Abstract
As an important biological signal, electrocardiogram (ECG) signals provide a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of ECG waveform, leading to the false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Based on the above, this paper introduces two improved adaptive algorithms based on the classical least mean square (LMS) algorithm by introducing symbolic functions and block-processing concepts.
Highlights
As the most important organ in the human body, the heart is the power source of metabolism of various organs and tissues
This paper introduces two improved adaptive algorithms based on the classical least mean square (LMS) algorithm by introducing symbolic functions and block-processing concepts
From the NLMS algorithm based on symbol function proposed in the previous section, the concept of block processing can be introduced to further reduce the computational complexity of the algorithm
Summary
As the most important organ in the human body, the heart is the power source of metabolism of various organs and tissues. Power frequency interference [6], the most common ECG signal interference, is caused by the magnetic field distributed by the human body by the power supply, resulting in added 50 Hz sinusoidal and harmonic components in the pure ECG signals [7]. The symbolic algorithms are proposed, namely, the normalized [11] based symbol the elimination of two kinds of interference: signal frequency interference and functions [13]and andthe thenormalized block-processing [14] concept introduced and applied to thefunction the elimination function block-processing. The and the block-processing concept introduced and applied to of two kinds of interference: ECG signal frequency interference and BW interference. Cancellation laboratory were used to validate the algorithm and analyze the results in detail
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