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Cardiovascular Disease Prediction using Single-Lead ECG Data
Chaeyoon Park, Gihun Joo, Suhwan Ji, Junbeom Park, Junho Baek, Hyeonseung Im
http://doi.org/10.5626/JOK.2024.51.10.928
The most representative approach to diagnosing cardiovascular disease is to analyze electrocardiogram (ECG), and most ECG data measured in hospitals consist of 12 leads. However, wearable healthcare devices usually measure only single-lead ECG, which has limitations in diagnosing cardiovascular disease. Therefore, in this paper, we conducted a study to predict common cardiovascular diseases such as atrial fibrillation (AF), left bundle branch block (LBBB), and right bundle branch block (RBBB) using a single lead that could be measured with a wearable healthcare device. For experiments, we used a convolutional neural network model and measured its performance using various leads in terms of AUC and F1-score. For AF, LBBB, and RBBB, average AUC values were 0.966, 0.971, and 0.965, respectively, and average F1-scores were 0.867, 0.816, and 0.848, respectively. These experimental results confirm the possibility of diagnosing cardiovascular disease using only a single lead ECG that can be obtained with wearable healthcare devices.
Implementing Structural Operational Semantics in Python
http://doi.org/10.5626/JOK.2018.45.11.1176
Operational semantics is the most commonly used technique to formally define the semantics of a programming language. It defines the meaning of a program in terms of how it is executed or interpreted as a sequence of computational steps. This paper introduces an implementation technique for small-step structural operational semantics for a simple ML-style functional language using visitor patterns and exception handling in Python. The secondary objective of this paper is to explain the core concepts of programming language theory and the techniques for implementing these concepts using Python, instead of traditional functional languages such as ML, Haskell, and Scheme. Since Python has a wide abundant user base due to its rich library and flexibility, it is more suitable to explain operational semantics for common users than functional languages, which are relatively less known and have a high learning curve.
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