Performance Evaluation of Different Denoising Techniques for Physiological Signals

Muppalla, Vineeth; Suraj, Nikhil; Reddy, Vyza Yashwanth Sai; Suman, D.

doi:10.1109/indicon.2017.8487739

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The sensor data quality was assessed by evaluating the noise level in the data using the signal-to-noise ratio (SNR). The SNR value of a signal is the ratio of the power of the signal to the power of the noise [ 51 ]. Alternatively, it is defined as the ratio of the mean of the measurements (µ) to the standard deviation of the measurements (

) as shown in Equation (4).…”

Section: Methodsmentioning

confidence: 99%

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Bangaru

Wang

Aghazadeh

2020

Sensors

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The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models.

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) as shown in Equation (4).…”

Section: Methodsmentioning

confidence: 99%

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Bangaru

Wang

Aghazadeh

2020

Sensors

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“…evaluating the performance of filters in literature, the most commonly used metrics are RMSE and Signal to Noise Ratio (SNR); the latter mostly used when removing noise in images(Zhang et al (2012),Muppalla et al (2018),Devi and Patil (2020) andBi et al (2021)…”

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confidence: 99%

Low-pass filter application for noise removal in water quality data with high temporal resolution

Coraggio,

Gronow,

Tryfonas

et al. 2023

Preprint

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Large datasets with high temporal resolution are becoming widely available through the use of wireless sensors and other low-effort, automated data collection techniques. The higher the sampling frequency is, the more obvious and significant the noise will be due to the highly unrealistic oscillations in the observations. Machine learning techniques work well with large amounts of data, but it is essential to ensure that the data collected is as clean as possible from noise; otherwise, the machine learning algorithm will struggle to predict the actual data and instead attempt to reproduce the noise. This study explores the use of four low pass filters: Butterworth, Chebyshev I, Chebyshev II and Savitzky-Golay filter for removing noise from water quality dataset with high temporal resolution. This study describes how the filters are implemented and gives advice on how to evaluate the filters’ capability to reduce noise and preserve signal features. The method is applied to five water quality parameters based on a water quality dataset with a 5 minutes resolution collected in an urban surface water body in Bristol, United Kingdom. Based on the results of this study, it has been found that for the analysed water quality parameters (conductivity, water temperature, dissolved oxygen, and fDOM) Butterworth filter with a cut-off frequency between 2.33E-05 Hz~12 hours and 4.5E-04 Hz~ 6 hours is the filter that allows the best compromise between noise removal and signal preservation

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“…between 12 and 14, the SNR value for Chebyshev Type II is high. It has been found that the SNR variations in the filter order leads to selecting the right filter for ECG filtering.Muppalla et al[18] evaluated Chebyshev Type I and Chebyshev Type I and the result obtained has represented that Chebyshev Type I filter outperformed better than Chebyshev Type II based on SNR, PSNR, MSE, and RMSE performance metrics.Kumar et al[19] utilized Butterworth, Chebyshev Type I, Chebyshev Type II and Elliptic filters reduce noise in ECG signal. Based on SNR and MSE Butterworth filter outperformed than other IIR filters.…”

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confidence: 99%

Heart beat peak detection using signal filtering in ECG data

Rexy¹,

Velmani²,

Rajakumar³

2019

IJATEE

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The human heart is the muscular organ that pumps the blood through the circulatory system and paves way for a human to be alive. Human being loss their life if the heart malfunctions. Nowadays the death ratio of human being has been increased due to heart disease. Heart disease is the leading cause of many human deaths. The physical interpretation of the electrical behaviour throughout the cardiac cycle is recorded as electrocardiogram (ECG). ECG is used to measure the rate and rhythm of heartbeats which is the base to identify the heart beat disorders. ECG leads to monitor the heartbeat status and makes way for earlier detection of heart problems. ECG signal is a graphical representation or cardiac activity which helps to identify cardiac diseases [1].

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Performance Evaluation of Different Denoising Techniques for Physiological Signals

Cited by 5 publications

References 15 publications

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Low-pass filter application for noise removal in water quality data with high temporal resolution

Heart beat peak detection using signal filtering in ECG data

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