A Real Time Approach: ECG Noise Reduction in Chebyshev Type II Digital Filter

Abstract

It is well known that biomedical signals carry important information about the behavior of the living systems under study. With the analysis of the Electrocardiogram (ECG) signal it may be possible to predict heart problems or monitor patient recovery after a heart intervention. A proper processing of these signals enhances their physiological and clinical information. The quality of biomedical signal is degraded mainly by many sources of noise such as power line interference (PLI), baseline drift, muscle contraction noise etc. The objective of this paper is to implement the Chebyshev Type II digital filter to overcome degradation by improving ECG signal quality for quality clinical diagnosis. Different artifacts are the reason in corruption of the ECG signal. Removing noise from the biomedical signal is still challenging and a rapidly expanding field with a wide range of applications in ECG noise reduction. Present paper deals with the design of Chebyshev Type II filters including low pass, high pass, notch filters. Also the performance is tested using cascading of filters. For real time application 1711 add on card is used. Different ECG signals from MIT/BIH arrhythmia database are used for validation and compared with real time ECG signals. The objective of this paper is to implement the Chebyshev Type II digital filter to overcome degradation by improving ECG signal quality for quality clinical diagnosis. Finally results indicate noise reduction in the ECG.

Optimization design of fractional‐order Chebyshev lowpass filters based on genetic algorithm

“…GA makes the magnitude response corresponding to the transfer function shown in Equation (10) approximate Equation (9), that is, minimizes the cumulative sum of the squares of the magnitude response errors of both of them, and the values of the parameters a 0 , a 1 , and a 2 can be obtained by using Equation (11):…”

“…According to the GA flow chart shown in Figure 7 and the GA control parameters in Table 1, the relevant program is executed, and the running time is 366.2 s (Running Environment: Intel(R) Core (TM) i7-8550U CPU @ 1.80GHz, RAM 16G, Windows 10, Python 3.9.5), the optimized parameter values in Equation ( 10) can be obtained using Equation (11), and the optimized parameter values are shown in Table 2.…”

“…Whether it is an analog filter or a digital filter, it is essential for many modern applications such as communications and biomedicine. 10,11 In engineering applications, when designing filters according to the design specifications, the order of the filter is usually a real number. However, when using traditional design methods, the order of the filter is rounded to the nearest integer.…”

“…The Chebyshev filter is one of the most often used filters in practice 12 and has been used in various fields, including biomedicine. 11,13 For any all-pole transfer function of a given order, the Chebyshev filter can provide the maximum theoretical roll-off rate. 14 The magnitude response of the normalized nth order Chebyshev lowpass filter is…”

Heart beat peak detection using signal filtering in ECG data

“…Chebyshev Type II has no ripple in the passband, but does have equiripple in the stopband. Inverse Chebyshev filters are also called Type II Chebyshev filter [31,32]. The gain is…”