Biomedical subjects

D I Fotiadis

Publications and source records attributed to D I Fotiadis.

13 recordsLinked to original sources

PubMed2006

A Kalman filter based methodology for EEG spike enhancement.

In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.

Electroencephalography↗

PubMed2006

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

OBJECTIVES: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. METHODS: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. RESULTS: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. CONCLUSIONS: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Action Potentials↗

PubMed2005

A novel method for automated EMG decomposition and MUAP classification.

OBJECTIVE: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. RESULTS: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. CONCLUSION: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals.

Action Potentials↗

PubMed2005

Phase response characteristics of sinoatrial node cells.

In this work, the dynamic response of the sinoatrial node (SAN), the natural pacemaker of the heart, to short external stimuli is investigated using the Zhang et al. model. The model equations are solved twice for the central cell and for the peripheral cell. A short current pulse is applied to reset the spontaneous rhythmic activity of the single sinoatrial node cell. Depending on the stimulus timing either a delay or an advance in the occurrence of next action potential is produced. This resetting behavior is quantified in terms of phase transition curves (PTCs) for short electrical current pulses of varying amplitude which span the whole period. For low stimulus amplitudes the transition from advance to delay is smooth, while at higher amplitudes abrupt changes and discontinuities are observed in PTCs. Such discontinuities reveal critical stimuli, the application of which can result in annihilation of activity in central SAN cells. The detailed analysis of the ionic mechanisms involved in its resetting behavior of sinoatrial node cell models provides new insight into the dynamics and physiology of excitation of the sinoatrial node of the heart.

Action Potentials↗

PubMed2005

An arrhythmia classification system based on the RR-interval signal.

OBJECTIVE: This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings. METHODOLOGY: A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of beats: normal, premature ventricular contractions, ventricular flutter/fibrillation and 2 degrees heart block. The beat classification is used as input of a knowledge-based deterministic automaton to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricular trigeminy, ventricular couplet, ventricular tachycardia, ventricular flutter/fibrillation and 2 degrees heart block. RESULTS: The method is evaluated by using the MIT-BIH arrhythmia database. The achieved scores indicate high performance: 98% accuracy for arrhythmic beat classification and 94% accuracy for arrhythmic episode detection and classification. CONCLUSION: The proposed method is advantageous because it uses only the RR-interval signal for arrhythmia beat and episode classification and the results compare well with more complex methods.

Algorithms↗

PubMed2005

In vivo validation of a novel semi-automated method for border detection in intravascular ultrasound images.

The aim of this work was to evaluate a new semi-automated intravascular ultrasound (IVUS) border detection method. The method was used to identify the lumen and the external elastic membrane or the borders of stents in 80 IVUS images, randomly selected from 10 consecutive human coronary arteries. These semi-automated results were compared with observations of two experts. Several indices in each case were obtained in order fully to evaluate the method. The time required for identification of the borders was also recorded. The interobserver variability of the method ranged from 1.21% to 5.61%, the correlation coefficient from 0.98 to 0.99, the slope was close to unity (0.94-1.03), the y intercept close to zero and the Williams index value was close to unity (range 0.67-0.91). The time (mean+/-SD) required for the method to identify the borders of the different vessel layers for the whole IVUS sequence was 5.2+/-0.2 min. The results demonstrate that the method is reliable and capable of identifying rapidly and accurately the different vessel layers depicted in IVUS images.

Aged↗

PubMed2004

Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines.

OBJECTIVE: Detection and characterization of microcalcification clusters in mammograms is vital in daily clinical practice. The scope of this work is to present a novel computer-based automated method for the characterization of microcalcification clusters in digitized mammograms. METHODS AND MATERIAL: The proposed method has been implemented in three stages: (a) the cluster detection stage to identify clusters of microcalcifications, (b) the feature extraction stage to compute the important features of each cluster and (c) the classification stage, which provides with the final characterization. In the classification stage, a rule-based system, an artificial neural network (ANN) and a support vector machine (SVM) have been implemented and evaluated using receiver operating characteristic (ROC) analysis. The proposed method was evaluated using the Nijmegen and Mammographic Image Analysis Society (MIAS) mammographic databases. The original feature set was enhanced by the addition of four rule-based features. RESULTS AND CONCLUSIONS: In the case of Nijmegen dataset, the performance of the SVM was Az=0.79 and 0.77 for the original and enhanced feature set, respectively, while for the MIAS dataset the corresponding characterization scores were Az=0.81 and 0.80. Utilizing neural network classification methodology, the corresponding performance for the Nijmegen dataset was Az=0.70 and 0.76 while for the MIAS dataset it was Az=0.73 and 0.78. Although the obtained high classification performance can be successfully applied to microcalcification clusters characterization, further studies must be carried out for the clinical evaluation of the system using larger datasets. The use of additional features originating either from the image itself (such as cluster location and orientation) or from the patient data may further improve the diagnostic value of the system.

Breast Diseases↗

PubMed2004

Anharmonic analysis of arterial blood pressure and flow pulses.

Fourier analysis is usually employed for the computation of blood flow in arteries. Although the orthogonality of Fourier eigenfunctions guarantees the accurate mathematical modeling of the blood pressure and flow waveforms, the physics behind this objective function is frequently missing. We propose a new method to account for the blood pressure and flow, single-cycle (systole-diastole) waveforms. It is based on the one dimensional hydrodynamic mass and momentum conservation equations for viscous flow. The similarity of the linear problem, under discussion, with related transmission line theory in electromagnetic wave propagation, permits expansion in anharmonic, non-separable eigenfunctions. In some cases one term in the expansion is adequate to fit the main peak of the observed waveforms. Analytical formulas are derived for the dependence of the pressure and flow main peaks on whole blood viscosity and distance from the heart, which interpret observations related to hypertension.

Animals↗

PubMed2004

A sequential method for discovering probabilistic motifs in proteins.

OBJECTIVES: This paper proposes a greedy algorithm for learning a mixture of motifs model through likelihood maximization, in order to discover common substrings, known as motifs, from a given collection of related biosequences. METHODS: The approach sequentially adds a new motif component to a mixture model by performing a combined scheme of global and local search for appropriately initializing the component parameters. A hierarchical clustering scheme is also applied initially which leads to the identification of candidate motif models and speeds up the global searching procedure. RESULTS: The performance of the proposed algorithm has been studied in both artificial and real biological datasets. In comparison with the well-known MEME approach, the algorithm is advantageous since it identifies motifs with significant conservation and produces larger protein fingerprints. CONCLUSION: The proposed greedy algorithm constitutes a promising approach for discovering multiple probabilistic motifs in biological sequences. By using an effective incremental mixture modeling strategy, our technique manages to successfully overcome the limitation of the MEME scheme which erases motif occurrences each time a new motif is discovered.

Algorithms↗

PubMed2002

Hydrodynamics of magnetic drug targeting.

Among the proposed techniques for delivering drugs to specific locations within the human body, magnetic drug targeting surpasses due to its non-invasive character and its high targeting efficiency. Although the method has been proposed almost 30 years ago, the technical problems obstruct possible applications. It is the aim of the present work to classify the emerging problems and propose satisfactory answers. A general phenomenological theory is developed and a model case is studied, which incorporates all the physical parameters of the problem.

Biocompatible Materials↗

PubMed2002

An automatic microcalcification detection system based on a hybrid neural network classifier.

A hybrid intelligent system is presented for the identification of microcalcification clusters in digital mammograms. The proposed method is based on a three-step procedure: (a) preprocessing and segmentation, (b) regions of interest (ROI) specification, and (c) feature extraction and classification. The reduction of false positive cases is performed using an intelligent system containing two sub-systems: a rule-based and a neural network sub-system. In the first step of the classification schema 22 features are automatically computed which refer either to individual microcalcifications or to groups of them. Further reduction in the number of features is achieved through principal component analysis (PCA). The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve (A(z)). In particular, the A(z) value for the Nijmegen dataset is 0.91 and for the MIAS is 0.92. The detection specificity of the two sets is 1.80 and 1.15 false positive clusters per image, at the sensitivity level higher than 0.90, respectively.

Automation↗

PubMed2001

An MRI-based semiautomated volumetric quantification of hip osteonecrosis.

OBJECTIVE: To objectively and precisely define the spatial distribution of osteonecrosis and to investigate the influence of various factors including etiology. DESIGN: A volumetric method is presented to describe the size and spatial distribution of necrotic lesions of the femoral head, using MRI scans. The technique is based on the definition of an equivalent sphere model for the femoral head. PATIENTS: The gender, age, number of hips involved, disease duration, pain intensity, limping disability and etiology were correlated with the distribution of the pathologic bone. Seventy-nine patients with 122 hips affected by osteonecrosis were evaluated. RESULTS: The lesion size ranged from 7% to 73% of the sphere equivalent. The lateral octants presented considerable variability, ranging from wide lateral lesions extending beyond the lip of the acetabulum, to narrow medial lesions, leaving a lateral supporting pillar of intact bone. Patients with sickle cell disease and steroid administration presented the largest lesions. The extent of the posterior superior medial octant involvement correlated with the symptom intensity, a younger age and male gender. CONCLUSION: The methodology presented here has proven a reliable and straightforward imaging tool for precise assessment of necrotic lesions. It also enables us to target accurately the drilling and grafting procedures.

Adolescent↗

PubMed2001

A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms.

A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics).

Electrocardiography↗