Biomedical subjects

Dimitrios I Fotiadis

Publications and source records attributed to Dimitrios I Fotiadis.

21 records · Page 2Linked to original sources

PubMed2003

Greedy mixture learning for multiple motif discovery in biological sequences.

MOTIVATION: This paper studies the problem of discovering subsequences, known as motifs, that are common to a given collection of related biosequences, by proposing a greedy algorithm for learning a mixture of motifs model through likelihood maximization. The approach adds sequentially a new motif to a mixture model by performing a combined scheme of global and local search for appropriately initializing its parameters. In addition, a hierarchical partitioning scheme based on kd-trees is presented for partitioning the input dataset in order to speed-up the global searching procedure. The proposed method compares favorably over the well-known MEME approach and treats successfully several drawbacks of MEME. RESULTS: Experimental results indicate that the algorithm is advantageous in identifying larger groups of motifs characteristic of biological families with significant conservation. In addition, it offers better diagnostic capabilities by building more powerful statistical motif-models with improved classification accuracy.

Algorithms↗

PubMed2002

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.

Automation↗

PubMed2002

Use of a novel rule-based expert system in the detection of changes in the ST segment and the T wave in long duration ECGs.

The development of a new fast and robust computerised system is examined in detecting electrocardiogram (ECG) changes in long duration ECG recordings. The system distinguishes these changes between ST-segment deviation and T-wave alterations and can support the produced diagnosis by providing explanations for the decisions made. The European Society of Cardiology ST-T Database was used for evaluating the performance of the system. Sensitivity and positive predictive accuracy were the performance measures used and the proposed system scored 92.02% and 93.77%, respectively, in detecting ST-segment episodes and 91.09% and 80.09% in detecting T-wave episodes. By using the chi-square test we also compared the performance of the system between ECG recordings with minimal and substantial amount of noise. The sensitivity of the proposed system is higher than of other algorithms reported in the literature and the positive predictive accuracy is comparable to, or better than, most of them.

Algorithms↗