Non-Alcoholic Fatty Pancreas Disease (NAFPD) is the most common pancreatic condition in adults an... more Non-Alcoholic Fatty Pancreas Disease (NAFPD) is the most common pancreatic condition in adults and is usually associated with obesity and insulin resistance. It is a new medical term that indicates the development of pancreatic steatosis, which at an advanced stage leads to the irreversible replacement of acinar cells with fat droplets. Although increasing prevalence rates are recorded worldwide for this condition, it has been studied to a small extent due to the diagnostic limitations of noninvasive medical imaging methods. In recent years and with the development of modern computer vision systems, digital pathology through biopsy imaging systems has become the gold standard in modern clinical trials. The current work presents an automated diagnostic tool for measuring the fat ratio in pancreatic biopsy specimens. The automated analysis is performed on a set of 20 histological images using supervised machine learning algorithms. Its diagnostic performance presents a minimum fat quantification error of 0.23% compared to that obtained from human semi-quantitative estimates.
ECG is one of the most common signals used in medical practice due to its noninvasive nature and ... more ECG is one of the most common signals used in medical practice due to its noninvasive nature and the information it contains. Several systems and various automated approaches have been developed that use computer technology to provide ECG diagnosis. These systems detect abnormalities and other features in the ECG signal and produce a decision which helps the physician when performing diagnosis. ECG decision support systems can serve as a diagnostic tool for specific cardiac anomalies such as myocardial ischaemia and arrhythmia.
The purpose of fetal monitoring during childbirth is the early recognition of any pathological co... more The purpose of fetal monitoring during childbirth is the early recognition of any pathological conditions to guide a clinician in early intervention to avoid any complication in the health of the fetus. Non-Invasive Fetal Electrocardiography (NIFECG) represents an alternative fetal monitoring technique. The fetal ECG (fECG) derived from maternal thoracic and abdominal ECG recordings, provides an alternative to typical embryo monitoring means. In addition, it allows for long-term and ambulatory registrations that broaden the diagnostic capabilities for assessing the fetal health. However, in real situations, clear fECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander and high-frequency interference. In this paper, a novel integrated adaptive methodology based on the combination of blind source separation, empirical mode decomposition, wavelet shrinkage denoising and correlation analysis, for the non-invasive extraction and processing of the FECG, is proposed. The methodology has been evaluated using both real and simulated recordings, and the obtained results indicate it efficiently.
A new approach for the assessment of motor symptoms caused by Parkinson's disease (PD), based... more A new approach for the assessment of motor symptoms caused by Parkinson's disease (PD), based on data recorded using a smart mobile phone, is presented in this manuscript. Data were obtained from the online platform kaggle.com, and were analyzed based on machine learning techniques to produce a comprehensive physician report, presenting motor symptoms in everyday life. The idea of this study is to equip PD patients with a smartphone, which will monitor signals in real time for a period of time in order to enhance the medical diagnosis routine of a physician, providing information about the overall picture of the patient's motor condition, resulting in the provision of individualized treatment to the patient.
Touch is a fundamental aspect of human interaction with the surrounding environment. It affects i... more Touch is a fundamental aspect of human interaction with the surrounding environment. It affects individuals' development in different manners and figures prominently in everyday operations such as the sense of presence, object recognition, performing actions, non-verbal communication and emotional state. In recent years there has been a growth of interest in researching the electro physiological activity of the brain originating from haptic stimulation. In the present preliminary experiment, we performed a classification process of extracted EEG features acquired from four healthy participants' EEG data when they actively touched different natural textures. Each participant was asked to use their fingertips and calmly rub for one minute, each of the three different textured materials (smooth, rough and water surface). EEG recordings were acquired and processed. Next, time and frequency-based features were extracted and used as inputto four classifiers to correctly identify each different texture. The results obtained show a classification performance of 63% with C4.5 algorithm and 76% with Random Forests and 10-fold cross-validation.
In this work, an innovative classification algorithmic technique through sequential pattern minin... more In this work, an innovative classification algorithmic technique through sequential pattern mining was developed to predict the secondary structure of proteins. A basic algorithm was selected for the extraction of the sequential patterns and another algorithm was developed which employs these patterns for protein structure prediction. In the matter of predicting protein structures and scoring sequential patterns, several methodologies has been implemented that theoretically and experimentally overcome the disadvantages of existing algorithms.
In this manuscript, a methodology for analysing motor signals from Parkinson’s disease (PD) patie... more In this manuscript, a methodology for analysing motor signals from Parkinson’s disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson’s Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.
Non-Alcoholic Fatty Liver Disease (NAFLD) is a frequent syndrome that exclusively refers to fat a... more Non-Alcoholic Fatty Liver Disease (NAFLD) is a frequent syndrome that exclusively refers to fat accumulation in liver and steatohepatitis1. It is considered as a massive disease ranging from 20% to 40% in adult populations of the Western World. Its prevalence is related to insulin resistance, which places individuals at high rates of mortality. An increased fat accumulation rate, can significantly increase the development of liver steatosis, which in later stages may progress into fibrosis and cirrhosis. In recent years, research groups focus on the automated fat detection based on histology and digital image processing. The current project, extends our previous work for the detection and quantification of fatty liver, by characterizing histological findings. It is an extensive study of supervised learning of fat droplet features, in order to exclude other findings from fat ratio computation. The method is evaluated on a set of 13 liver biopsy images, performing 92% accuracy.
Objective characterization of pain intensity is necessary under certain clinical conditions. The ... more Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band's energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.
In this paper, a novel stochastic approach for the induction of the decision trees in a tree-stru... more In this paper, a novel stochastic approach for the induction of the decision trees in a tree-structured ensemble classifier is presented. The proposed algorithm is based on a stochastic process to induct each decision tree, assigning a probability for the selection of the split attribute in every tree node, designed in order to create strong and independent trees. A selection of 33 well-known classification datasets have been employed for the evaluation of the proposed algorithm, obtaining high classification results, in terms of Classification Accuracy, Average Sensitivity and Average Precision. Furthermore, a comparative study with Random Forest, Random Subspace and C4.5 is performed. The obtained results indicate the importance of the proposed algorithm, since it achieved the highest overall results in all metrics.
Non-Alcoholic Fatty Pancreas Disease (NAFPD) is the most common pancreatic condition in adults an... more Non-Alcoholic Fatty Pancreas Disease (NAFPD) is the most common pancreatic condition in adults and is usually associated with obesity and insulin resistance. It is a new medical term that indicates the development of pancreatic steatosis, which at an advanced stage leads to the irreversible replacement of acinar cells with fat droplets. Although increasing prevalence rates are recorded worldwide for this condition, it has been studied to a small extent due to the diagnostic limitations of noninvasive medical imaging methods. In recent years and with the development of modern computer vision systems, digital pathology through biopsy imaging systems has become the gold standard in modern clinical trials. The current work presents an automated diagnostic tool for measuring the fat ratio in pancreatic biopsy specimens. The automated analysis is performed on a set of 20 histological images using supervised machine learning algorithms. Its diagnostic performance presents a minimum fat quantification error of 0.23% compared to that obtained from human semi-quantitative estimates.
ECG is one of the most common signals used in medical practice due to its noninvasive nature and ... more ECG is one of the most common signals used in medical practice due to its noninvasive nature and the information it contains. Several systems and various automated approaches have been developed that use computer technology to provide ECG diagnosis. These systems detect abnormalities and other features in the ECG signal and produce a decision which helps the physician when performing diagnosis. ECG decision support systems can serve as a diagnostic tool for specific cardiac anomalies such as myocardial ischaemia and arrhythmia.
The purpose of fetal monitoring during childbirth is the early recognition of any pathological co... more The purpose of fetal monitoring during childbirth is the early recognition of any pathological conditions to guide a clinician in early intervention to avoid any complication in the health of the fetus. Non-Invasive Fetal Electrocardiography (NIFECG) represents an alternative fetal monitoring technique. The fetal ECG (fECG) derived from maternal thoracic and abdominal ECG recordings, provides an alternative to typical embryo monitoring means. In addition, it allows for long-term and ambulatory registrations that broaden the diagnostic capabilities for assessing the fetal health. However, in real situations, clear fECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander and high-frequency interference. In this paper, a novel integrated adaptive methodology based on the combination of blind source separation, empirical mode decomposition, wavelet shrinkage denoising and correlation analysis, for the non-invasive extraction and processing of the FECG, is proposed. The methodology has been evaluated using both real and simulated recordings, and the obtained results indicate it efficiently.
A new approach for the assessment of motor symptoms caused by Parkinson's disease (PD), based... more A new approach for the assessment of motor symptoms caused by Parkinson's disease (PD), based on data recorded using a smart mobile phone, is presented in this manuscript. Data were obtained from the online platform kaggle.com, and were analyzed based on machine learning techniques to produce a comprehensive physician report, presenting motor symptoms in everyday life. The idea of this study is to equip PD patients with a smartphone, which will monitor signals in real time for a period of time in order to enhance the medical diagnosis routine of a physician, providing information about the overall picture of the patient's motor condition, resulting in the provision of individualized treatment to the patient.
Touch is a fundamental aspect of human interaction with the surrounding environment. It affects i... more Touch is a fundamental aspect of human interaction with the surrounding environment. It affects individuals' development in different manners and figures prominently in everyday operations such as the sense of presence, object recognition, performing actions, non-verbal communication and emotional state. In recent years there has been a growth of interest in researching the electro physiological activity of the brain originating from haptic stimulation. In the present preliminary experiment, we performed a classification process of extracted EEG features acquired from four healthy participants' EEG data when they actively touched different natural textures. Each participant was asked to use their fingertips and calmly rub for one minute, each of the three different textured materials (smooth, rough and water surface). EEG recordings were acquired and processed. Next, time and frequency-based features were extracted and used as inputto four classifiers to correctly identify each different texture. The results obtained show a classification performance of 63% with C4.5 algorithm and 76% with Random Forests and 10-fold cross-validation.
In this work, an innovative classification algorithmic technique through sequential pattern minin... more In this work, an innovative classification algorithmic technique through sequential pattern mining was developed to predict the secondary structure of proteins. A basic algorithm was selected for the extraction of the sequential patterns and another algorithm was developed which employs these patterns for protein structure prediction. In the matter of predicting protein structures and scoring sequential patterns, several methodologies has been implemented that theoretically and experimentally overcome the disadvantages of existing algorithms.
In this manuscript, a methodology for analysing motor signals from Parkinson’s disease (PD) patie... more In this manuscript, a methodology for analysing motor signals from Parkinson’s disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson’s Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.
Non-Alcoholic Fatty Liver Disease (NAFLD) is a frequent syndrome that exclusively refers to fat a... more Non-Alcoholic Fatty Liver Disease (NAFLD) is a frequent syndrome that exclusively refers to fat accumulation in liver and steatohepatitis1. It is considered as a massive disease ranging from 20% to 40% in adult populations of the Western World. Its prevalence is related to insulin resistance, which places individuals at high rates of mortality. An increased fat accumulation rate, can significantly increase the development of liver steatosis, which in later stages may progress into fibrosis and cirrhosis. In recent years, research groups focus on the automated fat detection based on histology and digital image processing. The current project, extends our previous work for the detection and quantification of fatty liver, by characterizing histological findings. It is an extensive study of supervised learning of fat droplet features, in order to exclude other findings from fat ratio computation. The method is evaluated on a set of 13 liver biopsy images, performing 92% accuracy.
Objective characterization of pain intensity is necessary under certain clinical conditions. The ... more Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band's energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.
In this paper, a novel stochastic approach for the induction of the decision trees in a tree-stru... more In this paper, a novel stochastic approach for the induction of the decision trees in a tree-structured ensemble classifier is presented. The proposed algorithm is based on a stochastic process to induct each decision tree, assigning a probability for the selection of the split attribute in every tree node, designed in order to create strong and independent trees. A selection of 33 well-known classification datasets have been employed for the evaluation of the proposed algorithm, obtaining high classification results, in terms of Classification Accuracy, Average Sensitivity and Average Precision. Furthermore, a comparative study with Random Forest, Random Subspace and C4.5 is performed. The obtained results indicate the importance of the proposed algorithm, since it achieved the highest overall results in all metrics.
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Papers by Markos Tsipouras