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

Detection of Bundle Branch Blocks using Machine Learning Techniques

The most effective method used for the diagnosis of heart diseases is the Electrocardiogram (ECG). The shape of the ECG signal and the time interval between its various components gives useful details about any underlying heart disease. Any dysfunction of the heart is called as cardiac arrhythmia. The electrical impulses of the heart are blocked due to the cardiac arrhythmia called Bundle Branch Block (BBB) which can be observed as an irregular ECG wave. The BBB beats can indicate serious heart disease. The precise and quick detection of cardiac arrhythmias from the ECG signal can save lives and can also reduce the diagnostics cost. This study presents a machine learning technique for the automatic detection of BBB. In this method both morphological and statistical features were calculated from the ECG signals available in the standard MIT BIH database to classify them as normal, Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). ECG records in the MIT- BIH arrhythmia database containing Normal sinus rhythm, RBBB, and LBBB were used in the study. The suitability of the features extracted was evaluated using three classifiers, support vector machine, k-nearest neighbours and linear discriminant analysis. The accuracy of the technique is highly promising for all the three classifiers with k-nearest neighbours giving the highest accuracy of 98.2%. Since the ECG waveforms of patients with the same cardiac disorder is similar in shape, the proposed method is subject independent. The proposed technique is thus a reliable and simple method involving less computational complexity for the automatic detection of bundle branch block. This system can reduce the effort of cardiologists thereby enabling them to concentrate more on treatment of the patients

A Deep Learning Classifier for Detecting Atrial Fibrillation in Hospital Settings Applicable to Various Sensing Modalities

Cardiac signals provide variety of information related to the patient\u27s health. One of the most important is for medical experts to diagnose the functionality of a patient’s heart. This information helps the medical experts monitor heart disease such as atrial fibrillation and heart failure. Atrial fibrillation (AF) is one of the most major diseases that are threatening patients’ health. Medical experts measure cardiac signals usng the Electrocardiogram (ECG or EKG), the Photoplethysmogram (PPG), and more recently the Videoplethysmogram (VPG). Then they can use these measurements to analyze the heart functionality to detect heart diseases. In this study, these three major cardiac signals were used with different classification methodologies such as Basic Thresholding Classifiers (BTC), Machine Learning (SVM) classifiers, and deep learning classifiers based on Convolutional Neural Networks (CNN) to detect AF. To support the work, cardiac signals were acquired from forty-six AF subjects scheduled for cardioversion who were enrolled in a clinical study that was approved by the Internal Review Committees to protect human subjects at the University of Rochester Medical Center (URMC, Rochester, NY), and the Rochester Institute of Technology (RIT, Rochester, NY). The study included synchronized measurements of 5 minutes and 30 seconds of ECG, PPG, VPG 180Hz (High-quality camera), VPG 30 Hz (low quality webcam), taken before and after cardioversion of AF subjects receiving treatment at the AF Clinic of URMC. These data are subjected to BTC, SVM, and CNN classifiers to detect AF and compare the result for each classifier depending on the signal type. We propose a deep learning approach that is applicable to different kinds of cardiac signals to detect AF in a similar manner. By building this technique for different sensors we aim to provide a framework to implement a technique that can be used for most devices, such as, phones, tablets, PCs, ECG devices, and wearable PPG sensors. This conversion of the different sensing platforms provides a single AF detection classifier that can support a complete monitoring cycle that is referring to screen the patient whether at a hospital or home. By using that, the risk factor of heart attack, stroke, or other kind of heart complications can be reduced to a low level to prevent major dangers, since increasing monitoring AF patients helps to predict the disease at an early stage as well as track its progress. We show that the proposed approach provides around 99% accuracy for each type of classifier on the test dataset, thereby helping generalize AF detection by simplifying implementation using a sensor-agnostic deep learning model

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque Government through the grant PRE_2018_2_0260

An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification

The aberration in human electrocardiogram (ECG) affects cardiovascular events that may lead to arrhythmias. Many automation systems for ECG classification exist, but the ambiguity to wisely employ the in-built feature extraction or expert based manual feature extraction before classification still needs recognition. The proposed work compares and presents the enactment of using machine learning and deep learning classification on time series sequences. The two classifiers, namely the Support Vector Machine (SVM) and the Bi-directional Long Short-Term Memory (BiLSTM) network, are separately trained by direct ECG samples and extracted feature vectors using multiresolution analysis of Maximal Overlap Discrete Wavelet Transform (MODWT). Single beat segmentation with R-peaks and QRS detection is also involved with 6 morphological and 12 statistical feature extraction. The two benchmark datasets, multi-class, and binary class, are acquired from the PhysioNet database. For the binary dataset, BiLSTM with direct samples and with feature extraction gives 58.1% and 80.7% testing accuracy, respectively, whereas SVM outperforms with 99.88% accuracy. For the multi-class dataset, BiLSTM classification accuracy with the direct sample and the extracted feature is 49.6% and 95.4%, whereas SVM shows 99.44%. The efficient statistical workout depicts that the extracted feature-based selection of data can deliver distinguished outcomes compared with raw ECG data or in-built automatic feature extraction. The machine learning classifiers like SVM with knowledge-based feature extraction can equally or better perform than Bi-LSTM network for certain datasets

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-network (DQN) algorithm was designed, using conventional feature engineering and deep convolutional neural network (CNN) methods, to extract the optimal features. The DQN yielded the optimal features: two CNN features from ECG and two conventional features from EEG. The ECG features were more significant for tracking the transitions within the alertness continuum with the DQN. The classification was performed with a small number of features, and the results were similar to those from using all of the features. This suggests that the DQN could be applied to investigating biomarkers for physiological responses and optimizing the classification system to reduce the input resources

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Rate-Distortion Classification for Self-Tuning IoT Networks

Many future wireless sensor networks and the Internet of Things are expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the signal statistics. New protocols will be then injected as a software as certain events occur. For instance, new data compressors could be (re)programmed on-the-fly as the monitored signal type or its statistical properties change. We consider a lossy compression scenario, where the application tolerates some distortion of the gathered signal in return for improved energy efficiency. To reap the full benefits of this paradigm, we discuss an automatic sensor profiling approach where the signal class, and in particular the corresponding rate-distortion curve, is automatically assessed using machine learning tools (namely, support vector machines and neural networks). We show that this curve can be reliably estimated on-the-fly through the computation of a small number (from ten to twenty) of statistical features on time windows of a few hundreds samples

Abnormality Detection in ECG Signal applying Poincare and Entropy-based Approaches

Detection of abnormality in heart is of major importance for early and appropriate clinical medication. In this work, we have proposed two models for detection of abnormality in ECG signals. The normal ECG signals are closely repetitive in nature to a large extent, whereas ECG signals with abnormalities tend to differ from cycle to cycle. Hence, repetitive plot like the Poincare is efficient to detect such non-repetitiveness of the signal; thereby, indicating abnormalities. Hence, we have used Poincare plot to develop the two proposed models. One of the models uses direct analysis of the binary image of the plot to detect the difference in retracing, between the healthy and unhealthy samples. The other model uses entropy of the Poincare plot to detect the difference in randomness of plots between the two classes. Most importantly, we have used only lead II ECG signal for analysis. This ensures ease of computation as it uses signal of only a single lead instead of the 12 leads of the complete ECG signal. We have validated the proposed models using ECG signals from the ‘ptb database’. We have observed that the entropy analysis of the Poincare plots gives the best results with 90% accuracy of abnormality detection. This high accuracy of classification, combined with less computational burden enables its practical implementation for the development of a real life abnormality detection schem