Screening vascular access dysfunction in hemodialysis requires tools that are objective and efficient. Listening for bruits during a physical exam is a subjective examination that can detect stenosis also called vascular narrowing when properly performed. Phonoangiograms (PAGs) which is a mathematical analysis of bruits increase the objectivity and sensitivity and permit quantification of stenosis. In this paper, we proposed Vision Transformer (ViT) for PAGs to automatically classify vascular stenosis. In particular, we added an auxiliary waveform to improve the classification performance. Moreover, we used the method of inter-patient verification to verify the performance of the proposed method. The experimental results show that the total F1 score, recall, and precision of the proposed method are 0.984, 1.000, and 0.969 respectively. We believe the method proposed in this paper has the potential to provide a reference for subsequent research.

The detection of consecutive cardiac cycles plays an important role in the daily monitoring of cardiovascular diseases. The Seismocardiogram (SCG) signal measures the cardiac-induced vibrations and is suitable for continuously tracking since it can be measured in a non-invasive fashion. In this paper, a beat-to-beat heart rate detection method based on low-frequency SCG signal using BiLSTM network is proposed. The regression model to predict Electrocardiogram (ECG) from SCG was trained on CEBS dataset and a 5-fold cross-validation method was adopted. The RMSE of the predicted ECG signal and ECG signal was 0.0367. The sensitivity and precision achieved 0.97 and 0.98 separately and the Spearman Correlation was 0.98. This work shows promise for the proposed SCG based methodology to be used as an alternative to ECG for continuous beat-to-beat heart rate monitoring.

Electrocardiogram (ECG), as an important method for diagnosing cardiovascular diseases, can record the heart activity over a period of time. However, most of the current studies on ECG classification focus on the single scale information and ignore the complementary information between different scales. Therefore, this paper proposed an end-to-end multi-scale fusion convolutional neural network (CNN) for heartbeat classification. In this method, multiple convolution kernels of different reception domains are used to extract unique features of different scales, and the extracted multiple scale features are fused, which could effectively capture disease patterns and suppress noise interference. At the same time, attention module is used to select features to improve model performance. Improve efficiency with residual module. Finally, we obtained 34, 983 heartbeats from the Physikalisch-Technische Bundesanstalt (PTB) dataset to validate the model performance. The overall Fl-score is 99. 69%, and the Fl-score of each single class is more than 99. 35%, which is better than the existing algorithms. It can be described as a reference for future research.

Wearable electrocardiogram (ECG) monitoring systems were effective ways to diagnose intermittent cardio diseases. However, the Electrode Motion Artifact (EMA) and the Muscle Artifact (MA) destroy the incipient shape of ECG signals, decrease the accuracy of diagnostic results. Here, we proposed a novel method for detecting destroyed segments of ECG signals. The method was based on a deep learning network, and its performance was evaluated on a synthetic dataset of the MIT-BIH arrhythmia database. Its practicability was tested with three R-peak detection algorithms. By removing the destroyed segments in ECG signals, the sensitive and positive prediction of these R-peak detection algorithms were promoted significantly.