Compression of ECG signals using variable-length classified vector sets and wavelet transforms

In this article, an improved and more efficient algorithm for the compression of the electrocardiogram (ECG) signals is presented, which combines the processes of modeling ECG signal by variable-length classified signature and envelope vector sets (VL-CSEVS), and residual error coding via wavelet transform. In particular, we form the VL-CSEVS derived from the ECG signals, which exploits the relationship between energy variation and clinical information. The VL-CSEVS are unique patterns generated from many of thousands of ECG segments of two different lengths obtained by the energy based segmentation method, then they are presented to both the transmitter and the receiver used in our proposed compression system. The proposed algorithm is tested on the MIT-BIH Arrhythmia Database and MIT-BIH Compression Test Database and its performance is evaluated by using some evaluation metrics such as the percentage root-mean-square difference (PRD), modified PRD (MPRD), maximum error, and clinical evaluation. Our experimental results imply that our proposed algorithm achieves high compression ratios with low level reconstruction error while preserving the diagnostic information in the reconstructed ECG signal, which has been supported by the clinical tests that we have carried out.ISIK University [06B302]The author would like to special thank Prof. Siddik Yarman who is Board of Trustees Chairman of the ISIK University and Umit Guz, Assistant Professor at the ISIK University for their valuable contributions and continuous interest in this article. The author also would like to thank Prof. Osman Akdemir who is a cardiologist in the Department of Cardiology at the T. C. Maltepe University and Dr. Ruken Bengi Bakal who is a cardiologist in the Department of Cardiology at the Kartal Kosuyolu Yuksek Ihtisas Education and Research Hospital for their valuable clinical contributions and suggestions and the reviewers for their constructive comments which improved the technical quality and presentation of the article. The present work was supported by the Scientific Research Fund of ISIK University, Project number 06B302.Publisher's Versio

Results of mitochondrial DNA sequence analysis in patients with clinically diagnosed leber’s hereditary optic neuropathy

Amaç: Klinik olarak Leber’in Herediter Optik Nöropatisi (LHON) tanısı alan hastalarda, kesin tanı ve genetik danışmanlık verilebilmesi için olası mitokondriyal DNA mutasyonlarının araştırılması. Gereç ve Yöntemler: 1982-2007 yılları arasında klinik olarak LHON tanısı alan 10 hasta kliniğimize davet edildi. Bütün olgularda rutin oftalmolojik muayeneden sonra alınan periferik venöz kan örneklerinden mitokondriyal DNA ayrıştırılarak, polimeraz zincir reaksiyonu (PCR) ve mitokondriyal DNA dizi analizi yapıldı. Bulgular: 6 hastadan birinde Homozigot mutant olarak m.11778G>A tek nükleotid değişimi (SNP) saptandı. 12 olgunun hepsinde m.14212C>T ve m.14580G>A SNP bulundu. 6 hastanın üçünde saptadığımız m.11719A>G SNP, aynı zamanda kontrol grubundaki 4 kişide de saptandı. 6 hastanın ikisinde m.3197T>C SNP ve bunlardan birinde ayrıca m.14258G>A SNP bulunurken bu SNP’lerin hiçbirine kontrol grubunda rastlanmadı. Sonuç: Klinik olarak LHON tanısı alan olgularımızdan sadece birinde bilinen mutasyonlardan biri saptanarak klinik tanı moleküler genetik olarak da desteklendi. m.14258G>A SNP’nin optik nöropatiye yol açabilecek olası mitokondriyal DNA mutasyonu olabileceği öngörüsündeyiz. Bu hipotezimizin kesinlik kazanabilmesi için olgu ve kontrol grubu sayısının arttırılarak çalışılmasına ihtiyaç vardır.Objective: To investigate possible mitochondrial DNA (mtDNA) mutations in patients with Leber’s hereditary optic neuropathy (LHON) in order to provide a precise diagnosis and genetic counseling. Material and Methods: Between 1982 and 2007, ten patients were clinically diagnosed with LHON and six of these patients agreed to be involved in this study. Six healthy individuals were also included as a control group. mtDNA was isolated from peripheral blood samples and polymerase chain reaction and mtDNA sequence analysis were performed. Results: In one of the six patients, a homoplasmic mutant m.11778G>A mutation was detected. All of the clinically diagnosed LHON patients and the control groups had the m.14212C>T and m.14580G>A single nucleotide polymorphisms (SNPs). The m.11719A>G SNP was detected in three of six patients and four of the controls. Two of the six patients had the m.3197T>C SNP and, in addition, the m.14258G>A SNP was found in one of these two patients, while neither of these mutations were present in the control group. Conclusion: The clinical diagnosis of LHON could be supported by molecular genetics only in one patient by the detection of one mutation. The m.3197T>C and m.14258G>A SNPs should be considered as potential mtDNA mutations due to the fact that they were detected in the patient group. These mutations should be investigated further in large case groups for suspected gene loci that could lead to optic neuropathy

Modeling of electrocardiogram signals using predefined signature and envelope vector sets

A novel method is proposed to model ECG signals by means of "predefined signature and envelope vector sets (PSEVS)." On a frame basis, an ECG signal is reconstructed by multiplying three model parameters, namely, predefined signature vector (PSV)(R)," "predefined envelope vector (PEV)(K)," and frame-scaling coefficient (FSC). All the PSVs and PEVs are labeled and stored in their respective sets to describe the signal in the reconstruction process. In this case, an ECG signal frame is modeled by means of the members of these sets labeled with indices R and K and the frame-scaling coefficient, in the least mean square sense. The proposed method is assessed through the use of percentage root-mean-square difference (PRD) and visual inspection measures. Assessment results reveal that the proposed method provides significant data compression ratio (CR) with low-level PRD values while preserving diagnostic information. This fact significantly reduces the bandwidth of communication in telediagnosis operations. Copyright (c) 2007 Hakan Gurkan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Publisher's Versio

Bürünsel, sözcüksel ve biçimbilgisel bilgiyi kullanan co-training ile Türkçe konuşma dilinin otomatik cümle bölütlemesi

Co-training, web sayfası sınıflandırması, kelime anlam açıklaştırma ve adlandırılmış varlık tanıma gibi pek çok sınıflandırma işlevinde başarı ile kullanılan oldukça etkili bir makine öğrenme algoritmasıdır. Co-training, elle etiketlenmiş eğitim veri setine, etiketlenmemiş büyük miktarlardaki veriyi belirli miktarlarda etiketleyerek katmak suretiyle öğreticili öğrenme algoritmalarının performansını arttıran bir yarı öğreticili öğrenme metodudur. Co-training algoritmaları etiketlenmiş giriş verisine ilişkin farklı bakışlar üzerinde eğitilmiş iki veya daha fazla sınıflandırıcının üretilmesi ve daha sonra bu sınıflandırıcıların etiketlenmemiş veriyi ayrı ayrı etiketlemesi için kullanıldığı algoritmalardır. Otomatik olarak en güvenilir biçimde etiketlenmiş örnekler daha sonra insanlar tarafından elle etiketlenmiş veriye katılmaktadır. Bu işlem pekçok defa devam ettirilmektedir. Bu projede konuşma verisine ilişkin bürünsel, sözcüksel ve biçimbilgisel bilgilerin bakış olarak kullanıldığı co-training ile cümle bölütlemenin gerçekleştirilmesi ele alınmıştır. Cümle Bölütleme işlevi standart konuşma tanıyıcılarının çıkışından elde edilen işlenmemiş kelime dizisi biçimindeki veriyi zenginleştirmeyi amaçlayan bir işlemdir. Bu işlemin rolü, kelime dizisi biçiminde olan verinin cümle ünitelerine ayrılmasını sağlamaktır. Cümle Bölütleme konuşma anlamaya kadar olan süreçte ilk adımdır. Cümle bölütleme işlevi, çözümleme, makine çevirimi, bilgi çıkarımı gibi cümle bölütlemenin yapıldığının varsayıldığı konuşma işlemenin daha ileri uygulamaları için bir ön adım olarak gerçekleştirilmektedir. Cümle sınırları belirlendikten sonra bu cümleler üzerinde daha ileri düzeydeki sözdizimsel ve/veya anlamsal analizler gerçekleştirilebilmektedir. Bu projede konuşma özellikleri (bürünsel, sözcüksel ve biçimbilgisel) ayrışık ve doğal özellik seti olarak ele alınmış ve bu özellik setlerinin co-training algoritması ile kullanılması ile baseline sistemin performansının arttırılmasına çalışılmıştır. Ayrıca, co-training için uzlaşma ve uzlaşmama adı verilen farklı öğrenme stratejileri de araştırılmıştır. Buna ek olarak, self-combined adını verdiğimiz ve kendi kendine eğitme ile co-training yaklaşımlarının bir araya getirildiği bir yaklaşım da öne sürülmüştür.Co-training is a very effective machine learning technique that has been used successfully in several classification tasks like web page classification, word sense disambiguation, and named entity recognition. Co-training is a semi-supervised learning method that aims to improve performance of a supervised learning algorithm by incorporating large amounts of unlabeled data into the training data set. Co-training algorithms work by generating two or more classifiers trained on different views of the input labeled data that are then used to label the unlabeled data separately. The most confidently labeled examples of the automatically labeled data can then be added to the set of manually labeled data. The process may continue for several iterations. In this project, we have described the application of the co-training method for sentence segmentation where we used the prosodic, lexical and morphological information as the views of the data. Sentence segmentation from speech is part of a process that aims at enriching the unstructured stream of words that are the output of standard speech recognizers. Its role is to find the sentence units in this stream of words. Sentence segmentation is a preliminary step toward speech understanding. It is of particular importance for speech related applications, as most of the further processing steps, such as parsing, machine translation and information extraction, assume the presence of sentence boundaries. In this project, we consider the speech features (prosodic, lexical and morphological) as disjoint and natural feature sets or views and we try to improve performance of the baseline by using these feature sets with the co-training algorithm, Furthermore we have tried to investigate the different learning strategies for the co-training such as agreement and disagreement. In addition to these strategies it has been proposed that a new approach that we called self-combined which is the mixed version of the self-training and co-training approaches.TÜBİTA

Extracting and using prosodic information for Turkish spoken language processing

Bu projede genel olarak, konuşulan dili (Türkçe) anlamada, konuşulan dilin bürünsel/ezgisel (prosodic) ve sözcüksel (lexical) özelliklerinin ortaya çıkarılması ve bu özelliklerin konuşulan dilin bilgisayarla otomatik olarak işlenmesinde kullanılması amaçlanmaktadır. Bu daha özel olarak, otomatik konuşma tanıyıcısının (ASR) çıkışına ilişkin cümle bölütleme işlevini içermektedir. Otomatik konuşma tanıma sistemlerinden çıkan yazılı metnin özellikle noktalama (punctuation), büyük küçük harf farklılıkları ve vurgu, tonlama, perde, durak gibi konuşmaya ilişkin temel bazı parametrelerden yoksun olması veya bu özellikleri kaybetmiş olması, özellikle anlamda farklılıklara yol açmaktadır. Bu çıktının zenginleştirilmesi (enrichment) başka bir deyiş ile bu özelliklerin tekrar geriye kazandırılması, bu metinlerin hem insanlar tarafından okunmasını ve doğru algılanmasını hem de makineler tarafından işlenmesini kolaylaştıracaktır. Bu projedeki amaç, bu zenginleştirme ve geri kazandırım işleminin dilin bürünsel özelliklerinden yararlanarak yapılmasıdır.The text which the output of the Automatic Speech Recognition (ASR) system lacks especially punctuation, differences in the capitalization and the parameters related to the speaking such as stress, tone, pitch, pause cause some differences in the meaning. Enrichment of this output or another words to gain this features back to the output will provide either reading and understanding of the humans or processing of the machines easily. The aim of this project is doing this enrichment and the process of gaining back by using the prosodic features of the spoken language. In this proposal, we would like to examine the extraction and use of prosodic information in addition to lexical features for spoken language processing of Turkish. Specifically, we would like to research the use of prosodic features for sentence segmentation of Turkish speech. Another outcome of the project is to obtain a database of prosodic features at the word and morpheme level, which can be used for other purposes such as morphological disambiguation or word sense disambiguation. Turkish is an agglutinative language. Thus, the text should be analyzed morphologically in order to determine the root forms and the suffixes of the words before further analysis. In the framework of this project, we also would like to examine the interaction of prosodic features with morphological information. The role of sentence segmentation is to detect sentence boundaries in the stream of words provided by the ASR module for further downstream processing. This is helpful for various language processing tasks, such as parsing, machine translation and question answering. We formulate sentence segmentation as a binary classification task. For each position between two consecutive words the system must decide if the position marks a boundary between two sentences or if the two neighboring words belong to the same sentence. The sentence segmentation process is established by combining the Hidden Event Language Models (HELMs) with discriminative classification methods. The HELM takes into account the sequence of words and the output discriminative classification methods such as decision tree that is based on prosodic features such as pause durations. The new approach combines the HELMs for exploiting lexical information, with maximum entropy and boosting classifiers that tightly integrate lexical, as well as prosodic, speaker change and syntactic features. The boostingbased classifier alone performs better than all the other classification schemes. When combined with a hidden event language model the improvement is even more pronounced.Publisher's Versio

An Optimization Approach for a Fresh Food Supply Chain: An Application for the Orange Supply Chain Design in Turkey

The optimization of supply chain problems in various industry areas is crucial in terms of controlling the quality of the products and costs during the supply chain processes. Protecting and controlling the quality of the product in the food supply chain processes while minimizing the cost is a difficult and critical problem in the food industry. In this study, an application of a model that integrates the quality of the food in decision-making on distribution and production in a food supply chain is implemented using real-life data in Turkey. The degradation of quality of products in storage or transportation is usually based on the storage temperature, storage time, and other constants such as activation energy. Therefore, prediction for the quality of food products is a complex task because of the dynamics of storage conditions and various product characteristics. A methodological approach is proposed to model the degradation of food quality in this study. The rate of quality degradation of food products is evaluated by the proposed approach. A mixed-integer programming model is developed for the optimization of distribution and production planning. To solve the problem, GAMS (General Algebraic Modeling System) CPLEX solver is used as an optimization tool. The results of the case study shows that the suggested model in this study is implementable to the problem with acceptable solution time. In addition, the suggested model is adaptable for different types of food supply chains. This study aims to develop a methodological approach that can be used as a guide for decision-makers

Two Cases of Cystic Fibrosis with Compound Heterozygous Variants Reported for the First Time

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Modeling Electrocardiogram (ECG) signals via signature and envelope functions

Bu çalışmada, EKG işaretlerinin Temel Tanım ve Zarf Fonksiyonları ile modellenmesine yönelik yeni bir yöntem sunulmaktadır. Sunulan yöntem, herhangi bir EKG işaretine ilişkin Xi(t) çerçeve fonksiyonunu  biçiminde modellemektedir. Bu modelde, jR(t), Temel Tanım Fonksiyonu olarak adlandırılmakta ve bir Ci katsayısı ile Xi çerçeve vektörünün en yüksek enerjisini taşımaktadır. aK(t), Zarf Fonksiyonu olarak adlandırılmakta ve Xi çerçeve vektörünün zarfını oluşturmaktadır. Ci katsayısı da Çerçeve Ölçekleme Katsayısı olarak adlandırılmaktadır. Temel Tanım ve Zarf Fonksiyonları iletim bandının herbir düğümüne yerleştirilerek EKG işaretinin iletimi, Temel Tanım ve Zarf Vektör Bankasının R ve K indislerinin ve Ci katsayısının iletimine indirgenerek önemli bir sıkıştırma oranı gerçeklenmiştir.Anahtar Kelimeler: Sıkıştırma, Modelleme, EKG.In this paper, a new method to model ECG signals by means of "Signature and Envelope Functions" is presented. In this work, on a frame basis, any ECG signal Xi(t) is modeled by the form of . In this model, jR(t) is defined as the Signature Function since it carries almost maximum energy of the frame vector Xi with a constant Ci. aK(t) is referred to as Envelope Function since it matches the envelope of CijR(t) to the original frame vector Xi; and Ci is called the Frame-Scaling Coefficient. It has been demonstrated that the sets F={jr(t)} and A={ak(t)} constitute a "Signature and Envelope Functional Banks" to describe any measured ECG signal. Thus, ECG signal for each frame is described in terms of the two indices "R" and "K" of Signature and Envelope Functional Banks and the frame-scaling coefficient Ci. It has been shown that the new method of modeling provides significant data compression with low level reconstruction error while preserving diagnostic information in the reconstructed ECG signal.. Furthermore, once Signature and Envelope Functional Banks are stored on each communication node, transmission of ECG signals reduces to the transmission of indexes "R" and "K" of [ak(t),jr(t)] pairs and the coefficient Ci, which also result in considerable saving in the transmission band.  Keywords: Compression, Modeling, ECG.

KRAS Mutation in Small Cell Lung Carcinoma and Extrapulmonary Small Cell Cancer

Background: Lung cancer is one of the most lethal cancers. It is mainly classified into 2 groups: non-small cell lung can-cer (NSCLC) and small cell lung cancer (SCLC). Extrapul-monary small cell carcinomas (EPSCC) are very rare. The Ras oncogene controls most of the cellular functions in the cell. Overall, 21.6% of human cancers contain a Kirsten Ras (KRAS) mutation. SCLC and EPSCC have several similar features but their clinical course is different.Aims: We investigated the KRAS mutation status in SCLC and EPSCC.Study design: Mutation research.Methods: Thirty-seven SCLC and 15 EPSCC patients were included in the study. The pathological diagnoses were confirmed by a second pathologist. KRAS analysis was performed in our medical genetic department. DNA isola-tion was performed with primary tumor tissue using the QIAamp DNA FFPE Tissue kit (Qiagen; Hilden, Germany) in all patients. The therascreen KRAS Pyro Kit 24 V1 (Qia-gen; Hilden, Germany) was used for KRAS analyses. Results: Thirty-four (91.9%) of the SCLC patients were male, while 11 (73.3%) of the EPSCC l patients were fe-male. SCLC was more common in males, and EPSCC in females (p=0.001). A KRAS mutation was found in 6 (16.2%) if SCLC patients. The most common mutation was Q61R (CAA>CGA). Among the 15 EPSCC patients, 2 had a KRAS mutation (13.3%). When KRAS mutant and wild type patients were compared in the SCLC group, no differ-ence was found for overall survival (p=0.6).Conclusion: In previous studies, the incidence of KRAS mutation in SCLC was 1-3%; however, it was 16.2% in our study. Therefore, there may be ethnic and geographical differences in the KRAS mutations of SCLC. As a result, KRAS mutation should not be excluded in SCL

Lack of Association Between Toll-like Receptor 2 Polymorphisms (R753Q and A-16934T) and Atopic Dermatitis in Children from Thrace Region of Turkey

Background: Atopic dermatitis is the most common chronic inflammatory skin disease. A complex interaction of both genetic and environmental factors is thought to contribute to the disease. Aims: To evaluate whether single nucleotide polymorphisms in the TLR2 gene c.2258C>T (R753Q) (rs5743708) and TLR2 c.-148+1614T>A (A-16934T) (rs4696480) (NM_0032643) are associated with atopic dermatitis in Turkish children. Study Design: Case-control study. Methods: The study was conducted on 70 Turkish children with atopic dermatitis aged 0.5-18 years. The clinical severity of atopic dermatitis was evaluated by the severity scoring of atopic dermatitis index. Serum total IgE levels, specific IgE antibodies to inhalant and food allergens were measured in both atopic dermatitis patients and controls, skin prick tests were done on 70 children with atopic dermatitis. Genotyping for TLR2 (R753Q and A-16934T) single nucleotide polymorphisms was performed in both atopic dermatitis patients and controls. Results: Cytosine-cytosine and cytosin-thymine genotype frequencies of the TLR2 R753Q single nucleotide polymorphism in the atopic dermatitis group were determined as being 98.6% and 1.4%, cytosine allele frequency for TLR2 R753Q single nucleotide polymorphism was determined as 99.29% and the thymine allele frequency was 0.71%, thymine-thymine, thymineadenine, and adenine-adenine genotype frequencies of the TLR2 A-16934T single nucleotide polymorphism were 24.3%, 44.3%, and 31.4%. The thymine allele frequency for the TLR2 A-16934T single nucleotide polymorphism in the atopic dermatitis group was 46.43%, and the adenine allele frequency was 53.57%, respectively. There was not statistically significant difference between the groups for all investigated polymorphisims (p>0.05). For all single nucleotide polymorphisms studied, allelic distribution was analogous among atopic dermatitis patients and controls, and no significant statistical difference was observed. No homozygous carriers of the TLR2 R753Q single nucleotide polymorphism were found in the atopic dermatitis and control groups. Conclusion: The TLR2 (R753Q and A-16934T) single nucleotide polymorphisms are not associated with atopic dermatitis in a group of Turkish patients