Costas Papaloukas

Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis

Cardiac beat classification is a key process in the detection of myocardial ischemic episodes in the electrocardiographic signal. In the present study, we propose a multicriteria sorting method for classifying the cardiac beats as ischemic or not. Through a supervised learning procedure, each beat is compared to preclassified category prototypes under five criteria. These criteria refer to ST segment changes, T wave alterations, and the patients age. The difficulty in applying the above criteria is the determination of the required method parameters, namely the thresholds and weight values. To overcome this problem, we employed a genetic algorithm, which, after proper training, automatically calculates the optimum values for the above parameters. A task-specific cardiac beat database was developed for training and testing the proposed method using data from the European Society of Cardiology ST-T database. Various experimental tests were carried out in order to adjust each module of the classification system. The obtained performance was 91% in terms of both sensitivity and specificity and compares favorably to other beat classification approaches proposed in the literature.

An ischemia detection method based on artificial neural networks

An automated technique was developed for the detection of ischemic episodes in long duration electrocardiographic (ECG) recordings that employs an artificial neural network. In order to train the network for beat classification, a cardiac beat dataset was constructed based on recordings from the European Society of Cardiology (ESC) ST-T database. The network was trained using a Bayesian regularisation method. The raw ECG signal containing the ST segment and the T wave of each beat were the inputs to the beat classification system and the output was the classification of the beat. The input to the network was produced through a principal component analysis (PCA) to achieve dimensionality reduction. The network performance in beat classification was tested on the cardiac beat database providing 90% sensitivity (Se) and 90% specificity (Sp). The neural beat classifier is integrated in a four-stage procedure for ischemic episode detection. The whole system was evaluated on the ESC ST-T database. When aggregate gross statistics was used the Se was 90% and the positive predictive accuracy (PPA) 89%. When aggregate average statistics was used the Se became 86% and the PPA 87%. These results are better than other reported.

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).

An association rule mining-based methodology for automated detection of ischemic ECG beats

Currently, an automated methodology based on association rules is presented for the detection of ischemic beats in long duration electrocardiographic (ECG) recordings. The proposed approach consists of three stages. 1) Preprocessing: Noise is removed and all the necessary ECG features are extracted. 2) Discretization: The continuous valued features are transformed to categorical. 3) Classification: An association rule extraction algorithm is utilized and a rule-based classification model is created. According to the proposed methodology, electrocardiogram (ECG) features extracted from the ST segment and the T-wave, as well as the patients age, were used as inputs. The output was the classification of the beat as ischemic or not. Various algorithms were tested both for discretization and for classification using association rules. To evaluate the methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 93%, respectively. The proposed methodology combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules

A two-stage methodology for sequence classification based on sequential pattern mining and optimization

We present a methodology for sequence classification, which employs sequential pattern mining and optimization, in a two-stage process. In the first stage, a sequence classification model is defined, based on a set of sequential patterns and two sets of weights are introduced, one for the patterns and one for classes. In the second stage, an optimization technique is employed to estimate the weight values and achieve optimal classification accuracy. Extensive evaluation of the methodology is carried out, by varying the number of sequences, the number of patterns and the number of classes and it is compared with similar sequence classification approaches.

Fetal heart rate extraction from composite maternal ECG using complex continuous wavelet transform

Fetal heart rate extraction from the abdominal ECG is of great importance due to the information that carries in assessing appropriately the fetus well-being during pregnancy. In this work a novel automated method is presented for the detection of the QRS complexes of the fetus cardiac activity using multichannel maternal ECG recordings. No accessory preprocessing step for noise filtering is required. The method is based on the complex continuous wavelet transform and modulus maxima theory. The proposed method was validated using real signals, recorded at different weeks of gestation, covering most of the pregnancy period. The system performs well, since almost all fetal beats are detected (accuracy: 99.5%).

Mining sequential patterns for protein fold recognition

Protein data contain discriminative patterns that can be used in many beneficial applications if they are defined correctly. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. Protein classification in terms of fold recognition plays an important role in computational protein analysis, since it can contribute to the determination of the function of a protein whose structure is unknown. Specifically, one of the most efficient SPM algorithms, cSPADE, is employed for the analysis of protein sequence. A classifier uses the extracted sequential patterns to classify proteins in the appropriate fold category. For training and evaluating the proposed method we used the protein sequences from the Protein Data Bank and the annotation of the SCOP database. The method exhibited an overall accuracy of 25% in a classification problem with 36 candidate categories. The classification performance reaches up to 56% when the five most probable protein folds are considered.

Prediction of cis/trans isomerization using feature selection and support vector machines

In protein structures the peptide bond is found to be in trans conformation in the majority of the cases. Only a small fraction of peptide bonds in proteins is reported to be in cis conformation. Most of these instances (>90%) occur when the peptide bond is an imide (X-Pro) rather than an amide bond (X-nonPro). Due to the implication of cis/trans isomerization in many biologically significant processes, the accurate prediction of the peptide bond conformation is of high interest. In this study, we evaluate the effect of a wide range of features, towards the reliable prediction of both proline and non-proline cis/trans isomerization. We use evolutionary profiles, secondary structure information, real-valued solvent accessibility predictions for each amino acid and the physicochemical properties of the surrounding residues. We also explore the predictive impact of a modified feature vector, which consists of condensed position-specific scoring matrices (PSSMX), secondary structure and solvent accessibility. The best discriminating ability is achieved using the first feature vector combined with a wrapper feature selection algorithm and a support vector machine (SVM). The proposed method results in 70% accuracy, 75% sensitivity and 71% positive predictive value (PPV) in the prediction of the peptide bond conformation between any two amino acids. The output of the feature selection stage is investigated in order to identify discriminatory features as well as the contribution of each neighboring residue in the formation of the peptide bond, thus, advancing our knowledge towards cis/trans isomerization.

An optimized sequential pattern matching methodology for sequence classification

In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process. In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated weight assignment to classes using optimization techniques and knowledge discovery in the domain of application.

Bayesian algorithm implementation in a real time exposure assessment model on benzene with calculation of associated cancer risks.

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.