ECG noise reduction technique using antlion (ALO) algorithm for heart rate monitoring devices - Hii, Kiew Hui (2020)
There are more 6 billion people alive today, and the number is expected to reach until 9 billion in coming 30-40 years. As of the declining of the birth rate and booming aging population, it is suspected that number of elderly people will be more than young children in human history. Wearable technology may provide the solution for providing health care to the growth of the aging population. Based on World Health Organization (2016) report, it has been found out that Cardiovascular Diseases (CVD) is the world’s leading cause of death. Wearable technology solutions could ease the burden on healthcare personal and hospital space more emergent or responsive care at the same time. Since the electrocardiogram (ECG) noise is in the low frequency, it is very easy interrupt with the noise; especially noise from electrical and the intensity of physiological. It will lead to the problem on interruption the morphology and it will make doctors hard to identify what is the disease are. As a result, this is very important to obtain the clean and clear on the morphology of ECG. This is because the morphology of ECG supports in analyzing many information of the heart disorder. This thesis aims to research into an effective way to capture human critical physiological parameters - heart rate. One of the objectives of the research is to identify the morphology of electrocardiogram signal by using the median filter to reduce the high frequency noise for the ECG database. Second objective is to identify and optimize the cutoff frequency using the Antlion optimization (ALO) for FIR filter. The signal processing techniques - Antlion Optimizer (ALO) can help on finding the cutoff frequency automatically. By finding out the cutoff frequency, it will bring to the cancelling noise stage. The cutoff frequency is applied to a Finite Impulse filter (FIR) for getting an original and clean ECG signal. From the simulations, the optimized cutoff frequency is retrieved in higher than the conventional method’s signal to ratio (SNR). The proposed method’s cutoff frequency performance was with the conventional cutoff frequency of feature extraction of the signal. The ALO optimum cutoff frequencies’ result shows that the proposed method reduced more noise than the conventional method.