Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

Genotype-phenotype analysis of three Chinese families with Jervell and Lange-Nielsen syndrome

Long QT syndrome (LQTS) is characterized by QT prolongation, syncope and sudden death. This study aims to explore the causes, clinical manifestations and therapeutic outcomes of Jervell and Lange-Nielsen syndrome (JLNS), a rare form of LQTS with congenital sensorineural deafness, in Chinese individuals.Three JLNS kindreds from the Chinese National LQTS Registry were investigated. Mutational screening of KCNQ1 and KCNE1 genes was performed by polymerase chain reaction and direct DNA sequence analysis. LQTS phenotype and therapeutic outcomes were evaluated for all probands and family members.We identified 7 KCNQ1 mutations. c.1032_1117dup (p.Ser373TrpfsX10) and c.1319delT (p.Val440AlafsX26) were novel, causing JLNS in a 16-year-old boy with a QTc (QT interval corrected for heart rate) of 620 ms and recurrent syncope. c.605-2A>G and c.815G>A (p.Gly272Asp) caused JLNS in a 12-year-old girl and her 5-year-old brother, showing QTc of 590 to 600 ms and recurrent syncope. The fourth JLNS case, a 46-year-old man carrying c.1032G>A (p.Ala344Alasp) and c.569G>A (p.Arg190Gln) and with QTc of 460 ms, has been syncope-free since age 30. His 16-year-old daughter carries novel missense mutation c.574C>T (p.Arg192Cys) and c.1032G>A(p.Ala344Alasp) and displayed a severe phenotype of Romano-Ward syndrome (RWS) characterized by a QTc of 530 ms and recurrent syncope with normal hearing. Both the father and daughter also carried c.253G>A (p.Asp85Asn; rs1805128), a rare single nucleotide polymorphism (SNP) on KCNE1. Bizarre T waves were seen in 3/4 JLNS patients. Symptoms were improved and T wave abnormalities became less abnormal after appropriate treatment.This study broadens the mutation and phenotype spectrums of JLNS. Compound heterozygous KCNQ1 mutations can result in both JLNS and severe forms of RWS in Chinese individuals.SCI(E)CPCI-S(ISTP)PubMed0MEETING ABSTRACT267-75

Hypoglycaemia detection for type 1 diabetic patients based on ECG parameters using Fuzzy Support Vector Machine

Nocturnal hypoglycaemia in type 1 diabetic patients can be dangerous in which symptoms may not be apparent while blood glucose level decreases to very low level, and for this reason, an effective detection system for hypoglycaemia is crucial. This research work proposes a detection system for the hypoglycaemia based on the classification of electrocardiographic (ECG) parameters. The classification uses a Fuzzy Support Vector Machine (FSVM) with inputs of heart rate, corrected QT (QTc) interval and corrected TpTe (TpTe c) interval. Three types of kernel functions (radial basis function (RBF), exponential radial basis function (ERBF) and polynomial function) are investigated in the classification. Moreover, parameters of the kernel functions are tuned to find the optimum of the classification. The results show that the FSVM classification using RBF kernel function demonstrates better performance than using SVM. However, both classifiers result approximately same performance if ERBF and polynomial kernel functions are used. © 2010 IEEE

LQTS Gene LOVD Database

The Long QT Syndrome (LQTS) is a group of genetically heterogeneous disorders that predisposes young individuals to ventricular arrhythmias and sudden death. LQTS is mainly caused by mutations in genes encoding subunits of cardiac ion channels (KCNQ1, KCNH2, SCN5A, KCNE1, and KCNE2). Many other genes involved in LQTS have been described recently (KCNJ2, AKAP9, ANK2, CACNA1C, SCNA4B, SNTA1, and CAV3). We created an online database (http://www.genomed.org/LOVD/introduction.html) that provides information on variants in LQTS-associated genes. As of February 2010, the database contains 1738 unique variants in 12 genes. A total of 950 variants are considered pathogenic, 265 are possible pathogenic, 131 are unknown/unclassified, and 292 have no known pathogenicity. In addition to these mutations collected from published literature, we also submitted information on gene variants, including one possible novel pathogenic mutation in the KCNH2 splice site found in ten Chinese families with documented arrhythmias. The remote user is able to search the data and is encouraged to submit new mutations into the database. The LQTS database will become a powerful tool for both researchers and clinicians. © 2010 Wiley-Liss, Inc

Automatic detection of early repolarization in ECG signal

Abstract. The early repolarization is one form of heart’s electrical disorder. The scope of this thesis is to develop an algorithm, which detects the marks of the early repolarization from the electrocardiography data. The definition of the early repolarization was fine-tuned in 2015 and the updated definition is used in this thesis. The implementation of the algorithm is done with Matlab. The theory part of this work includes description of the heart’s structure, physiology and review of the most common heart diseases. The heart’s electrical functionality is explained in more detail and the principle of the electrocardiography is viewed, including the main precepts of the analysis of electrocardiography data. The definition of the early repolarization is presented in detail and the significance of this phenomenon is evaluated based on the research data. This work also includes short survey of some of the existing methods for detecting the early repolarization from the electrocardiography data. Thesis includes also the description of the algorithm developed in this work and the analysis of the results. The performance of the algorithm is evaluated with manually classified ECG test set. The sensitivity of the algorithm is 94.0% and the specificity is 92.2%. The correlation to mortality was also studied for few different versions of the algorithm with the Health 2000 data. The correlation to mortality is found with two algorithm versions. The algorithm version with slightly relaxed early repolarization definition shows increased risk for all-cause-mortality in inferior leads, when the slur detection is deactivated. The algorithm version with precise thresholds of the early repolarization definition shows increased risk for all-cause-mortality and for cardiac death in inferior leads, when the slur detection is deactivated.Tiivistelmä. Tämä diplomityö käsittelee sydämen sähköisen toiminnan häiriötilaa, jota kutsutaan aikaiseksi repolarisaatioksi. Työn tavoitteena on kehittää algoritmi havaitsemaan aikaisen repolarisaation merkit sydämen elektrokardiografia mittausdatasta. Tämä työ perustuu vuonna 2015 tarkennettuun aikaisen repolarisaation määritelmään. Työssä kehitetty algoritmi on toteutettu Matlabilla. Diplomityön teoriaosuudessa käydään läpi sydämen rakennetta, fysiologiaa ja yleisimpiä sydänsairauksia. Työssä tutustutaan tarkemmin sydämen sähköiseen toimintaan, elektrokardiografian tuottamaan dataan ja siihen, miten tätä dataa voidaan tulkita. Aikainen repolarisaatio käsitellään omana osionaan, jossa käydään läpi sen tarkka määritelmä, arvioidaan tutkimuksiin pohjautuen ilmiön merkitsevyyttä sekä esitellään muutamia olemassa olevia menetelmiä aikaisen repolarisaation havaitsemiseen elektrokardiografia datasta. Työ sisältää myös kehitetyn algoritmin esittelyn ja tulosten analysointia. Algoritmin suorituskyky todettiin testisetillä, joka sisältää manuaalisesti luokiteltuja elektrokardiografia signaaleita. Algoritmin sensitiivisyys on 94,0% ja spesifisyys 92,2%. Tämän lisäksi ajettiin testejä kuolleisuus korrelaation selvittämiseksi muutamalla algoritmin variaatiolla Terveys 2000 datalle. Korrelaatio kuolleisuuteen löytyi kahdella algoritmivariaatiolla. Algoritmiversio hieman väljennetyillä aikaisen repolarisaation kynnysarvoilla ennustaa kohonnutta riskiä kokonaiskuolleisuuteen inferiorisissa signaaleissa, slur-tunnistuksen ollessa pois käytöstä. Algoritmiversio aikaisen repolarisaation määritelmän mukaisilla tarkoilla kynnysarvoilla ennustaa kohonnutta riskiä sekä kokonaiskuolleisuuteen että sydänperäiseen kuolemaan inferiorisissa signaaleissa, slur-tunnistuksen ollessa pois käytöstä

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity. © 2011 Biomedical Engineering Society