid.5805.8Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, 75005 Paris, France
| | - Koushik Maharatna
- 0000 0004 1936 9297grid.5491.9School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ UK
| | - Mohamed Chetouani
- 0000 0001 1955 3500grid.5805.8Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, 75005 Paris, France
| | - Filippo Muratori
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Viale del Tirreno, 331, 56018 Calambrone, Pisa, Italy
| | - David Cohen
- 0000 0001 1955 3500grid.5805.8Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, 75005 Paris, France ,0000 0001 1955 3500grid.5805.8Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie, 75013 Paris, France
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Maheshwari S, Acharyya A, Puddu PE, Mazomenos EB, Schiariti M, Maharatna K. Robust and accurate personalised reconstruction of standard 12-lead system from Frank vectorcardiographic system. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2016. [DOI: 10.1080/21681163.2014.931029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Bono V, Das S, Jamal W, Maharatna K. Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG. J Neurosci Methods 2016; 267:89-107. [DOI: 10.1016/j.jneumeth.2016.04.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 03/13/2016] [Accepted: 04/06/2016] [Indexed: 10/21/2022]
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Bono V, Narzisi A, Jouen AL, Tilmont E, Hommel S, Jamal W, Xavier J, Billeci L, Maharatna K, Wald M, Chetouani M, Cohen D, Muratori F. GOLIAH: A Gaming Platform for Home-Based Intervention in Autism - Principles and Design. Front Psychiatry 2016; 7:70. [PMID: 27199777 PMCID: PMC4848303 DOI: 10.3389/fpsyt.2016.00070] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 04/07/2016] [Indexed: 11/13/2022] Open
Abstract
Children with Autism need intensive intervention and this is challenging in terms of manpower, costs, and time. Advances in Information Communication Technology and computer gaming may help in this respect by creating a nomadically deployable closed-loop intervention system involving the child and active participation of parents and therapists. An automated serious gaming platform enabling intensive intervention in nomadic settings has been developed by mapping two pivotal skills in autism spectrum disorder: Imitation and Joint Attention (JA). Eleven games - seven Imitations and four JA - were derived from the Early Start Denver Model. The games involved application of visual and audio stimuli with multiple difficulty levels and a wide variety of tasks and actions pertaining to the Imitation and JA. The platform runs on mobile devices and allows the therapist to (1) characterize the child's initial difficulties/strengths, ensuring tailored and adapted intervention by choosing appropriate games and (2) investigate and track the temporal evolution of the child's progress through a set of automatically extracted quantitative performance metrics. The platform allows the therapist to change the game or its difficulty levels during the intervention depending on the child's progress. Performance of the platform was assessed in a 3-month open trial with 10 children with autism (Trial ID: NCT02560415, Clinicaltrials.gov). The children and the parents participated in 80% of the sessions both at home (77.5%) and at the hospital (90%). All children went through all the games but, given the diversity of the games and the heterogeneity of children profiles and abilities, for a given game the number of sessions dedicated to the game varied and could be tailored through automatic scoring. Parents (N = 10) highlighted enhancement in the child's concentration, flexibility, and self-esteem in 78, 89, and 44% of the cases, respectively, and 56% observed an enhanced parents-child relationship. This pilot study shows the feasibility of using the developed gaming platform for home-based intensive intervention. However, the overall capability of the platform in delivering intervention needs to be assessed in a bigger open trial.
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Affiliation(s)
- Valentina Bono
- Electronics and Computer Science, University of Southampton , Southampton , UK
| | - Antonio Narzisi
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation , Pisa , Italy
| | - Anne-Lise Jouen
- Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie , Paris , France
| | - Elodie Tilmont
- Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, Paris, France; Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie, Paris, France
| | - Stephane Hommel
- Electronics and Computer Science, University of Southampton , Southampton , UK
| | - Wasifa Jamal
- Electronics and Computer Science, University of Southampton , Southampton , UK
| | - Jean Xavier
- Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie , Paris , France
| | - Lucia Billeci
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation , Pisa , Italy
| | - Koushik Maharatna
- Electronics and Computer Science, University of Southampton , Southampton , UK
| | - Mike Wald
- Electronics and Computer Science, University of Southampton , Southampton , UK
| | - Mohamed Chetouani
- Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie , Paris , France
| | - David Cohen
- Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, Paris, France; Department of Child and Adolescent Psychiatry, APHP, Groupe Hospitalier Pitié-Salpêtrière et University Pierre and Marie Curie, Paris, France
| | - Filippo Muratori
- Department of Developmental Neuroscience, IRCCS Stella Maris Foundation , Pisa , Italy
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Li S, Dasmahapatra S, Maharatna K. Dynamical System Approach for Edge Detection Using Coupled FitzHugh-Nagumo Neurons. IEEE Trans Image Process 2015; 24:5206-5219. [PMID: 26276989 DOI: 10.1109/tip.2015.2467206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The prospect of emulating the impressive computational capabilities of biological systems has led to considerable interest in the design of analog circuits that are potentially implementable in very large scale integration CMOS technology and are guided by biologically motivated models. For example, simple image processing tasks, such as the detection of edges in binary and grayscale images, have been performed by networks of FitzHugh-Nagumo-type neurons using the reaction-diffusion models. However, in these studies, the one-to-one mapping of image pixels to component neurons makes the size of the network a critical factor in any such implementation. In this paper, we develop a simplified version of the employed reaction-diffusion model in three steps. In the first step, we perform a detailed study to locate this threshold using continuous Lyapunov exponents from dynamical system theory. Furthermore, we render the diffusion in the system to be anisotropic, with the degree of anisotropy being set by the gradients of grayscale values in each image. The final step involves a simplification of the model that is achieved by eliminating the terms that couple the membrane potentials of adjacent neurons. We apply our technique to detect edges in data sets of artificially generated and real images, and we demonstrate that the performance is as good if not better than that of the previous methods without increasing the size of the network.
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Kavi L, Gamble J, Dastagir N, Gilbert K, Furniss G, Rosengarten J, Shunmugam S, Signy J, Umar F, Providência R, Almeida T, Newton J, Nuttall M, Opie-Moran M, Low DA, Nicholson L, Toora D, Caldow E, Gregory K, Khiani R, Herring N, Foley P, Ginks M, Rajappan K, Bashir Y, Betts T, Salinet J, Li X, Vanheusden F, Almeida T, Chu G, Stafford P, Schlindwein F, Ng G, Hogarth A, MacDonald W, Lewis N, Tan L, Tayebjee M, Villaquiran J, Newcombe D, Lines I, Dalrymple-Hay M, Haywood G, Chui K, Dima S, Panagiotou C, Maharatna K, Curzen N, Morgan J, Veasey R, Sugihara C, Anderson S, Furniss S, Sulke N, Puri N, Steele J, Furniss S, Sulke A, Patel N, Veasey R, Taylor R, Stegemann B, Marshall H, Flannigan S, Leyva F, Rogers D, Cobb V, Babu G, Mann I, Bronis K, Posdziech V, Lambiase P, Ahsan S, Segal O, Lowe M, Rowland E, Khan F, Chow A, Chu G, Salinet J, Vanheusden F, Li X, Tuan J, Stafford P, Schlindwein F, Ng GA. Posters 159Misdiagnosed, misbelieved and misdirected; largest uk study casts doubt on some long held but poorly validated assumptions about the pots population and suggests improvements in care pathways and service provision60An acute comparison of different strategies for targeting the left ventricular lead for cardiac resynchronisation therapy61Relationship of phase singularities and high dominant frequency regions during persistent atrial fibrillation in humans62Restoration of sinus rhythm results in early and late improvements in the functional reserve of the heart following direct current cardioversion of persistent af: fresh-af63Non-concomitant hybrid ablation using the estech cobra device for the treatment of longstanding persistent atrial fibrillation: an initial single-centre experience64Artificial intelligence outperforms manual ecg scoring in the detection of arrhythmia substrate65Single centre experience and outcome of persistent af ablation using nmarq catheter: 2 year follow up66The growing burden of atrial fibrillation and management at a typical district general hospital67Haemodynamic effects of single-vein, simultaneous, multipoint pacing compared with bipolar pacing in patients undergoing cardiac resynchronisation therapy68Is multisite pacing of interest in cardiac resynchronization therapy? teachings from a long-term follow-up of a cohort of patients implanted with triventricular pacing systems69Differences in fractionated electrogram detection: a direct quantitative comparison between navx and carto: Table 1. Europace 2015. [DOI: 10.1093/europace/euv329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Chatterjee SK, Das S, Maharatna K, Masi E, Santopolo L, Mancuso S, Vitaletti A. Exploring strategies for classification of external stimuli using statistical features of the plant electrical response. J R Soc Interface 2015; 12:20141225. [PMID: 25631569 DOI: 10.1098/rsif.2014.1225] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli--sodium chloride (NaCl), sulfuric acid (H₂SO₄) and ozone (O₃). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.
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Affiliation(s)
- Shre Kumar Chatterjee
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Saptarshi Das
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Elisa Masi
- Department of Agri-food Production and Environmental Science (DISPAA), University of Florence, viale delle Idee 30, Sesto Fiorentino, Florence 50019, Italy
| | - Luisa Santopolo
- Department of Agri-food Production and Environmental Science (DISPAA), University of Florence, viale delle Idee 30, Sesto Fiorentino, Florence 50019, Italy
| | - Stefano Mancuso
- Department of Agri-food Production and Environmental Science (DISPAA), University of Florence, viale delle Idee 30, Sesto Fiorentino, Florence 50019, Italy
| | - Andrea Vitaletti
- WLAB S.r.L., via Adolfo Ravà 124, Rome 00142, Italy DIAG, SAPIENZA Università di Roma, via Ariosto 25, Rome 00185, Italy
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Jamal W, Das S, Maharatna K. Existence of millisecond-order stable states in time-varying phase synchronization measure in EEG signals. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2013:2539-42. [PMID: 24110244 DOI: 10.1109/embc.2013.6610057] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, we have developed a new measure of understanding the temporal evolution of phase synchronization for EEG signals using cross-electrode information. From this measure it is found that there exists a small number of well-defined phase-synchronized states, each of which is stable for few milliseconds during the execution of a face perception task. We termed these quasi-stable states as synchrostates. We used k-means clustering algorithms to estimate the optimal number of synchrostates from 100 trials of EEG signals over 128 channels. Our results show that these synchrostates exist consistently in all the different trials. It is also found that from the onset of the stimulus, switching between these synchrostates results in well-behaved temporal sequence with repeatability which may be indicative of the dynamics of the cognitive process underlying that task. Therefore these synchrostates and their temporal switching sequences may be used as a new measure of the stability of phase synchrony and information exchange between different regions of a human brain.
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Jamal W, Das S, Maharatna K, Apicella F, Chronaki G, Sicca F, Cohen D, Muratori F. On the existence of synchrostates in multichannel EEG signals during face-perception tasks. Biomed Phys Eng Express 2015. [DOI: 10.1088/2057-1976/1/1/015002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Mazomenos EB, Biswas D, Cranny A, Rajan A, Maharatna K, Achner J, Klemke J, Jobges M, Ortmann S, Langendorfer P. Detecting Elementary Arm Movements by Tracking Upper Limb Joint Angles With MARG Sensors. IEEE J Biomed Health Inform 2015; 20:1088-99. [PMID: 25966489 DOI: 10.1109/jbhi.2015.2431472] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close ( ±6%) to the average value, for different task durations further attesting to the algorithms robustness.
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Koulaouzidis G, Das S, Cappiello G, Mazomenos EB, Maharatna K, Puddu PE, Morgan JM. Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG. Int J Cardiol 2014; 182:38-43. [PMID: 25576717 DOI: 10.1016/j.ijcard.2014.12.067] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Accepted: 12/21/2014] [Indexed: 10/24/2022]
Abstract
AIM To develop a statistical index based on the phase space reconstruction (PSR) of the electrocardiogram (ECG) for the accurate and timely diagnosis of ventricular tachycardia (VT) and ventricular fibrillation (VF). METHODS Thirty-two ECGs with sinus rhythm (SR) and 32 ECGs with VT/VF were analyzed using the PSR technique. Firstly, the method of time delay embedding were employed with the insertion of delay "τ" in the original time-series X(t), which produces the Y(t)=X(t-τ). Afterwards, a PSR diagram was reconstructed by plotting Y(t) against X(t). The method of box counting was applied to analyze the behavior of the PSR trajectories. Measures as mean (μ), standard deviation (σ) and coefficient of variation (CV=σ/μ), kurtosis (β) for the box counting of PSR diagrams were reported. RESULTS During SR, CV was always <0.05, while with the onset of arrhythmia CV increased >0.05. A similar pattern was observed with β, where <6 was considered as the cut-off point between SR and VT/VF. Therefore, the upper threshold for SR was considered CVth=0.05 and βth<6. For optimisation of the accuracy, a new index (J) was proposed: J=wCVCVth+1-wββth. During SR the upper limit of J was the value of 1. Furthermore CV, β and J crossed the cut-off point timely before the onset of arrhythmia (average time: 4min 31s; SD: 2min 30s); allowing sufficient time for preventive therapy. CONCLUSION The J index improved ECG utility for arrhythmia monitoring and detection utility, allowing the prompt and accurate diagnosis of ventricular arrhythmias.
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Affiliation(s)
- George Koulaouzidis
- University Hospital Southampton NHS Foundation Trust, UK; Department of Cardiology, Castle Hill Hospital, Hull York Medical School (at University of Hull), Kingston Upon Hull, UK
| | - Saptarshi Das
- School of Electronics and Computer Science, University of Southampton, UK
| | - Grazia Cappiello
- School of Electronics and Computer Science, University of Southampton, UK
| | | | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, UK
| | - Paolo E Puddu
- Department of Cardiovascular Sciences, Sapienza University of Rome, Italy
| | - John M Morgan
- University Hospital Southampton NHS Foundation Trust, UK
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Cappiello G, Das S, Mazomenos EB, Maharatna K, Koulaouzidis G, Morgan J, Puddu PE. A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits. Physiol Meas 2014; 36:107-31. [DOI: 10.1088/0967-3334/36/1/107] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Biswas D, Corda D, Baldus G, Cranny A, Maharatna K, Achner J, Klemke J, Jöbges M, Ortmann S. Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist. Physiol Meas 2014; 35:1751-68. [PMID: 25119720 DOI: 10.1088/0967-3334/35/9/1751] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91-99% for healthy subjects and 70-85% for stroke patients.
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Affiliation(s)
- Dwaipayan Biswas
- Faculty of Physical Sciences and Engineering, University of Southampton, Hampshire, UK
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Jamal W, Das S, Oprescu IA, Maharatna K, Apicella F, Sicca F. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng 2014. [PMID: 24981017 DOI: 10.1088/1741‐2560/11/4/046019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. APPROACH Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. MAIN RESULTS The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. SIGNIFICANCE The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.
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Affiliation(s)
- Wasifa Jamal
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
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Jamal W, Das S, Oprescu IA, Maharatna K, Apicella F, Sicca F. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng 2014; 11:046019. [PMID: 24981017 DOI: 10.1088/1741-2560/11/4/046019] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. APPROACH Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. MAIN RESULTS The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. SIGNIFICANCE The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.
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Affiliation(s)
- Wasifa Jamal
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
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Koulaouzidis G, Das S, Cappiello G, Mazomenos EB, Maharatna K, Morgan J. A novel approach for the diagnosis of ventricular tachycardia based on phase space reconstruction of ECG. Int J Cardiol 2014; 172:e31-3. [DOI: 10.1016/j.ijcard.2013.12.088] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2013] [Accepted: 12/21/2013] [Indexed: 10/25/2022]
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Bono V, Mazomenos EB, Taihai Chen, Rosengarten JA, Acharyya A, Maharatna K, Morgan JM, Curzen N. Development of an Automated Updated Selvester QRS Scoring System Using SWT-Based QRS Fractionation Detection and Classification. IEEE J Biomed Health Inform 2014; 18:193-204. [DOI: 10.1109/jbhi.2013.2263311] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Dima SM, Panagiotou C, Mazomenos EB, Rosengarten JA, Maharatna K, Gialelis JV, Curzen N, Morgan J. On the Detection of Myocadial Scar Based on ECG/VCG Analysis. IEEE Trans Biomed Eng 2013; 60:3399-409. [DOI: 10.1109/tbme.2013.2279998] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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34
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Das S, Maharatna K. Fractional dynamical model for the generation of ECG like signals from filtered coupled Van-der Pol oscillators. Comput Methods Programs Biomed 2013; 112:490-507. [PMID: 24028797 DOI: 10.1016/j.cmpb.2013.08.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2012] [Revised: 08/07/2013] [Accepted: 08/20/2013] [Indexed: 06/02/2023]
Abstract
In this paper, an incommensurate fractional order (FO) model has been proposed to generate ECG like waveforms. Earlier investigation of ECG like waveform generation is based on two identical Van-der Pol (VdP) family of oscillators, which are coupled by time delays and gains. In this paper, we suitably modify the three state equations corresponding to the nonlinear cross-product of states, time delay coupling of the two oscillators and low-pass filtering, using the concept of fractional derivatives. Our results show that a wide variety of ECG like waveforms can be simulated from the proposed generalized models, characterizing heart conditions under different physiological conditions. Such generalization of the modelling of ECG waveforms may be useful to understand the physiological process behind ECG signal generation in normal and abnormal heart conditions. Along with the proposed FO models, an optimization based approach is also presented to estimate the VdP oscillator parameters for representing a realistic ECG like signal.
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Affiliation(s)
- Saptarshi Das
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom.
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35
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Maheshwari S, Acharyya A, Puddu PE, Mazomenos EB, Leekha G, Maharatna K, Schiariti M. An automated algorithm for online detection of fragmented QRS and identification of its various morphologies. J R Soc Interface 2013; 10:20130761. [PMID: 24132202 DOI: 10.1098/rsif.2013.0761] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Fragmented QRS (f-QRS) has been proven to be an efficient biomarker for several diseases, including remote and acute myocardial infarction, cardiac sarcoidosis, non-ischaemic cardiomyopathy, etc. It has also been shown to have higher sensitivity and/or specificity values than the conventional markers (e.g. Q-wave, ST-elevation, etc.) which may even regress or disappear with time. Patients with such diseases have to undergo expensive and sometimes invasive tests for diagnosis. Automated detection of f-QRS followed by identification of its various morphologies in addition to the conventional ECG feature (e.g. P, QRS, T amplitude and duration, etc.) extraction will lead to a more reliable diagnosis, therapy and disease prognosis than the state-of-the-art approaches and thereby will be of significant clinical importance for both hospital-based and emerging remote health monitoring environments as well as for implanted ICD devices. An automated algorithm for detection of f-QRS from the ECG and identification of its various morphologies is proposed in this work which, to the best of our knowledge, is the first work of its kind. Using our recently proposed time-domain morphology and gradient-based ECG feature extraction algorithm, the QRS complex is extracted and discrete wavelet transform (DWT) with one level of decomposition, using the 'Haar' wavelet, is applied on it to detect the presence of fragmentation. Detailed DWT coefficients were observed to hypothesize the postulates of detection of all types of morphologies as reported in the literature. To model and verify the algorithm, PhysioNet's PTB database was used. Forty patients were randomly selected from the database and their ECG were examined by two experienced cardiologists and the results were compared with those obtained from the algorithm. Out of 40 patients, 31 were considered appropriate for comparison by two cardiologists, and it is shown that 334 out of 372 (89.8%) leads from the chosen 31 patients complied favourably with our proposed algorithm. The sensitivity and specificity values obtained for the detection of f-QRS were 0.897 and 0.899, respectively. Automation will speed up the detection of fragmentation, reducing the human error involved and will allow it to be implemented for hospital-based remote monitoring and ICD devices.
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Affiliation(s)
- Sidharth Maheshwari
- Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, , Guwahati, India
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36
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Panagiotou C, Dima SM, Mazomenos EB, Rosengarten J, Maharatna K, Gialelis J, Morgan J. Detection of myocardial scar from the VCG using a supervised learning approach. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013:7326-9. [PMID: 24111437 DOI: 10.1109/embc.2013.6611250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.
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Billeci L, Sicca F, Maharatna K, Apicella F, Narzisi A, Campatelli G, Calderoni S, Pioggia G, Muratori F. On the application of quantitative EEG for characterizing autistic brain: a systematic review. Front Hum Neurosci 2013; 7:442. [PMID: 23935579 PMCID: PMC3733024 DOI: 10.3389/fnhum.2013.00442] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Accepted: 07/18/2013] [Indexed: 01/23/2023] Open
Abstract
Autism-Spectrum Disorders (ASD) are thought to be associated with abnormalities in neural connectivity at both the global and local levels. Quantitative electroencephalography (QEEG) is a non-invasive technique that allows a highly precise measurement of brain function and connectivity. This review encompasses the key findings of QEEG application in subjects with ASD, in order to assess the relevance of this approach in characterizing brain function and clustering phenotypes. QEEG studies evaluating both the spontaneous brain activity and brain signals under controlled experimental stimuli were examined. Despite conflicting results, literature analysis suggests that QEEG features are sensitive to modification in neuronal regulation dysfunction which characterize autistic brain. QEEG may therefore help in detecting regions of altered brain function and connectivity abnormalities, in linking behavior with brain activity, and subgrouping affected individuals within the wide heterogeneity of ASD. The use of advanced techniques for the increase of the specificity and of spatial localization could allow finding distinctive patterns of QEEG abnormalities in ASD subjects, paving the way for the development of tailored intervention strategies.
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Affiliation(s)
- Lucia Billeci
- Institute of Clinical Physiology, National Council of Research (CNR) , Pisa , Italy
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38
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Park SJ, Lee SH, Byeon KM, On YK, Huh J, Kim JS, Berezin A, Kremzer A, Kouraki K, Strauss M, Skarlos A, Zeymer U, Zahn R, Kleemann T, Rosengarten JA, Dima SM, Panagiotou C, Scott PA, Chui KHO, Mazomenos EB, Maharatna K, Curzen NP, Morgan J. Oral Abstract Session: Novel non-invasive risk marker. Europace 2013. [DOI: 10.1093/europace/eut166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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39
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Rosengarten JA, Dima SM, Panagiotou C, Scott PA, Chui KHO, Mazomenos EB, Maharatna K, Curzen NP, Morgan JM. 072 NOVEL NON INVASIVE DETECTION OF ARRHYTHMIA SUBSTRATE USING SUPPORT VECTOR MACHINE LEARNING. Heart 2013. [DOI: 10.1136/heartjnl-2013-304019.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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40
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Mazomenos EB, Biswas D, Acharyya A, Chen T, Maharatna K, Rosengarten J, Morgan J, Curzen N. A low-complexity ECG feature extraction algorithm for mobile healthcare applications. IEEE J Biomed Health Inform 2013; 17:459-69. [PMID: 23362250 DOI: 10.1109/titb.2012.2231312] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.
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Acharyya A, Maharatna K, Al-Hashimi BM, Mondal S. Robust channel identification scheme: solving permutation indeterminacy of ICA for artifacts removal from ECG. Annu Int Conf IEEE Eng Med Biol Soc 2010; 2010:1142-5. [PMID: 21096325 DOI: 10.1109/iembs.2010.5627133] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper we propose a novel channel identification scheme for solving permutation indeterminacy introduced by Independent Component Analysis (ICA) for artifacts removal from recorded three channel ECG signals within the remote health monitoring environment. The proposed scheme does not depend on the definition of any specific artifact which is the case with the existing approach and therefore leads to more robust and generic solution to this problem. The proposed scheme has been validated using nine practical case studies and its robustness has been proved by comparison with the existing approach. Simulation results show that the proposed scheme works successfully for all the nine cases whereas the existing approach fails to identify the correct channel for four cases.
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Affiliation(s)
- Amit Acharyya
- Pervasive Systems Centre, School of Electronics and Computer Science, University of Southampton, SO17 1BJ, United Kingdom.
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