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CLASSIFYING HEARTBEATS USING MACHINE LEARNING team

CalCardiac: Classifying Heartbeats Using Machine Learning

December 27, 2018 by

CalCardiac worked to reduce false hospital alarms due to arrhythmias. Collaborating with the UCSF School of Nursing, the team used various machine learning techniques to create a browser-based utility to view and label ECG waveform images.

Capstone Team: Alex Ackroyd, Adam Andrews, Siddhant Issar, Segev Malool, Umesh Thillaivasan, Yuntao Wang.
Research in collaboration with Jacob Abba and Ran Xiao at UCSF; Advised by Xiao Hu, UCSF, and Gabriel Gomes, UC Berkeley.

Challenge

CLASSIFYING HEARTBEATS
When the heart beats irregularly, it is known as an arrhythmia. A common heart arrhythmia, known as premature ventricular contraction (PVC), ac-counts for the highest number of non-actionable and false-positive in-hospital patient monitoring alarms. Current in-hospital patient monitoring systems do not have the capabilities to discern true-PVC alarms from false-alarms, and therefore medical professionals experience alarm fatigue, a desensitization to alarms leading to lower quality of care. This presents an opportunity to apply sophisticated machine learning methods to improve the accuracy of these alarms.

Actions & Results

MACHINE LEARNING
Our team explored several machine learning approaches to handle and classify electrocardiogram (ECG) signal data from two data sets: the famous MIT-BIH Arrhythmia labeled data set, and UC San Francisco’s massive unlabeled data set. The MIT-BIH Arrhythmia Database has 48 records of half-hour ECG signals. UCSF’s data set has over 10 million records each 10-seconds long.

LOGISTIC REGRESSION, RANDOM FOREST, AND NEURAL NETWORKS
We sliced the MIT-BIH data set into 0.8-seconds ECG signal strips, 0.4-seconds before and 0.4-seconds after each annotated beat. Then, using a data set of 7129 PVC alarms, and 7129 non-PVC records derived from the full MIT-BIH sliced data set, Logistic Regression, Random Forest, and a Neural Network was trained.

TSNE, K-MEANS CLUSTERING, AND TRANSFER LEARNING
We also explored techniques to distinguish types of heartbeats. T-distributed stochastic neighbor embedding (TSNE) at-tempts to preserve high dimensional neighborhoods using a probabilistic interpretation of transformed data (Left). Anomaly detection with K-Means clustering algorithms can be used for QRS complex detection in time series ECG signal data (Center). Transfer Learning using one of Google’s pre-trained models can lead to improved heart beat classification (Right).

*CalCardiac was the 2017 winner of the Fung Institute Alumni Award for the Most Innovative Exposition. This award honors a team that presents their project in the most creative way to a general audience.

Classifying_Heartbeats_using_Machine_Learning_ Project Brief

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