Electrocardiogram (ECG) signal is a common diagnostic basis for heart disease. Reliable waveform detection method for ECG has important applications in clinical diagnosis. This work proposes a new method for P- and T- wave detection based on auxiliary waveform. The algorithm includes three parts: preprocessing, construction of auxiliary waveforms and P- and T-wave position detection. First, the ECG signal is preprocessed to remove baseline drift and high-frequency noise. Then, the auxiliary waveforms is constructed in the P- and T-wave searching windows which are defined based on the QRS wave position. Next, on the basis of the prominence of the auxiliary waveform, each lead of the 12-lead ECG signal is weighted and the final auxiliary waveform is constructed. Finally the P/T-wave positions are detected according to the auxiliary waveform memory. The performance of the proposed algorithm has been evaluated on a 12-lead manually annotated database. Standard deviations of 26.55 and 30.92 ms were obtained for the onset and offset of T-wave respectively, the standard deviation of the onset and offset of P-wave is 13.25 and 15.7 ms. The algorithm we proposed can provide reference for following research.

Atrial fibrillation and atrial flutter are two more common rapid atrial arrhythmias, they are independent of each other and can transform each other. Because the clinical characteristics of the two diseases are similar, it is easy to cause misdiagnosis. Therefore, effective diagnosis of distinguishing atrial fibrillation and atrial flutter can reduce the damage suffered by patients during treatment. In this paper, we propose a kind of based on residual network and multi-scale feature fusion structure of neural network to automatically classify atrial fibrillation, atrial flutter and other ECG signals. The ECG signals used are derived from clinical data labeled by expert diagnosis. In addition, we used several popular classification methods proposed in the past for comparative experiments to verify the classification performance of our models. The experimental results show that the total F1, recall and precision of the proposed algorithm are 0.923, 0.916 and 0.93. The algorithm we propose has the potential to provide reference for subsequent research.

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.

Atrial fibrillation is a serious cardiovascular disease. It is the main cause of heart disease such as myocardial infarction. ECG based atrial fibrillation detection is very important for clinical diagnosis. In this paper, a method based on one-dimensional CNN and time domain features of ECG sequence is proposed to detect atrial fibrillation. The ECG data used came from the MIT-BIH atrial fibrillation database. The first step is to filter out the noise interference in ECG. In the second step, ECG signals were segmented into seven heart beats. In the third step, 8 features are extracted based on the time domain features of ECG sequence to form the feature vector (size 1*8). In the fourth step, the one-hot label (1*2) output by the convolutional neural network was combined with the extracted time domain features (size 1*8) to obtain a total of 10 dimensional features. In the fifth step, the extracted 10-dimensional features are normalized and then put into the SVM classifier. The experimental results show that the sensitivity, specificity and total accuracy of the proposed algorithm are 99.07%, 97.05% and 98.03%, respectively. This algorithm has great potential to help doctors and reduce mortality.

With the popularization of computer technology and the development of signal processing technology, the computer automatic diagnosis of ECG has entered the practical stage. In this paper, a multi-functional ECG analysis software system is designed and implemented, which can read and analyze ECG signals and draw ECG waveforms, calculate ECG parameters such as heart rate (HR), P wave time and RR interval, and draw corresponding diagnostic conclusions. C# was used as programming language in Windows and WPF framework for software development. Software design based on model-View-Controller (MVC) framework; Access ECG data to disk through file IO operation; the ECG data of 40 patients were processed and analyzed, and the results were compared with doctors' diagnosis results. After testing, the software system can run stably on PC. The accuracy of the calculation of the relevant parameters of 40 sets of data is above 95%, and the accuracy of the interpretation of normal ECG is above 98%. The accuracy rate of arrhythmia detection is over 90%. This ECG analysis software system has certain clinical application value.