心電図のAI解析

転移学習

  1. Classify Time Series Using Wavelet Analysis and Deep Learning　This example shows how to classify human electrocardiogram (ECG) signals using the continuous wavelet transform (CWT) and a deep convolutional neural network (CNN). Training a deep CNN from scratch is computationally expensive and requires a large amount of training data. In various applications, a sufficient amount of training data is not available, and synthesizing new realistic training examples is not feasible. In these cases, leveraging existing neural networks that have been trained on large data sets for conceptually similar tasks is desirable. This leveraging of existing neural networks is called transfer learning. In this example we adapt two deep CNNs, GoogLeNet and SqueezeNet, pretrained for image recognition to classify ECG waveforms based on a time-frequency representation. GoogLeNet and SqueezeNet are deep CNNs originally designed to classify images in 1000 categories. We reuse the network architecture of the CNN to classify ECG signals based on images from the CWT of the time series data. The data used in this example are publicly available from PhysioNet.　https://www.mathworks.com/help/wavelet/ug/classify-time-series-using-wavelet-analysis-and-deep-learning.html

医療データ解析

  1. Proceedings of Machine Learning Research 252:1–31, 2024 Machine Learning for Healthcare MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis　https://arxiv.org/pdf/2408.07773

データ

  1. medical-data-analysis Here are 20 public repositories matching this topic…　https://github.com/topics/medical-data-analysis

ECG解析

心電図から異常を読み取って、心血管イベントの予測に活かすということは循環器内科医が実現したいことだと思います。そのような解析において、機械学習やAIがどう役立つでしょうか。

  1. Working with ECG — Heart Rate data, on Python Bartek Kulas Bartek Kulas Follow 9 min read · Feb 8, 2023　https://bartek-kulas.medium.com/working-with-ecg-heart-rate-data-on-python-7a45fa880d48
  2. How to analyze an ECG with Python Alejandro Ena Alejandro Ena Follow 4 min read · Nov 16, 2022 https://medium.com/@lalesena/how-to-analyze-ecgs-with-python-396e34ece937
  3. ECGxAI: Explainable AI for the electrocardiogram　https://github.com/UMCUtrecht-ECGxAI/ecgxai
  4. Computers in Biology and Medicine Volume 170, March 2024, 107908 Computers in Biology and Medicine Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review　https://www.sciencedirect.com/science/article/pii/S0010482523013732
  5. ECG data classification and explainability with machine learning and deep learning algorithms Jaya Ojha　Thesis submitted for the degree of Master in Applied Computer and Information Technology – ACIT (Data Science) 60 credits Department of Computer Science Faculty of Technology, Art and Design Oslo Metropolitan University — OsloMet Spring 2024　https://oda.oslomet.no/oda-xmlui/bitstream/handle/11250/3162970/no.oslomet%3Ainspera%3A232817044%3A126581247.pdf

異常の検出・予後予測

  1. Anomaly detection in healthcare data with Darts　https://unit8.com/resources/anomaly-detection-in-healthcare-data-with-darts/
  2. Ensemble Post-hoc Explainable AI in Multivariate Time Series: Identifying Medical Features Driving Disease Prediction Jacqueline Michelle Metsch, Philip Hempel, Miriam Cindy Maurer, Nicolai Spicher, Anne-Christin Hauschild doi: https://doi.org/10.1101/2025.02.14.638219　Posted February 18, 2025.

データクリーニング

  1. EEG および ECG 信号クリーニングの概要 医療信号処理は人間の健康を理解する鍵です。EEGとECG信号は脳と心臓の機能に関する深い洞察を与えてくれます。しかし、これらの信号は有用な情報を明らかにするためにはクリーニングが必要です。https://editverse.com/ja/clean-filter-transform-complete-python-workflow-for-medical-signal-processing/

論文

  1. Published: 06 April 2025 Analyzing the performance of biomedical time-series segmentation with electrophysiology data Richard Redina, Jakub Hejc, Marina Filipenska & Zdenek Starek Scientific Reports volume 15, Article number: 11776 (2025)　https://www.nature.com/articles/s41598-025-90533-y

時系列データ解析

  1. Platform for Analysis and Labeling of Medical Time Series　Sensors 2020, 20(24), 7302; https://doi.org/10.3390/s20247302