<![CDATA[Monitoring ECG signals using a Holter device is a common method for diagnosing heart conditions over the long term. However, one of the main challenges in using this device is motion artifacts caused by patient movement during monitoring. These artifacts often affect the quality of the ECG signal recordings, reduce the accuracy of heart condition detection, and hinder the diagnostic process. Therefore, this study focuses on implementing a Butterworth digital filter to reduce motion artifacts, with the hope of improving data recording accuracy and supporting more accurate diagnostic decisions. Based on Fast Fourier Transform (FFT) analysis, the Butterworth digital filter with varying filter orders shows different levels of effectiveness in suppressing noise frequencies. A second-order filter can reduce noise effectively but is not fully optimal. With a fourth-order filter, noise reduction appears almost perfect, while a sixth-order filter can completely suppress noise frequencies. However, in terms of Normal Sinus Rhythm (NSR) signal analysis, the fourth-order filter shows the most consistent and high-quality results. The trials conducted indicate that the reduction of motion artifacts in Holter ECG recordings significantly improves data accuracy. This finding shows that the signal recording quality is already effective in reducing unwanted frequency noise. However, for PQRS peak noise when the patient is walking, noise reduction is not yet completely perfect, making this an area for further improvement in future research. The use of a Butterworth digital filter has proven effective in suppressing noise, especially with a fourth-order filter, which has the highest quality for NSR signal detection. The results of this study are expected to enhance patient monitoring through Holter ECG and improve the early detection of heart condition changes. With the support of Internet of Things (IoT) technology, data from the device can be transmitted directly to healthcare providers, enabling faster and more timely medical responses.]]>
