Browsing by Subject "ECG"
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Item An Application Of Mutual Information For Electrocradiogram Feature Selection(2016-05) Eisele III, ValCorrect classification of arrhythmias by implantable cardio-defibrillators (ICDs) and automatic external defibrillators (AEDs) depend on the quality of the features used during classification. Mutual Information provides a means of quantifying and assessing the quality of cardiac related features. This paper proposes a novel algorithm called Weighted Mutual Information or WMI based on mutual information as a method for selecting cardiac related features for classification. To evaluate the algorithm, a comparative analysis was conducted against the Principal Component Analysis (PCA) algorithm using two separate feature sets extracted from the MIT-BIH database: QRS based features and morphology based amplitude values (ECG amplitude values). Two k Nearest Neighbor (k-NN) clustering algorithms were trained using features extracted using WMI and PCA to classify four event types: Normal, Ventricular Ectopic, Atrial Ectopic, and Fusion events. The k-NNs trained using WMI produced a lower classification error when compared to the k-NNs trained using PCA with statistical significance (0.01 > p). Lastly, the k-NN classifier trained on QRS based features outperformed the k-NN classifier trained only on morphology based features with a measurable significant difference.Item Explaining Predictive Artificial Intelligence Models for ECG using Shallow and Generative Models(2020-05) Attia, Zachi ItzahkOpening the lid on the “black box” of artificial intelligence (AI) models including deep neural networks is important for the adoption of this technology in clinical medicine. Given the high stakes, potential for novel or unexpected recommendations, the risk of implicit bias, and the potential legal liability, clinicians may be hesitant to respond to medical diagnoses or therapies suggested by neural networks without the presence of a general understanding of the specific features or characteristics they process to derive their recommendations. Furthermore, the ability to explain predictive AI models may also enhance the ability to improve their performance and to predict appropriate use cases for their adoption. Deep learning methods and convolutional neural networks in specific, achieved state of the art performance in numerous fields and reached human like accuracy in image detection and classification. In some areas, deep learning models superseded human expert capabilities, for example, by detecting asymptomatic left ventricular dysfunction from ECG, by detecting age, sex and cardiovascular risk from fundus photography, and by beating the world champion in Go. Convolutional neural networks use convolutional operations together with non-linear transformations to create feature maps based on the specific outcome the network trained to optimize. While the training of a model as a whole is considered supervised since network weights are optimized with respect to human defined labels, the extraction of the features from a signal is unsupervised, and the features used by a network and their meaning remain unknown (hence, referred to as a “black box”). In traditional computer vision and signal processing, features are engineered based on human knowledge and human observations and later hard coded as a separate step prior to input into a classification model, the human feature are meaningful and in the case of the electrocardiogram (ECG), these features are based on known biological mechanisms. In our work we sought to identify the meaning in convolutional neural network feature maps that were trained on the ECG signal and compare network features to the understandable, human-selected features. Using our proposed methods, which are generalizable, we developed tools to explain AI models. To test, validate, and demonstrate use of this tool, we employ a previously developed AI model that can detect patients age and sex using a surface electrocardiogram (ECG). For any domain with meaningful features, we show that the neural network selects features that are similar to those selected by a human expert, and that neural network “black box” features are in fact a linear combination of human identifiable features. As the network features were created without any human knowledge, this raises the possibility that artificial intelligence models develop a "sense" of the signal it processes in a similar manner to how a human expert does. Thus, artificial intelligence may be truly intelligent; and this work may open the door for creating explainability in artificial intelligence models.Item The Lack of Evidence to Support Electrocardiography as a Routine Screening Test for Coronary Heart Disease(2010-09-15) Moen, SteffanyThe U.S. Preventive Services Task Force recommends not using electrocardiography (ECG) as a test to screen for blockage in coronary arteries, the arteries that supply blood to the heart. It also recommends against using an ECG to predict a person’s risk for coronary heart disease.Item Low voltage / low power rail-to-rail CMOS operational amplifier for portable ECG(2013-08) Lee, BoramOne of the most important building blocks in modern IC design is the operational amplifier. For the portable electrocardiogram (ECG), the operational amplifier is employed to sense and amplify the electrical signal of heartbeat of human body. For the battery powered portable ECG system, low supply voltage environments are required to reduce power consumption and the result is a reduced input common mode range (ICMR) of the op-amp. To overcome the reduced ICMR problem, complementary differential pairs operated in parallel are commonly used to achieve a rail-to-rail input common mode range. However, this complementary differential input pair structure can have a substantial transconductance (gm) variation problem and a dead zone problem in a low supply voltage environment and an extremely low supply voltage environment respectively. In the past years, a number of techniques have been proposed to overcome those problems for low- and extremely low-supply voltage environments. This dissertation is focused on an op-amp applicable to a portable ECG system and in total five novel rail-to-rail constant gm op-amps usful for circuits such as a portable ECG are proposed. Three of those op-amps work in the low supply voltage environment and two op-amps are proposed for the extremely low supply voltage environment. Cadence SPECTRE simulation and TSMC 0.25-µm CMOS technology are used to simulate and lay out these works.