Real Time EMG Noise Cancellation from ECG Signals using Adaptive Filtering

Hanine, M.; Abdelmounim, Elhassane; Haddadi, Rachid; Belaguid, Abdelaziz

doi:10.1145/3167486.3167543

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…The methods which were compared are the low-pass Humming-window FIR filter, the adaptive LMS Filter, and the proposed filter approach that combines both Adaptive LMS and low-pass FIR filters. The SNR is calculated by the formula below [9], [16]. As stated in Section 3.1, in the experiments which were done with a vehicle in an idle condition, all recorded acceleration data will be treated as noises.…”

Section: Performance Analysismentioning

confidence: 99%

“…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: Performance Analysismentioning

confidence: 99%