# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
###### Abstract  
Because a cardiac function signal cannot reflect cardiac health in all directions, we propose a classification method using ECG and PCG signals based on BiLSTM-GoogLeNet-DS. The electrocardiogram (ECG) and phonocardiogram (PCG) signals used as research objects were collected synchronously. Firstly, the obtained ECG and PCG signals were filtered, and then the ECG and PCG signals were fused and classified by using a bi-directional long short-term memory network (BiLSTM). After that, the time-frequency processing was performed on the filtered ECG and PCG signals to obtain the time-frequency diagram of each signal; the one-dimensional signal was changed into a two-dimensional image signal, and the size of each image signal was adjusted to input the improved GoogLeNet network for classification. Then we obtained the two-channel classification results. The three-channel classification results, combined with the classification results of the above BiLSTM network, were finally obtained. The classification results of these three channels were used to make a decision via the fusion strategy of the improved D-S theory. Finally, we obtained the classification results. Taking 70% of the signals in the database as training data and 30% as test data, the obtained classification accuracy was 96.13%, the sensitivity was 98.48%, the specificity was 90.8%, and the F1 score was 97.24%. From the experimental results, the method proposed in this paper obtained high classification accuracy, and the classification effect was better than a cardiac function signal, which makes up for the low accuracy of the cardiac function signal for judging cardiac disease. ECG and PCG signals; bi-directional long short-term memory network (BiLSTM); GoogLeNet; D-S theory +
Footnote †: journal: Medical Imaging  
can be used in clinical practice, but this method is traumatic and risky to the patient, and requires the hospital to have high technical equipment. Nowadays, most researchers carry out signal analyses and algorithm development for cardiac function signals, such as an ECG or PCG signal, while there are few related studies based on ECG and PCG signals. Although researchers have fused PCG, ECG, and cardiac impedance signals, most of them treat one kind of signal as the reference of another kind of signal. For example, a cardiac impedance signal is processed with the help of an ECG signal, the PCG signal extracts the feature points with the help of the ECG signal, or simply fuses the ECG and PCG signals to give the heart disease diagnosis result by only a cardiac function signal. For example, [PERSON] et al. determined cardiac hemodynamic parameters by using the combined features of ECG, PCG, and cardiac impedance signals. The relevant parameters of cardiac impedance were obtained with the help of ECG and PCG signals [2]. [PERSON] et al. compared heart rate monitoring results by synchronizing the acquisition of the ballistic cardiography, electrocardiogram, and phonocardiograms signals [3]. [PERSON] et al. improved heart disease diagnosis accuracy by acquiring ECG and PCG signals synchronously [4]. [PERSON] detected coronary artery disease by a dual-input neural network using electrocardiogram and phonocardiogram signals [5]. [PERSON] et al. synchronously acquired ECG and cardiac impedance signals with the least electrodes. The parameter calculation of a cardiac function signal was completed by means of an ECG signal [6]. [PERSON] et al. used different classifiers to classify the signals by the synchronously collected PCG and ECG signals and compared the classification results with the PCG signals [7]. [PERSON] proposed the detection method of coronary artery disease, which made use of multi-modal features. The multi-modal feature models were proposed using ECG and PCG signals. Finally, the classification accuracies of these modes were compared [8]. Through the above-mentioned research, it can be found that people have gradually shifted the focus from cardiac function signals to more model cardiac function signals.