Abnormal ECG search in long-term electrocardiographic recordings from an animal model of heart failure

Heart failure is one of the leading causes of death in the United States. Five million Americans suffer from heart failure. Advances in portable electrocardiogram (ECG) monitoring systems and large data storage space allow the ECG to be recorded continuously for long periods. Long-term monitoring could potentially lead to better diagnosis and treatment if the progression of heart failure could be followed. The challenge is to analyze the sheer mass of data. Manual analysis using the classical methods is impossible. In this dissertation, a framework for analysis of long-term ECG recording and methods for searching an abnormal ECG are presented.;The data used in this research were collected from an animal model of heart failure. Chronic heart failure was gradually induced in rats by aldosterone infusion and a high Na and low Mg diet. The ECG was continuously recorded during the experimental period of 11-12 weeks through radiotelemetry. The ECG leads were placed subcutaneously in lead-II configuration. In the end, there were 80 GB of data from five animals. Besides the massive amount of data, noise and artifacts also caused problems in the analysis.;The framework includes data preparation, ECG beat detection, EMG noise detection, baseline fluctuation removal, ECG template generation, feature extraction, and abnormal ECG search. The raw data was converted from its original format and stored in a database for data retrieval. The beat detection technique was improved from the original algorithm so that it was less sensitive to signal baseline jump and more sensitive to beat size variation. A method for estimating a parameter required for baseline fluctuation removal is proposed. It provides a good result on test signals. A new algorithm for EMG noise detection was developed using morphological filters and moving variance. The resulting sensitivity and specificity are 94% and 100%, respectively. A procedure for ECG template generation was proposed to capture gradual change in ECG morphology and manage the matching process if numerous ECG templates are created. RR intervals and heart rate variability parameters are extracted and plotted to display progressive changes as heart failure develops. In the abnormal ECG search, premature ventricular complexes, elevated ST segment, and split-R-wave ECG are considered. New features are extracted from ECG morphology. The Fisher linear discriminant analysis is used to classify the normal and abnormal ECG. The results provide classification rate, sensitivity, and specificity of 97.35%, 96.02%, and 98.91%, respectively

Analysis of Noise Sensitivity of Different ECG Detection Algorithms

This paper presents an analysis of noise sensitivities of different detection algorithms for electrocardiogram (ECG) taken from MIT-BIH arrhythmia database. Seven methods used in this paper are based on derivatives, digital filters (DF), neural network (NN) and wavelet transform (WT). The raw ECG is corrupted with 5 different types of synthesized noise, namely, power line interference, base line drift due to respiration, abrupt baseline shift, electromyogram (EMG) interference and a composite noise made from other types. A total of 315 data sets are constructed from 15 raw data sets for each type of noise adding 0%, 25%, 50%, 75% and 100% noise levels. The application of the methods to detect QRS complexes of a total of 33,774 beats of ECG shows that none of the algorithms are able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on NN and WT show better performance considering all noise types and the two algorithms perform similarly. The result of this study will help to develop a more robust ECG detector and this will make ECG interpretation system more effective.DOI:http://dx.doi.org/10.11591/ijece.v3i3.251

MicroECG: an integrated platform for the cardiac arrythmia detection and characterization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e ComputadoresO desenvolvimento de um pacote de software para lidar facilmente com electrocardiogramas de alta resolução tornou-se importante para pesquisa na área de electrocardiografia. O desenvolvimento de novas técnicas para detecção de potenciais tardios e outros problemas associados a arritmias cardíacas têm sido objecto de estudo ao longo dos anos. No entanto, ainda existe a lacuna de um pacote de software que facilmente permita implementar algumas destas inovadoras técnicas de uma forma integrada, possibilitando avaliar técnicas clássicas como o protocolo de Simson para a detecção de sinais não estacionários (potenciais tardios). Algumas destas inovadoras técnicas envolvem a detecção tempo-frequência usando escalogramas ou a análise espectral usando metodologias wavelet-packet, sendo implementadas no software desenvolvido com flexibilidade e versatilidade suficientes para que futuramente sirva de plataforma de pesquisa para o refinamento destas mesmas técnicas no que toca ao processamento de sinais de electrocardiogramas de alta resolução. O software aqui desenvolvido foi também desenhado de forma a suportar dois tipos de ficheiros diferentes provenientes de outros tantos sistemas de aquisição. Os sistemas suportados são o ActiveTwo da Biosemi e o USBamp da g.tec

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system

Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

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

Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD); and most existing algorithms are usually designed for the diagnosis (i.e.; feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper; we utilized the MIT-BIH AF Database (AFDB); which is composed of data from normal people and patients with AF and onset characteristics; and the AFPDB database (i.e.; PAF Prediction Challenge Database); which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF); and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction; we regarded diagnosis and prediction as two classification problems; adopted the traditional support vector machine (SVM) algorithm; and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process; the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases; the sensitivity; specificity; and accuracy measures were 99.2% and 99.2%; 99.2% and 93.3%; and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases; respectively. Moreover; the sensitivity; specificity; and accuracy were 94.2%; 79.7%; and 87.0%; respectively; when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels

Classification of operator’s workload based on physiological response

People spend most of their lives at work, during which time they are exposed to mechanical and environmental conditions that can harm their health. This risk can occur in an hour- long or over long periods, even when performed at a light to moderate intensity due to cumulative fatigue. Several measures have been proposed in order to prevent or reduce fatigue-inducing repetitive work. However, these measures are essentially subjective or only measure fatigue locally. Wearables are an attractive solution to measure work-related fatigue globally and at any time. The purpose of this study is to quantify biosignals information for the determination of fatigue while performing repetitive work. Electrocardiogram (ECG), electromyography (EMG), respiratory inductance plethysmography (RIP) and Accelerometer (ACC) signals were collected from 25 healthy participants. The participants were instructed to perform a repetitive task after induced fatigue. Their biosignals were processed, and different families of features were extracted. These features were used to fit a classifier in order to evaluate fatigue. Self-Similarity Matrix (SSM) was used to select and segment the data in Baseline and Fatigue. Autocorrelation of inertial measures, respiratory synchrony, and the root mean square of the cardiovascular load features achieved 88% of accuracy. It was possible to verify that the ACC’s features lead to the best classification results, followed by the RIP, EMG and finally the ECG’s features. Multimodal data allows global classification of when a person is working after expe- riencing fatigue. Motor information contributes significantly to this classification due to compensations that occur while performing the repetitive task. More studies should be done to develop an index characterising the fatigue state.As pessoas passam a maior parte da sua vida a trabalhar. A exposição a condições mecânicas e ambientais no trabalho pode ser prejudicial à sua saúde. Este risco pode ocorrer devido à fadiga cumulativa. Lesões podem surgir tanto em curtos como em longos períodos, mesmo quando a tarefa tem uma intensidade leve a moderada. Várias medidas foram propostas para prevenir ou reduzir o trabalho repetitivo que induz fadiga, no entanto, estas medidas são essencialmente subjetivas ou apenas medem a fadiga localmente. Os wearables são uma solução interessante para medir a fadiga relacionada ao trabalho a nível global e em qualquer momento. O objetivo deste estudo foi quantificar informações de biosinais para a determinação da fadiga durante a realização de trabalhos repetitivos. Os sinais de eletrocardiograma (ECG), eletromiografia (EMG), pletismografia de indutância respiratória (RIP) e acelerómetro (ACC) foram recolhidos de 25 participantes saudáveis. Os participantes realizaram uma tarefa repetitiva onde fadiga foi provocada. Os biosinais foram processados, e diferentes famílias de métricas foram extraídas. Estas métricas foram usadas para classificar a fadiga. Recorreu-se a Matrizes de Auto-Similaridade (SSM) para selecionar e segmentar os dados em fadiga e não fadiga. A autocorrelação das medidas inerciais, a sincronia respiratória e o quadrado médio da raiz da carga cardiovascular alcançaram 88% de precisão. Foi possível verificar que as features do ACC tiveram os melhores resultados de classificação, seguindo-se do RIP, EMG e, por último, de ECG. Os dados multimodais permitiram a classificação global de quando uma pessoa está a trabalhar, após sentir fadiga. A informação motora contribui, significativamente, para esta classificação devido às compensações que ocorrem durante a realização da tarefa repetitiva. Futuro trabalho deve ser feito com fim a determinar um índice que possa caracterizar o estado de fadiga