A comprehensive adaptive filter to eliminate baseline wander from ECG signals

Rahman, Saifur; Kim, Youngpil; Choi, Chul-Hyung; Kim, Sikyung

doi:10.1109/ictc.2017.8190939

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…wavelet, low-, high-pass filter), time-frequency-domain filters (e.g. discrete cosine transform) and data-driven filters (such as empirical mode decomposition, single value decomposition) have also been used for removing ECG noise [12][13][14][15]. The major…”

Section: Research Gaps In Electrocardiogram Noise Measurementmentioning

confidence: 99%

Robustness of electrocardiogram signal quality indices

Rahman

Karmakar

Natgunanathan

et al. 2022

J. R. Soc. Interface.

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Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQI p ), skewness (SQI skew ), signal-to-noise ratio (SQI snr ), higher order statistics SQI (SQI hos ) and peakedness of kurtosis (SQI kur ). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.

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Section: Research Gaps In Electrocardiogram Noise Measurementmentioning

confidence: 99%

Robustness of electrocardiogram signal quality indices

Rahman

Karmakar

Natgunanathan

et al. 2022

J. R. Soc. Interface.

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show abstract

“…A study presented the application adaptive filter based on LMS algorithm combined with low pass Butterworth filter to reduce both the harmonics and fundamental frequency of the sirens (Lu, et al, 2015). Another research proposed NLMS adaptive filter with modified step size to eliminate baseline wandering from ECG signals with minimal distortion to the original signal (Rahman, et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Broad-Band Noise Reduction using Least Mean Square (LMS)-Adaptive Line Enhancer (ALE) on Doppler Blood Flow Sound Signal

SANTOSO¹,

Wahyunggoro

Nugroho

2022

ELKOMIKA

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Haemorrhoidal artery ligation (HAL) adalah metode operasi wasir yang memiliki keunggulan rasa sakit minimal dan waktu pemulihan cepat. Prosedur HAL bergantung pada bunyi Doppler untuk menentukan letak arteri dalam liang anus. Derau yang tercampur dalam sinyal membuat dokter kesulitan mendengarkan bunyi Doppler yang menandakan letak arteri wasir. Adaptive line enhancer (ALE) dengan algoritma least mean square (LMS) digunakan dalam penelitian ini untuk mengurangi aras derau yang terkandung dalam sinyal bunyi Doppler aliran darah. Hasil simulasi menunjukkan parameter mean square error (MSE), signal to noise ratio (SNR), dan waktu eksekusi untuk berbagai orde tapis. Nilai maksimum power spectral density (PSD) sinyal yang masuk ke tapis -86 dB/Hz, kemudian turun menjadi -101 dB/Hz setelah lewat tapis. SNR tertinggi 40,03 dB didapat ketika orde tapis 16. Waktu eksekusi hampir tidak berubah meskipun orde filter dinaikkan.

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