The hidden waves in the ECG uncovered: a sound automated interpretation method

A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental $P$, $Q$, $R$, $S$ and $T$ waves plus an error term to account for artefacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow to all the different patterns in heartbeats be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet, the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors

Bottom-up design of artificial neural network for single-lead electrocardiogram beat and rhythm classification

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined in terms of perfecting individual sub-systems to improve the over all system performance is used. Sub-systems include pre-processing, QRS detection and fiducial point estimations, feature calculations, and pattern classification. Inaccuracies in time-domain fiducial point estimations are overcome with the derivation of features in the frequency domain. Feature extraction in frequency domain is based on a spectral estimation technique (combination of simulation and subtraction of a normal beat). Auto-regressive spectral estimation methods yield a highly sensitive spectrum, providing several local features with information on beat classes like flutter, fibrillation, and noise. A total of 27 features, including 16 in time domain and 11 in frequency domain are calculated. The entire data and problem are divided into four major groups, each group with inter-related beat classes. Classification of each group into related sub-classes is performed using smaller feed-forward neural networks. Input feature sub-set and the structure of each network are optimized using an iterative process. Optimal implementations of feed-forward neural networks provide high accuracy in beat classification. Associated neural networks are used for the more deterministic rhythm-classification task. An accuracy of more than 85% is achieved for all 13 classes included in this study. The system shows a graceful degradation in performance with increasing noise, as a result of the noise consideration in the design of every sub-system. Results indicate a neural network-based bottom-up design of single-lead ECG classification is able to provide very high accuracy, even in the presence of noise, flutter, and fibrillation

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

Feature Selection and Non-Euclidean Dimensionality Reduction: Application to Electrocardiology.

Heart disease has been the leading cause of human death for decades. To improve treatment of heart disease, algorithms to perform reliable computer diagnosis using electrocardiogram (ECG) data have become an area of active research. This thesis utilizes well-established methods from cluster analysis, classification, and localization to cluster and classify ECG data, and aims to help clinicians diagnose and treat heart diseases. The power of these methods is enhanced by state-of-the-art feature selection and dimensionality reduction. The specific contributions of this thesis are as follows. First, a unique combination of ECG feature selection and mixture model clustering is introduced to classify the sites of origin of ventricular tachycardias. Second, we apply a restricted Boltzmann machine (RBM) to learn sparse representations of ECG signals and to build an enriched classifier from patient data. Third, a novel manifold learning algorithm is introduced, called Quaternion Laplacian Information Maps (QLIM), and is applied to visualize high-dimensional ECG signals. These methods are applied to design of an automated supervised classification algorithm to help a physician identify the origin of ventricular arrhythmias (VA) directed from a patient's ECG data. The algorithm is trained on a large database of ECGs and catheter positions collected during the electrophysiology (EP) pace-mapping procedures. The proposed algorithm is demonstrated to have a correct classification rate of over 80% for the difficult task of classifying VAs having epicardial or endocardial origins.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113303/1/dyjung_1.pd

Flexible Time Series Matching for Clinical and Behavioral Data

Time Series data became broadly applied by the research community in the last decades after a massive explosion of its availability. Nonetheless, this rise required an improvement in the existing analysis techniques which, in the medical domain, would help specialists to evaluate their patients condition. One of the key tasks in time series analysis is pattern recognition (segmentation and classification). Traditional methods typically perform subsequence matching, making use of a pattern template and a similarity metric to search for similar sequences throughout time series. However, real-world data is noisy and variable (morphological distortions), making a template-based exact matching an elementary approach. Intending to increase flexibility and generalize the pattern searching tasks across domains, this dissertation proposes two Deep Learning-based frameworks to solve pattern segmentation and anomaly detection problems. Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is proposed, learning to distinguish, point-by-point, desired sub-patterns from background content within a time series. The proposed framework was validated in two use-cases: electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed two conventional matching techniques, being capable of notably detecting the targeted cycles even in noise-corrupted or extremely distorted signals, without using any reference template nor hand-coded similarity scores. Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction ability of Variational Autoencoders and a local similarity score to identify non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results indicated competitiveness relative to recent techniques, achieving detection AUC scores of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade científica nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este aumento exigiu uma melhoria das atuais técnicas de análise que, no domínio clínico, auxiliaria os especialistas na avaliação da condição dos seus pacientes. Um dos principais tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação). Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade para procurar por subsequências similares ao longo de séries temporais. Todavia, dados do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura de padrões entre domínios, esta dissertação propõe duas abordagens baseadas em Deep Learning para solucionar problemas de segmentação de padrões e deteção de anomalias. Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente, mesmo em sinais corrompidos por ruído ou extremamente distorcidos, sem o uso de nenhum padrão de referência nem métricas de similaridade codificadas manualmente. A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade local para identificar anomalias desconhecidas. A proposta foi validada na identificação de arritmias cardíacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidenceThis research was funded by the Ministry of Science, Innovation and Universities of Spain, and the European Regional Development Fund of the European Commission, Grant Nos. RTI2018-095324-B-I00, RTI2018-097122-A-I00, and RTI2018-099646-B-I00S

Morphological Variability Analysis of Physiologic Waveform for Prediction and Detection of Diseases

For many years it has been known that variability of the morphology of high-resolution (∼30-1000 Hz) physiological time series data provides additional prognostic value over lower resolution (≤ 1Hz) derived averages such as heart rate (HR), breathing rate (BR) and blood pressure (BP). However, the field has remained rather ad hoc, based on hand-crafted features. Using a model-based approach we explore the nature of these features and their sensitivity to variabilities introduced by changes in both the sampling period (HR) and observational reference frame (through breathing). HR and BR are determined as having a statistically significant confounding effect on the morphological variability (MV) evaluated in high-resolution physiological time series data, thus an important gap is identified in previous studies that ignored the effects of HR and BR when measuring MV. We build a best-in-class open-source toolbox for exploring MV that accounts for the confounding factors of HR and BR. We demonstrate the toolbox’s utility in three domains on three different signals: arterial BP in sepsis; photoplethysmogram in coarctation of the aorta; and electrocardiogram (ECG) in post-traumatic stress disorder (PTSD). In each of the three case studies, incorporating features that capture MV while controlling for BR and/or HR improved disease classification performance compared to previously established methods that used features from lower resolution time series data. Using the PTSD example, we then introduce a deep learning approach that significantly improves our ability to identify the effects of PTSD on ECG morphology. In particular, we show that pre-training the algorithm on a database of over 70,000 ECGs containing a set of 25 rhythms, allowed us to boost performance from an area under the receiver operating characteristic curve (AUROC) of 0.61 to 0.85. This novel approach to identifying morphology indicates that there is much more to morphological variability during stressful PTSD-related events than the simple periodic modulation of the T-wave amplitude. This research indicates that future work should focus on identifying the etiology of the dynamic features in the ECG that provided such a large boost in performance, since this may reveal novel underlying mechanisms of the influence of PTSD on the myocardium.Ph.D