Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Background and objectives - Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. Methods - A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. Results - The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. Conclusions - In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal

Deteção de arritmias cardíacas em eletrocardiogramas usando deep learning

As arritmias cardíacas são perturbações do ritmo cardíaco comuns e podem causar sérios riscos na vida das pessoas, sendo hoje em dia umas das principais causas de morte na população em geral, essencialmente nos países desenvolvidos e em desenvolvimento. Muitas destas mortes poderiam ser evitadas se fosse realizada a deteção e a monitorização prévia destas arritmias a partir do Eletrocardiograma (ECG). O ECG é um exame fundamental no diagnóstico de doenças cardiovasculares e em várias patologias clínicas, registando a informação relativa ao funcionamento do coração através da sua atividade elétrica em cada batimento cardíaco. Através da análise dos dados obtidos por ECG, pretende-se a identificação dos períodos com maior probabilidade de ocorrência de arritmia, possibilitando assim uma maior eficácia na deteção e previsão dos sistemas baseados em ECG. Desta forma, os pacientes poderão melhorar bastante a sua qualidade de vida, garantindo uma maior rapidez na intervenção médica. Ademais, esta abordagem permitirá evitar os efeitos colaterais das arritmias e possivelmente reduzir a administração da medicação. O objetivo deste trabalho passa pelo desenvolvimento de uma metodologia capaz de classificar sinais resultantes do ECG, para deteção de arritmias cardíacas. São apresentadas nesta dissertação diversas técnicas utilizadas para o processamento e classificação dos sinais ECG, pretendendo-se que sejam aplicadas neste trabalho algumas destas técnicas.Heart arrhythmia, a group of conditions in which the heartbeat is irregular, is known nowadays as one of the main causes of death, targeting specially the population in developed and developing countries. Most of these fatalities could’ve been avoided by the previous detection and monitorization of this condition, through an Electrocardiography (ECG). The ECG is a fundamental exam on the diagnosis of heart conditions and several clinical pathologies, through the heart’s electrical activity, it registers information related to its behavior. Considering the data obtained from the ECG, it is intended the identification of periods in which there is more probability of arrhythmia occurrence, allowing a more efficient detection and prediction on ECG based systems. Taking this into account, it is foreseen an improvement on patient’s quality of life, assuring a quick medical intervention, avoiding the collateral effects of this condition, and possibly decreasing the dependence on medication. The main purpose of this work is centered on the development of a methodology, capable of identifying ECG signals, for the detection of heart arrhythmia. On this thesis, there will be presented several techniques for the processing and classification of ECG signals, some of which will be applied on this work

Artificial intelligence method for time series data mining - implementation on the human ECG signal

Tehnološki razvoj u modernom društvu kao jednu od posljedica ima i povećan broj generiranih podataka čija brojnost predstavlja značajan tehnički i znanstveni izazov u smislu pohrane i obrade.Za podatke koji u sebi ne sadrže vremensku komponentu, metode i tehnike za pohranu i analizu su veoma razvijene, ali za podatke koji su producirani slijedno ovo je još uvijek izazov. Podaci u vremenskim serijama nisu podobni za analizu klasičnim statističkim metodama jer je svaki podatak mjerenja direktno ovisan o prethodnom podatku mjerenom na istom izvoru. Ovime je prekršeno temeljno načelo klasičnih statističkih metoda o nezavisnosti opservacija u uzorku. Jedan od složenijih problema u analizi vremenskih serija je analiza elektrokardiograma (EKG-a). Ovaj rad predlaženovu metodu za analizu vremenskih serija te predstavlja istraživanje u kojem je ista metoda primijenjena u analizi ljudskog EKG signala. EKG kao postupak relativno niskih troškova koji je k tome ineinvazivan jest jedna od osnovnih dijagnostičkih metoda. Kako dugotrajno pregledavanje mnogo-brojnih EKG valova može biti naporno i neprakticno za ljudskog eksperta, računalna analiza EKG signala je značajan znanstveni i tehnički izazov s mnogim potencijalnim primjenama. Problem analize EKG signala obuhvaća nekoliko podrucja istraživanja poput uklanjanja šumova i smetnjikoje nastaju tijekom snimanja, detekcije otkucaja srca, analize ritma te raspoznavanja oblika EKG valova. Ovo istraživanje fokusirano je na detekciju otkucaja srca i raspoznavanje oblika valova.Inspiracija za razvoj metode dolazi iz spoznaja racunalne neuroznanosti, a metoda je u okviruovog istraživanja implementirana u programskom jeziku C++. Provedeni su eksperimenti u detekciji QRS kompleksa bez filtriranja signala, te detekciji QRS kompleksa i prepoznavanja oblikavalova nakon filtriranja signala. U tu svrhu su implementirani i digitalni filtri. U istraživanjusu dobiveni rezultati koji nadmašuju trenutno stanje tehnike te su dobivene spoznaje za daljnjirazvoj i primjenu metode i u podrucju racunalnog vida. Postignuta je tocnost detekcije otkucaja srca bez primjene filtara u prosjeku iznad 95% izracunato prema metodi unakrsne validacije nadsvakim zapisom, te iznad 99% nakon filtriranja signala prema više realisticnoj metodi testiranja baziranoj na subjektu te iznad 96% u raspoznavanju oblika EKG valova testirano prema prepo-rukama AAMI standarda. Takvo testiranje realno simulira potencijalnu klinicku primjenu. U smislu racunalnog vida, provedeni su eksperimenti u raspoznavanju rukom napisanih brojeva i drugih dvodimenzionalnih oblika. Rezultati u tim eksperimentima su približni trenutnom stanjutehnike i kreću se oko 90% tocnosti u raspoznavanju rukom napisanih brojeva iz MNIST skupa podataka.In this research, a new method (algorithm) of artificial intelligence for pattern recognition is proposed. The method is based on principles of human perception and it is a part of computer engineering domain, the field of artificial intelligence. Method is the result of perennial scientific research and development. The main implementation of the algorithm within the project is on the example of the human ECG signal analysis, which is one of the most demanding problems within the field of time series analysis. Research scope included software implementation and testing on the officially recognized databases of the human ECG signal (MIT-BIH Arrhythmia Database) by using the scientifically recognized metrics (specificity, sensitivity, positive predictivity etc.). The essence of the method is its algorithm, which, in the authors opinion, reminds of human perception principles. In the scientific literature no similar approach is yet known. The approach is based ona study of a specific field in Computational Neuroscience and also, on the conclusions about how brain neurons perceive stimuli coming from sensors (human senses). Beyond the analysis of ECG signals, the above method has many other applications, such as applications in finance, industry,energy, computer vision (recognition of 2D and 3D shapes or photographs after pre-processing) etc. Results achieved in the research are competitive with the current state of the art methods.Without signal filtering, QRS detection is accurate in more than 95% cases. After signal filtering, accuracy is above 99% tested with the subject-based methodology, which is the most realistic one. Heartbeat classification is accurate above 96% tested by the AAMI standard methodology.Handwritten character recognition is accurate around 90% (MNIST dataset). Methods are implemented in C++ programming language