Tag: lstm autoencoder

  • Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks

    Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks

    This study aims to implement and evaluate the K-means algorithm for ranking/clustering S&P 500 stocks based on average annualized return and volatility. The second goal is to detect anomalies in the best performing S&P 500 stocks using the Isolation Forest algorithm. Additionally, anomalies in the S&P 500 historical stock price time series data will be…

  • AI-Based ECG Recognition – EOY ’22 Status

    AI-Based ECG Recognition – EOY ’22 Status

    Featured Photo by cottonbro studio on pexels. Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. The recent study demonstrates that an AI is capable of automatically diagnosing the abnormalities indicated by an ECG. In this post we will review and illustrate how AI applies to ECG analysis to outperform traditional ECG analysis.…

  • DL-Assisted ECG/EKG Anomaly Detection using LSTM Autoencoder

    DL-Assisted ECG/EKG Anomaly Detection using LSTM Autoencoder

    This project implements an ECG anomaly detection framework using an LSTM Autoencoder to accurately identify abnormal ECG events. It trains the autoencoder on normal rhythms, using reconstruction errors to identify anomalies. The proposed method aims to improve abnormal ECG detection, as demonstrated by test results on the ECG5000 dataset, providing valuable information for patient health…