Abstract
Electrocardiogram (ECG) signals have been used to monitor and diagnose signs of cardiovascular disease and abnormal signals about the human body. ECG signals are typically characterized by the PR, QRS, QT interval, ST-segment, and heart rate (HR) parameters. ECG devices are widely used for many applications, especially for the elderly. However, ECG signals are often affected by noises from the environment. There are mainly two types of noises that affect the ECG signals: low frequencies from muscle activity and 50/60 Hz from the electrical grid. Removing these noises is important for improving the quality of the ECG signal. A clear ECG signal makes it easy to diagnose cardiovascular problems. ECG signals with high sampling frequency are more accurate. However, the noises in the signal will be more obvious and it will be difficult to remove these noises with filters. We analyzed the symmetrical correlation between the sampling frequency of the signal and the parameters of the signal such as signal to noise ratio (SNR) and signal amplitude. This study will compare characterization of ECG signals performed at different sampling frequencies before and after applying infinite impulse response (IIR) and symmetric finite impulse response (FIR) filters. Therefore, it is critical that the sampling frequency is consistent at the same frequency of the ECG signal for accurate diagnosis. Furthermore, the approach can be also important for the device to help reduce the device’s computing power and hardware resources. Our results were tested with the MIT/ BIH database at 360 Hz sampling frequency with 11-bit resolution. We also experimented with the device operating in real-time with a sampling frequency from 100 Hz to 2133 Hz and a 24-bit resolution. The test results show the advantages of the symmetric FIR filter over IIR when applied to the filtering of ECG signals. The study’s conclusions can be applied to real-world devices to improve the quality of ECG signals.
Highlights
According to WHO statistics, the number of people with cardiovascular problems is increasing, especially among the elderly [1]
We proposed an ECG signal acquisition model based on large production components to collect real-time signals
We focused on complementing the new knowledge and results related to ECG signal acquisition and analysis
Summary
According to WHO statistics, the number of people with cardiovascular problems is increasing, especially among the elderly [1]. One of the critical applications is in health monitoring and diagnosis [9,10]. It provides one of the essential pieces of information supporting the diagnosis of stroke. Electrocardiogram monitoring helps in the early detection and identification of the cause of stroke [11]. The quality of the ECG signal plays a direct role in the diagnostic outcome [12]. The components of the ECG signal include the Q, R, S, and T wave [13], where the R peaks play an essential role in calculating the patient’s heart rate [9]. Distinguishing the components of an ECG signal will yield more helpful information [14]
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