The Study and Comparison between Various Digital Filters for ECG De-noising

Chieng, Thion Ming; Hau, Yuan Wen; Omar, Zaid

doi:10.1109/iecbes.2018.8626661

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…Wavelet noise reduction was employed to remove the baseline noise and drift signal from the signal. Gaussian filters, on the other hand, transform the data by building a mathematical model, smoothing it, and reducing the effect of noise [ 30 , 31 , 32 , 33 , 34 ]. The detailed data processing methods are shown in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Wang

et al. 2022

Biosensors

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With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving the human–machine interaction system is to improve its understanding of the pilots’ status, including fatigue, stress, workload, etc. Monitoring pilots’ status can effectively prevent human error and achieve optimal human–machine collaboration. As such, there is a need to recognize pilots’ status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot’s behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots’ behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way.

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Section: Methodsmentioning

confidence: 99%

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Wang

et al. 2022

Biosensors

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“…The smaller the RMSE value is, the lesser the distortion of the signal after the noise reduction process and closer to the noiseless signal. The mathematical definition of RMSE is expressed in (26). (24) (25) (26) where s(n) is the noiseless ECG signal and is the de-noised ECG signal.…”

Section: Methodsmentioning

confidence: 99%

“…This cause a huge complication in analyzing and identifying the ECG abnormalities [8]. ECG noises that commonly encountered includes powerline interference [9][10][11][12][13], baseline wander [14][15][16][17], electromyography noise [18,19], and electrode motion noise [20][21][22][23][24][25][26]. The ECG analysis can be challenging with the existence of these artifacts as the chance of misinterpretation increase, subsequently lead to the inaccurate clinical analysis and diagnosis result.…”

Section: Introductionmentioning

confidence: 99%

Qualitative and Quantitative Performance Comparison of ECG Noise Reduction and Signal Enhancement Method based on Various Digital Filter Designs and Discrete Wavelet Transform

Chieng¹,

Hau²,

Omar³

et al. 2020

IJCDS

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“…The ultimate Intrinsic Mode Functions (IMFs) are acquired by taking the average of the set of IMFs that correspond to each experiment as given by eqn. (1).…”

Section: A Mlpt-eemdmentioning

confidence: 99%

An Improved Firefly Optimization Algorithm for Analysis of Arrhythmia Types

Sinnoor,

Janardhan

2023

IJRITCC

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Irregular heartbeats rhythm is the result of arrhythmia condition which can be a threat to life if not treated at the early stage. If it is necessary to know the type of arrhythmia to treat the patient appropriately. The traditional method is complex and an efficient algorithm is required to diagnose. An improved firefly optimization algorithm is proposed to analyze arrhythmia types. Four performance measures confirm the model's effectiveness and experimental evaluation shows that it achieves a sensitivity of 86.27%, accuracy of 86.14%, precision of 87.52%, and specificity of 87.37% in arrhythmia-type classification. The algorithm can effectively classify the arrhythmia types with high accuracy and specificity.

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The Study and Comparison between Various Digital Filters for ECG De-noising

Cited by 7 publications

References 33 publications

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Qualitative and Quantitative Performance Comparison of ECG Noise Reduction and Signal Enhancement Method based on Various Digital Filter Designs and Discrete Wavelet Transform

An Improved Firefly Optimization Algorithm for Analysis of Arrhythmia Types

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