Adaptive filter design for ECG noise reduction using LMS algorithm

Sharma, Ishika; Mehra, Rajesh; Singh, Mohan

doi:10.1109/icrito.2015.7359333

Cited by 19 publications

(6 citation statements)

References 7 publications

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“…The adaptive LMS filter is proved that it could cancel noises in digital signal processing. Some research shows that this filter method is able to cancel out most error in digital signal, particularly appear in ECG signal [8], [9] and acoustic signal [10].…”

Section: Related Workmentioning

confidence: 99%

“…Certain research papers use this adaptive LMS filter to improve the response of accelerometer for automotive application [5], [8]. Other common applications of this digital adaptive filter are to cancel the signal error in electrocar-diogram (ECG) signal [9], [10] and to compensate cross-talk in acoustic signal [11]. In this paper, The LMS filter is chosen to adopt the vibration frequency error detected by the Z-axis accelerometer and then cancel the vibration error sensed in X and Y axes.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Vibration Error Compensation on IMU-accelerometer Sensor Using Adaptive Filter and Low-pass Filter Approaches

Suwandi

Kitasuka

Aritsugi

2019

Journal of Information Processing

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In vehicle dead reckoning or vehicle positioning systems, an inertial measurement unit (IMU) sensor has an important role to provide acceleration and orientation of the vehicle. The acceleration from the IMU accelerometer is used to calculate the velocity of the vehicle, and then it estimates the vehicle's distance traveled to time. However, the accelerometer suffers from external noises such as vehicle vibrations (generated from the engine, alternator, compressor, etc) and road noises. This paper delivers deep analysis and focuses on how to handle the error from vehicle vibrations. A filter method is proposed by using a combination of adaptive least mean squares (LMS) and low-pass finite impulse response (FIR) filters. The adaptive LMS filter is used to cancel the vehicle vibration error frequencies and adapts those frequency changes in several engine rotation conditions. It is then finalized with the low-pass FIR filter which is used to filter high-frequency vibration noises. Several experiments were made and the results show that the proposed filtering method is able to give better signal to noise ratio (SNR dB) and noise attenuation ratio (ATT dB) in comparison with regular low-pass FIR filter and independent adaptive LMS filter in a particular condition.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Vibration Error Compensation on IMU-accelerometer Sensor Using Adaptive Filter and Low-pass Filter Approaches

Suwandi

Kitasuka

Aritsugi

2019

Journal of Information Processing

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“…The LMS algorithms a linear adaptive filtering algorithm, which consists of two processes A filtering process, which involves computing the output of a linear filter in response to an input signal and generating an estimation error by comparing this output with a desired response [6]. The LMS is based on the steepest descent algorithm where the weight vector (coefficients) is updated from sample to sample [7].…”

Section: Lms Algorithmmentioning

confidence: 99%

Improving the SNR of the underwater acoustic signal affected by wind driven ambient noise using RLS algorithm

Prince

Mehra

Kumar

2015

2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI)

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In recent years, the transmission of acoustic signal underwater is a challenging task. Acoustic is the most preferred signal at low frequency because of low absorption characteristics. Acoustic signals are affected by the different background noise which is produced by the breaking waves, rainfall, marine life and flowing wind. Underwater signal transmission is highly affected by wind noise which is predominant at low frequency. To eliminate such type of noises different adaptive filter algorithm are used. In this paper RLS algorithm is proposed to improve the SNR of acoustic signal. The SNR calculated for different speed samples of wind shows an average increase of 15 dB as compare to LMS. The proposed method shows that RLS algorithm performs better as compared to LMS for the calculation for SNR of the underwater acoustic signal.

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“…Unfortunately, the denoising process is a challenging task due to the overlap of all the noise signals at both low and high frequencies [4]. To prevent noise interference, several approaches have been proposed to denoise ECG signals based on adaptive filtering [5][6][7], wavelet methods [8,9], and empirical mode decomposition [10,11]. However, all these proposed techniques require analytical calculation and high computation; also, because cut-off processing can lose clinically essential components of the ECG signal, these techniques run the risk of misdiagnosis [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

et al. 2020

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The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice.

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Adaptive filter design for ECG noise reduction using LMS algorithm

Cited by 19 publications

References 7 publications

Vehicle Vibration Error Compensation on IMU-accelerometer Sensor Using Adaptive Filter and Low-pass Filter Approaches

Vehicle Vibration Error Compensation on IMU-accelerometer Sensor Using Adaptive Filter and Low-pass Filter Approaches

Improving the SNR of the underwater acoustic signal affected by wind driven ambient noise using RLS algorithm

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

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