Kalidas Vignesh, Tamil Lakshman S
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:337-340. doi: 10.1109/EMBC44109.2020.9176054.
In this paper, we propose a technique for detection of premature ventricular complexes (PVC) based on information obtained from single-lead electrocardiogram (ECG) signals. A combination of semisupervised autoencoders and Random Forests models are used for feature extraction and PVC detection. The ECG signal is first denoised using Stationary Wavelet Transforms and denoising convolutional autoencoders. Following this, PVC classification is performed. Individual ECG beat segments along with features derived from three consecutive beats are used to train a hybrid autoencoder network to learn class-specific beat encodings. These encodings, along with the beat-triplet features, are then input to a Random Forests classifier for final PVC classification. Results: The performance of our algorithm was evaluated on ECG records in the MIT-BIH Arrhythmia Database (MITDB) and the St. Petersburg INCART Database (INCARTDB). Our algorithm achieves a sensitivity of 92.67% and a PPV of 95.58% on the MITDB database. Similarly, a sensitivity of 88.08% and a PPV of 94.76% are achieved on the INCARTDB database.
在本文中，我们提出了一种基于从单导联心电图（ECG）信号中获取的信息来检测室性早搏（PVC）的技术。半监督自动编码器和随机森林模型相结合用于特征提取和PVC检测。首先使用平稳小波变换和去噪卷积自动编码器对ECG信号进行去噪。在此之后，进行PVC分类。将单个ECG心搏段以及从三个连续心搏中提取的特征用于训练混合自动编码器网络，以学习特定类别的心搏编码。然后，将这些编码与心搏三联征特征一起输入到随机森林分类器中进行最终的PVC分类。结果：我们的算法在麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库（MITDB）和圣彼得堡INCART数据库（INCARTDB）中的ECG记录上进行了评估。我们的算法在MITDB数据库上实现了92.67%的灵敏度和95.58%的阳性预测值。同样，在INCARTDB数据库上实现了88.08%的灵敏度和94.76%的阳性预测值。
