18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05), 2005
An automated methodology which detects transient events in EEG recordings and classifies those as... more An automated methodology which detects transient events in EEG recordings and classifies those as epileptic spikes, muscle activity, eye blinking activity and sharp alpha activity is presented. It is based on data mining algorithms and includes four stages: (I) EEG preprocessing and transient events detection, (II) clustering of transient events and feature extraction, (III) feature discretization and (IV) association rule
Chronic hind-limb ischemia confers cytoprotection after coronary occlusion, but it is unclear whe... more Chronic hind-limb ischemia confers cytoprotection after coronary occlusion, but it is unclear whether it ameliorates substrate formation for ventricular tachyarrhythmias (VTs). Chronic hind-limb ischemia was generated by femoral artery excision in 50 rats, while 25 animals were sham-operated. Left coronary artery ligation was performed after 3 weeks and infarct size was measured 24 hours thereafter. The inducibility of VTs was assessed by programmed electrical stimulation (PES) 4 weeks post-ligation. A score was assigned, based on protocol stage and tachyarrhythmia duration. Monophasic action potentials (MAP) were recorded prior to and 4 weeks after ligation. The infarct size was smaller (p=0.000079) in the ischemic rats (25.7±2.1%) than in the controls (41.7±2.2%), accompanied by a lower (p=0.029) arrhythmia score (1.05±0.38 versus 2.70±0.68, respectively). The action potential duration (APD) was shorter (p<0.05) in the ischemic rats prior to ligation and remained stable after 4...
In the current work, a system for the monitoring, assessment and management of patients with chro... more In the current work, a system for the monitoring, assessment and management of patients with chronic movement disorders such as Parkinson's disease (PD) is presented. The so called PERFORM system consists of the patient and the healthcare center subsystem. PERFORM monitors patient's motion status in daily activities, using a set of light wearable sensors. Based on the analysis of the
An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this pa... more An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).
In this work a decision support tool for optimal administration of Levodopa (L-Dopa) in patients ... more In this work a decision support tool for optimal administration of Levodopa (L-Dopa) in patients with Parkinson&amp;amp;amp;amp;amp;amp;amp;#x27;s Disease (PD) is presented. The so called Medication Change Proposer (MCP) is part of the PERFORM system, which monitors patient&amp;amp;amp;amp;amp;amp;amp;#x27;s motion status using a set of wearable sensors, assesses PD symptoms and their severity through dedicated symptoms detection modules (On-Off detection and Levodopa-Induced
The platform of SensorART emphases on the controlling and remote treatment of patients suffering ... more The platform of SensorART emphases on the controlling and remote treatment of patients suffering from failure of the heart, using an implantable Ventricular Assist Device (VAD). It provides an extendable, interoperable and independent from VAD solution, which includes different software and hardware components in a holistic approach, in order to improve the quality the treatment of the patients and the
ABSTRACT In this paper, we introduce a new suction detection approach based on online learning of... more ABSTRACT In this paper, we introduce a new suction detection approach based on online learning of a Gaussian Mixture Model (GMM) with constrained parameters to model the reduction in pump flow signals baseline during suction events. A novel three-step methodology is employed: i) signal windowing, ii) GMM based classification and iii) GMM parameter adaptation. More specifically, the first 5 second segment is used for the parameter initialization and the consequent 1 second windows are classified and used for model adaptation. The proposed approach has been tested in simulation (pump flow) signals and satisfactory results have been obtained.
ABSTRACT The detection of epileptic seizures is of primary interest for the diagnosis of patients... more ABSTRACT The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmicity discharge for either a focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of them highly recommended especially in long term EEG recordings. The present work proposes an automated method to detect the epileptic seizures by using an unsupervised method based on k-means clustering end Ensemble Empirical Decomposition (EEMD). EEG segments are obtained from a publicly available dataset and are classified in two categories “seizure” and “non-seizure”. Using EEMD the Marginal Spectrum (MS) of each one of the EEG segments is calculated. The MS is then divided into equal intervals and the averages of these intervals are used as input features for k-Means clustering. The evaluation results are very promising indicating overall accuracy 98% and is comparable with other related studies. An advantage of this method that no training data are used due to the unsupervised nature of k-Means clustering.
ABSTRACT Electrooculographic (EOG) artefact is one of the most common contaminations of Electroen... more ABSTRACT Electrooculographic (EOG) artefact is one of the most common contaminations of Electroencephalographic (EEG) recordings. The corruption of EEG characteristics from Blinking Artefacts (BAs) affects the results of EEG signal processing methods and also impairs the visual analysis of EEGs. In this paper, our scope was a comparative analysis of the performance of three standard denoising methods like continuous Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT) and Kalman Filter (KF). In order to evaluate the performance of EMD, DWT and KF of noise reduction and to express the quality of the denoised EEG, we calculate several indexes such as the Signal-to-Noise Ratio (SNR). All the results obtained from noise simulated EEG data show that WT achieved the greatest SNR difference and also the mode mixing issue of EMD affected this method&#39;s performance.
This work presents the Treatment Tool, which is a component of the Specialist&amp;amp;amp;amp... more This work presents the Treatment Tool, which is a component of the Specialist&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s Decision Support Framework (SDSS) of the SensorART platform. The SensorART platform focuses on the management of heart failure (HF) patients, which are treated with implantable, left ventricular assist devices (LVADs). SDSS supports the specialists on various decisions regarding patients with LVADs including decisions on the best treatment strategy, suggestion of the most appropriate candidates for LVAD weaning, configuration of the pump speed settings, while also provides data analysis tools for new knowledge extraction. The Treatment Tool is a web-based component and its functionality includes the calculation of several acknowledged risk scores along with the adverse events appearance prediction for treatment assessment.
The antiarrhythmic potential of postconditioning in in vivo models remains poorly defined. We com... more The antiarrhythmic potential of postconditioning in in vivo models remains poorly defined. We compared the effects of pre- and postconditioning on ventricular arrhythmogenesis against controls with and without reperfusion. Wistar rats (n = 40, 269 ± 3 g) subjected to ischemia (30 minutes)--reperfusion (24 hours) were assigned to the following groups: (1) preconditioning (2 cycles), (2) postconditioning (6 cycles), or (3) no intervention and were compared with (4) nonreperfused infarcts and (5) sham-operated animals. Infarct size was measured, and arrhythmogenesis was evaluated with continuous telemetric electrocardiographic recording, heart rate variability indices, and monophasic action potentials (MAPs). During a 24-hour observation period, no differences in mortality were observed. Reperfusion decreased infarct size and ameliorated sympathetic activation during the late reperfusion phase. Preconditioning decreased infarct size by a further 35% (P = .0017), but only a marginal decrease (by 18%, P = .075) was noted after postconditioning. Preconditioning decreased arrhythmias during ischemia and early reperfusion, whereas postconditioning almost abolished them during the entire reperfusion period. No differences were noted in MAPs or in the magnitude of sympathetic activation between the 2 interventions. Compared to postconditioning, preconditioning affords more powerful cytoprotection, but both interventions exert antiarrhythmic actions. In the latter, these are mainly evident during the ischemic phase and continue during early reperfusion. Postconditioning markedly decreases reperfusion arrhythmias during a prolonged observation period. The mechanisms underlying the antiarrhythmic effects of pre- and postconditioning are likely different but remain elusive.
18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05), 2005
An automated methodology which detects transient events in EEG recordings and classifies those as... more An automated methodology which detects transient events in EEG recordings and classifies those as epileptic spikes, muscle activity, eye blinking activity and sharp alpha activity is presented. It is based on data mining algorithms and includes four stages: (I) EEG preprocessing and transient events detection, (II) clustering of transient events and feature extraction, (III) feature discretization and (IV) association rule
Chronic hind-limb ischemia confers cytoprotection after coronary occlusion, but it is unclear whe... more Chronic hind-limb ischemia confers cytoprotection after coronary occlusion, but it is unclear whether it ameliorates substrate formation for ventricular tachyarrhythmias (VTs). Chronic hind-limb ischemia was generated by femoral artery excision in 50 rats, while 25 animals were sham-operated. Left coronary artery ligation was performed after 3 weeks and infarct size was measured 24 hours thereafter. The inducibility of VTs was assessed by programmed electrical stimulation (PES) 4 weeks post-ligation. A score was assigned, based on protocol stage and tachyarrhythmia duration. Monophasic action potentials (MAP) were recorded prior to and 4 weeks after ligation. The infarct size was smaller (p=0.000079) in the ischemic rats (25.7±2.1%) than in the controls (41.7±2.2%), accompanied by a lower (p=0.029) arrhythmia score (1.05±0.38 versus 2.70±0.68, respectively). The action potential duration (APD) was shorter (p<0.05) in the ischemic rats prior to ligation and remained stable after 4...
In the current work, a system for the monitoring, assessment and management of patients with chro... more In the current work, a system for the monitoring, assessment and management of patients with chronic movement disorders such as Parkinson's disease (PD) is presented. The so called PERFORM system consists of the patient and the healthcare center subsystem. PERFORM monitors patient's motion status in daily activities, using a set of light wearable sensors. Based on the analysis of the
An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this pa... more An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).
In this work a decision support tool for optimal administration of Levodopa (L-Dopa) in patients ... more In this work a decision support tool for optimal administration of Levodopa (L-Dopa) in patients with Parkinson&amp;amp;amp;amp;amp;amp;amp;#x27;s Disease (PD) is presented. The so called Medication Change Proposer (MCP) is part of the PERFORM system, which monitors patient&amp;amp;amp;amp;amp;amp;amp;#x27;s motion status using a set of wearable sensors, assesses PD symptoms and their severity through dedicated symptoms detection modules (On-Off detection and Levodopa-Induced
The platform of SensorART emphases on the controlling and remote treatment of patients suffering ... more The platform of SensorART emphases on the controlling and remote treatment of patients suffering from failure of the heart, using an implantable Ventricular Assist Device (VAD). It provides an extendable, interoperable and independent from VAD solution, which includes different software and hardware components in a holistic approach, in order to improve the quality the treatment of the patients and the
ABSTRACT In this paper, we introduce a new suction detection approach based on online learning of... more ABSTRACT In this paper, we introduce a new suction detection approach based on online learning of a Gaussian Mixture Model (GMM) with constrained parameters to model the reduction in pump flow signals baseline during suction events. A novel three-step methodology is employed: i) signal windowing, ii) GMM based classification and iii) GMM parameter adaptation. More specifically, the first 5 second segment is used for the parameter initialization and the consequent 1 second windows are classified and used for model adaptation. The proposed approach has been tested in simulation (pump flow) signals and satisfactory results have been obtained.
ABSTRACT The detection of epileptic seizures is of primary interest for the diagnosis of patients... more ABSTRACT The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmicity discharge for either a focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of them highly recommended especially in long term EEG recordings. The present work proposes an automated method to detect the epileptic seizures by using an unsupervised method based on k-means clustering end Ensemble Empirical Decomposition (EEMD). EEG segments are obtained from a publicly available dataset and are classified in two categories “seizure” and “non-seizure”. Using EEMD the Marginal Spectrum (MS) of each one of the EEG segments is calculated. The MS is then divided into equal intervals and the averages of these intervals are used as input features for k-Means clustering. The evaluation results are very promising indicating overall accuracy 98% and is comparable with other related studies. An advantage of this method that no training data are used due to the unsupervised nature of k-Means clustering.
ABSTRACT Electrooculographic (EOG) artefact is one of the most common contaminations of Electroen... more ABSTRACT Electrooculographic (EOG) artefact is one of the most common contaminations of Electroencephalographic (EEG) recordings. The corruption of EEG characteristics from Blinking Artefacts (BAs) affects the results of EEG signal processing methods and also impairs the visual analysis of EEGs. In this paper, our scope was a comparative analysis of the performance of three standard denoising methods like continuous Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT) and Kalman Filter (KF). In order to evaluate the performance of EMD, DWT and KF of noise reduction and to express the quality of the denoised EEG, we calculate several indexes such as the Signal-to-Noise Ratio (SNR). All the results obtained from noise simulated EEG data show that WT achieved the greatest SNR difference and also the mode mixing issue of EMD affected this method&#39;s performance.
This work presents the Treatment Tool, which is a component of the Specialist&amp;amp;amp;amp... more This work presents the Treatment Tool, which is a component of the Specialist&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s Decision Support Framework (SDSS) of the SensorART platform. The SensorART platform focuses on the management of heart failure (HF) patients, which are treated with implantable, left ventricular assist devices (LVADs). SDSS supports the specialists on various decisions regarding patients with LVADs including decisions on the best treatment strategy, suggestion of the most appropriate candidates for LVAD weaning, configuration of the pump speed settings, while also provides data analysis tools for new knowledge extraction. The Treatment Tool is a web-based component and its functionality includes the calculation of several acknowledged risk scores along with the adverse events appearance prediction for treatment assessment.
The antiarrhythmic potential of postconditioning in in vivo models remains poorly defined. We com... more The antiarrhythmic potential of postconditioning in in vivo models remains poorly defined. We compared the effects of pre- and postconditioning on ventricular arrhythmogenesis against controls with and without reperfusion. Wistar rats (n = 40, 269 ± 3 g) subjected to ischemia (30 minutes)--reperfusion (24 hours) were assigned to the following groups: (1) preconditioning (2 cycles), (2) postconditioning (6 cycles), or (3) no intervention and were compared with (4) nonreperfused infarcts and (5) sham-operated animals. Infarct size was measured, and arrhythmogenesis was evaluated with continuous telemetric electrocardiographic recording, heart rate variability indices, and monophasic action potentials (MAPs). During a 24-hour observation period, no differences in mortality were observed. Reperfusion decreased infarct size and ameliorated sympathetic activation during the late reperfusion phase. Preconditioning decreased infarct size by a further 35% (P = .0017), but only a marginal decrease (by 18%, P = .075) was noted after postconditioning. Preconditioning decreased arrhythmias during ischemia and early reperfusion, whereas postconditioning almost abolished them during the entire reperfusion period. No differences were noted in MAPs or in the magnitude of sympathetic activation between the 2 interventions. Compared to postconditioning, preconditioning affords more powerful cytoprotection, but both interventions exert antiarrhythmic actions. In the latter, these are mainly evident during the ischemic phase and continue during early reperfusion. Postconditioning markedly decreases reperfusion arrhythmias during a prolonged observation period. The mechanisms underlying the antiarrhythmic effects of pre- and postconditioning are likely different but remain elusive.
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Papers by Alexandros Tzallas