# Lightweight Ensemble Network for Detecting Heart Disease Using ECG Signals
## 4 Results and Discussion
The digital filter was designed with general settings. Sometimes, the input data require a different filter band. For example, if another ECG study is performed using a different ECG database, the settings of the Butterworth filter used in this study must be changed. Alternatively, a filter other than the Butterworth filter may need to be used. Figure 14 presents ECG signals to which the Fourier transform was applied. The two ECG signals represent the results of the fast Fourier transform. They exhibit different frequency characteristics. Considering these frequency characteristics, the proposed algorithm should design an appropriate filter regardless of which ECG signal is input. The plan currently under consideration involves calculating the signal-to-noise ratio (SNR) of the signal and redesigning the filter when the SNR is too low. Figure 12: Results of K-fold validation for evaluating the classification performance of the algorithm. When the K value is equal to \(N\), this algorithm performs the verification \(N\) times and calculates the average value for all the results. A K value of 10 yielded the best results. If the K value exceeds 10, the accuracy gradually decreases. The ECG signals used in this study were obtained from the MIT-BIH database. Digital filters were designed and applied to increase the classification accuracy. Subsequently, the learning data were sufficiently secured using the matching pursuit algorithm. Data classification was performed using the MobileNetV2-BiLSTM algorithm. The dataset used consisted of four classes, and the number of data was unbalanced. To solve this problem, the matching pursuit algorithm was used to analyze signals, adding 500 pieces of data from all classes. In this study, the proposed data augmentation method was used only to detect heart disease. However, if research on data aggregation progresses, we will be able to solve problems arising from unbalanced datasets. In addition, because the proposed method can arbitrarily add data measured in a short period of time, e.g., ECG signals, sufficient learning data can be secured, which can increase the algorithm accuracy. The advantage of this algorithm is that arrhythmia diagnosis using a mobile device is possible. Deep learning, which has recently been used in various ways, has problems, such as the classification accuracy, the classification accuracy  
as the use of high-performance CPUs and the consumption of large amounts of memory and power, because it focuses on performance. However, MobileNet is an algorithm designed for use in situations where the computational performance is limited; thus, it can solve the aforementioned problems. The accuracy of the proposed model was 91.7%. Considering that the accuracy of the existing arrhythmia detection algorithm has reached approximately 99%, the performance of the proposed algorithm was not excellent. In addition to the accuracy, the sensitivity, specificity, and precision were poor compared with previous studies. However, the objective of the proposed method is to allow heart patients to check their health using mobile devices in their daily lives. To overcome the disadvantages of this study, further research on the weight reduction of the model, the learning method of deep learning, and noise reduction is needed.