Stationary wavelet transform based ECG signal denoising method

Kumar, Ashish; Tomar, Harshit; Mehla, Virender Kumar; Komaragiri, Rama; Kumar, Manjeet

doi:10.1016/j.isatra.2020.12.029

Cited by 145 publications

(67 citation statements)

References 36 publications

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“…Electromagnetic interference in the surrounding environment may also cause power line interference [ 31 ]. At present, the researchers still consider power line interference when processing data with wearable ECG acquisition devices [ 32 , 33 ]. Accordingly, the 50 Hz band filter is used to remove the power line interference.…”

Section: Methodsmentioning

confidence: 99%

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Pan

Wang

et al. 2021

Sensors

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Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

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Section: Methodsmentioning

confidence: 99%

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Pan

Wang

et al. 2021

Sensors

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“…The ECG is the recording of the heart's electrical activity moving in time and corresponding to the depolarization and repolarization of the heart muscle [20]. Fig.…”

Section: Ecg Signal Denoising Overviewmentioning

confidence: 99%

Hardware Architecture for Adaptive Dual Threshold Filter and Discrete Wavelet Transform based ECG Signal Denoising

Mejhoudi¹,

JENKAL²,

Saddik³

et al. 2021

IJACSA

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The ECG signal, like all signals obtained when instrumenting a data acquisition system, is affected by noises of physiological and technical sources such as Electromyogram (EMG) and power line interferences, which can deteriorate its morphology. To overcome this issue, it's subjected to apply a preprocessing step to remove these noises. Filtring techniques are complex computations becoming more common in medical applications, which must be completed in real-time. As a result, these applications are geared at integrating high-performance embedded architectures. This paper presents an FPGA (Field Programmable Gate Array) embedded architecture designed for an ECG denoising hybrid technique based on the Discrete Wavelet transform (DWT) and the Adaptive Dual Threshold Filter (ADTF), dedicated to handle with noises affecting ECG signals.The architecture was designed following a hardware-software codesign using a high-level description language and synthetized to be implemented on different FPGAs due to the structural description flexibility. The global architecture was divided into a set of functional blocks to allow parallel processing of ECG data. The simulation results confirm the high performance of the system in noise reduction without affecting the morphology of the signal. The process takes 0.3 ms with an acquisition frequency of 360 Hz. The whole architecture requires a small area in different FPGAs in terms of resources utilization. It uses less than 1% of the total registers for all FPGA devices which represents a total of 292 registers for Cyclone III LS, Cyclone IV GX, Cyclone IV E, and Arria II GX; and a total of 329 registers for Cyclone V. The logic elements occupancy varies between 3% using Cyclone V and 60% using Cyclone IV GX freeing up space for other parallel processing tasks.

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“…Wavelet Transform can perform a time-frequency joint analysis of signals, with high resolution, and can effectively denoise and extract non-stationary signals [ 17 , 18 ]. Kumar used Wavelet Transform to process ECG signals with an improved threshold algorithm to remove the influence of baseline drift, power frequency interference and EMG interference [ 19 ]. Guevorkian applied the pipeline structure to the discrete Wavelet Transform, which greatly improved the computational efficiency [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Vital Signs Fast Detection and Extraction Method of UWB Impulse Radar Based on SVD

Liu

Cheng

et al. 2022

Sensors

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The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy.

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Stationary wavelet transform based ECG signal denoising method

Cited by 145 publications

References 36 publications

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Hardware Architecture for Adaptive Dual Threshold Filter and Discrete Wavelet Transform based ECG Signal Denoising

A Vital Signs Fast Detection and Extraction Method of UWB Impulse Radar Based on SVD

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