Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks

Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model

Skylab vectorcardiograph: System description and in flight operation

A vectorcardiograph system was used to measure cardiac electrical activity of Skylab crewmen. This system was chosen because of its data-quantification advantages. The vectorcardiograph was required to meet recommended American Heart Association specifications, to withstand space environmental extremes, and to facilitate data gathering in the weightless environment. The vectorcardiograph system performed without failure, and all projected data were acquired. The appendix lists the design specifications used for the Skylab vectorcardiograph system

Extracting heart rate dependent electrocardiogram templates for a body emulator environment

Abstract. Medical device and analysis method developments often include tests on humans, which are expensive, time consuming, and sometimes even dangerous. In order to perform human tests, special safety conditions and ethical and legal requirements must be taken into account. Emulators that can emulate the physiological functions of the human body could solve these difficulties. In this study, the heart rate depended electrocardiogram templates for this kind of an emulator were extracted. The real-life electrocardiogram preprocessing included a high-pass filter and a Savitzky-Golay filter. A beat detection algorithm was developed to detect QRS complexes in the signals and classify beat artefacts based on the RR interval sequences and two adaptive thresholds. Heart rate levels were detected using the K-means clustering technique. Vectorcardiogram signals were converted from the electrocardiogram signals using the inverse Dower’s transformation matrix, and vectorcardiogram templates were extracted to the respective heart rate levels. Finally, a graphical user interface was created for the mentioned methods. The developed beat detection algorithm was tested with the MIT-BIH Arrhythmia Database and the comparison was made with the state-of-the-art algorithms. The beat detection algorithm resulted the sensitivity of 99.77 \%, precision of 99.65 \%, and detection error rate of 0.58 \%. Based on the results, the proposed methods and extracted vectorcardiogram templates were successful.Sykkeestä riippuvien elektrokardiogrammimallien poiminta kehoemulaattoriympäristöön. Tiivistelmä. Lääketieteellisten laitteiden ja analyysimenetelmien kehitystyö sisältää usein ihmisille suoritettavia testejä, jotka ovat kalliita, aikaa vieviä ja joskus jopa vaarallisia. Ihmiskokeiden toteuttamiseksi on otettava huomioon erityisiä turvallisuusehtoja, sekä eettisiä ja laillisia vaatimuksia. Emulaattorit, jotka pystyvät jäljittelemään ihmiskehon fysiologisia toimintoja, voivat olla ratkaisu näihin ongelmiin. Tässä tutkimuksessa sykkeestä riippuvia elektrokardiogrammimalleja poimittiin tämän tyyppiselle emulaattorille. Tosielämän elektrokardiogrammisignaalien esikäsittely sisälsi ylipäästösuodattimen ja Savitzky-Golay suodattimen. Sydämen lyöntien tunnistussalgoritmi kehitettiin tunnistamaan QRS-komplekseja signaaleista ja luokittelemaan lyöntiartefakteja RR-intervallisekvenssien ja kahden adaptiivisen kynnysarvon perusteella. Syketasot tunnistettiin käyttämällä K-means klusterointitekniikkaa. Vektorikardiogrammisignaalit muunnettiin elektrokardiogrammisignaaleista käyttämällä käänteistä Dowerin muunnosmatriisia ja vektorikardiogrammimallit poimittiin vastaaville syketasoille. Lopuksi luotiin graafnen käyttöliittymä mainituille menetelmille. Kehitetty lyöntien tunnistusalgoritmi testattiin MIT-BIH Arrhythmia Database-tietokannalla ja vertailu suoritettiin vastaavien algoritmien kanssa. Algoritmi suoriutui 99,77 % herkkyydellä, 99,65 % spesifsyydellä ja 0,58 % virheprosentilla. Tulosten perusteella ehdotetut menetelmät ja poimitut vektorikardiogrammimallit olivat onnistuneita

Derivation of respiration from electrocardiogram during heart rate variability studies

A method was developed to derive the respiration signal from the ECG signal based on the observation that the body-surface ECG is influenced by electrode motion relative to the heart and that fluctuations in the mean cardiac electrical axis accompany respiration. S-Plus programs were developed to calculate the changes in the value of the mean cardiac electrical axis during respiration from a two lead ECG signal and to generate a continuous ECG-derived respiratory signal from the angle information. Data were taken from 9 healthy subjects during rest, paced breathing and exercise. The respiration was derived from the recorded ECG signals. The ECG-derived respiration was compared with the original respiration recorded through an impedance pneumography device. The derived respiration shows an excellent correspondence with the original respiration. Statistical analysis indicates that the ECG-derived respiration has a high correlation with the original respiration in the frequency domain. Our study provides a method to obtain the respiration from the ECG signal when respiration information is not directly available. This can be done either directly or from a Holter recording. It is therefore possible to do spectral analysis of heart rate variability and determine the frequency of the spectral peak occurring at the respiration frequency

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S ( I ), THI, and SHI, where S ( I ) is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S ( I ),THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services

Nonlinear Stochastic Modeling and Analysis of Cardiovascular System Dynamics - Diagnostic and Prognostic Applications

The purpose of this investigation is to develop monitoring, diagnostic and prognostic schemes for cardiovascular diseases by studying the nonlinear stochastic dynamics underlying complex heart system. The employment of a nonlinear stochastic analysis combined with wavelet representations can extract effective cardiovascular features, which will be more sensitive to the pathological dynamics instead of the extraneous noises. While conventional statistical and linear systemic approaches have limitations for capturing signal variations resulting from changes in the cardiovascular system states. The research methodology includes signal representation using optimal wavelet function design, feature extraction using nonlinear recurrence analysis, and local recurrence modeling for state prediction.Industrial Engineering & Managemen

Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.</p