Summary of Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: a Comprehensive Review, by Abhijith S et al.
Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: A Comprehensive Review
by Abhijith S, Arjun Rajesh, Mansi Manoj, Sandra Davis Kollannur, Sujitta R V, Jerrin Thomas Panachakel
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This review explores advancements in myocardial infarction (MI) classification methodologies for wearable devices, emphasizing their potential in real-time monitoring and early diagnosis. The paper critically examines traditional approaches like morphological filtering and wavelet decomposition alongside cutting-edge techniques such as Convolutional Neural Networks (CNNs) and VLSI-based methods. By synthesizing findings on machine learning, deep learning, and hardware innovations, the review highlights strengths, limitations, and future prospects. The integration of these techniques into wearable devices offers promising avenues for efficient, accurate, and energy-aware MI detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to detect heart attacks (myocardial infarctions) earlier using wearable devices like smartwatches. It compares old methods with new ones like special computer chips (VLSI-based) and super-powerful computers (Convolutional Neural Networks). The review shows what works well, what doesn’t, and what’s coming next. By putting all these ideas together, the paper shows how wearable devices can be used to quickly and accurately detect heart attacks. |
Keywords
* Artificial intelligence * Classification * Deep learning * Machine learning
