Detection of ADC clipping, quantization noise, and amplifier saturation in surface electromyography

Fraser, G. D.; Chan, Adrian D. C.; Green, James R.; MacIsaac, Dawn

doi:10.1109/memea.2012.6226629

Cited by 21 publications

(15 citation statements)

References 6 publications

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“…Two further saturation identification algorithms were explored: the rail contact mask [13] and analogue-to-digital converter (ADC) clipping [14]. The rail contact mask [13] identifies a whole second of samples if they exist within 1% of the limits of the ADC resolution.…”

Section: Identification and Filtering Algorithmsmentioning

confidence: 99%

“…The rail contact mask [13] identifies a whole second of samples if they exist within 1% of the limits of the ADC resolution. The ADC clipping algorithm [14] identifies a second of data (e.g. a single R‐peak interval at a common heart rate of 60 beats per minute) as an artefact should two consecutive samples lie beyond 95% of the average signal amplitude.…”

Section: Identification and Filtering Algorithmsmentioning

confidence: 99%

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ECG artefact identification and removal in mHealth systems for continuous patient monitoring

Imtiaz

Mardell

Saremi-Yarahmadi

et al. 2016

Healthcare Technology Letters

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Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automatically processed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patient movement, sensor placement and interference from other sources. Owing to the large volume of data these artefacts need to be automatically identified so that the analysis systems and doctors are aware of them while making medical diagnosis. Three important factors are explored that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality, interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm, whereas the third is particularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filtering algorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstrate how different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.

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Section: Identification and Filtering Algorithmsmentioning

confidence: 99%

Section: Identification and Filtering Algorithmsmentioning

confidence: 99%

ECG artefact identification and removal in mHealth systems for continuous patient monitoring

Imtiaz

Mardell

Saremi-Yarahmadi

et al. 2016

Healthcare Technology Letters

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“…sEMG was simulated in the same manner that was presented in [12], by passing white Gaussian noise through a shaping filter [2]. In this work, shaping parameters were randomly selected from four shaping parameters sets (i.e., pairs of f l and f h as shown in Table 1) at each iteration.…”

Section: B Simulated Datamentioning

confidence: 99%

“…Previous work in our group has developed sEMG biosignal quality analysis methods for power line interference [8]- [10], ECG interference [11], analog-to-digital conversion clipping [12], quantization noise [12], and amplifier saturation [12]. These methods are considered "bottom-up" approaches, each targeting a single specific contaminant.…”

Section: Introductionmentioning

confidence: 99%

“…Such "top-down" approaches can complement "bottom-up" approaches. This paper examines the correlation coefficient test for normality as a means of discerning contaminant-free sEMG (a "top-down" approach); this correlation coefficient test for normality was employed in one of our previous works to detect amplifier saturation [12]. In this work, we evaluate the method for detecting power line interference, motion artifact, and ECG interference.…”

Section: Introductionmentioning

confidence: 99%

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Biosignal quality analysis of surface EMG using a correlation coefficient test for normality

Fraser

Chan

Green

et al. 2013

2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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A correlation test of normality is applied to surface electromyography (sEMG) signals to detect and quantify contaminants. Three contaminants were examined: power line interference, motion artifact, and electrocardiogram (ECG) interference. sEMG data from both simulations and human subjects were artificially contaminated at various signal-to-noise ratios (SNR). For each contaminant, lower SNR values were associated with a lower Pearson correlation coefficient; however, the value of the Pearson correlation coefficient did not correspond to the same SNR across contaminant types. The correlation test of normality can be a useful method for detecting contaminants in sEMG, when the type of contaminant is unknown (e.g., for automatic verification sEMG acquisition setups or automatic rejection of contaminated sEMG signals).

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Identification and removal of contaminants in sEMG recordings through a methodology based on Fuzzy Inference and Actor-Critic Reinforcement learning

Tosin¹,

Balbinot²

2022

Expert Systems with Applications

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Detection of ADC clipping, quantization noise, and amplifier saturation in surface electromyography

Cited by 21 publications

References 6 publications

ECG artefact identification and removal in mHealth systems for continuous patient monitoring

ECG artefact identification and removal in mHealth systems for continuous patient monitoring

Biosignal quality analysis of surface EMG using a correlation coefficient test for normality

Identification and removal of contaminants in sEMG recordings through a methodology based on Fuzzy Inference and Actor-Critic Reinforcement learning

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