Automated Biosignal Quality Analysis for Electromyography Using a One-Class Support Vector Machine

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

doi:10.1109/tim.2014.2317296

Cited by 99 publications

(74 citation statements)

References 34 publications

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“…When acquiring S-EMG signals, which generally needs specific hardware and circuitry [11] that must also produce a signal with acceptable quality [12], one has to chose parameters such as sampling rate, bit depth, number of channels (derivations), and record duration, which have direct impact on necessary transmission and storage resources. In addition, such a problem is even worse when high-density electromyography [13] is employed, due to the large amount of data.…”

Section: Introductionmentioning

confidence: 99%

Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

Melo

Filho

Júnior

2016

JCIS

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Abstract-Recently, two-dimensional techniques were successfully employed for encoding surface electromyographic (S-EMG) records, through the use of off-the-shelf image encoders as an effective alternative for that kind of signal. However, as S-EMG signals are very different from natural images, there is often a preprocessing step before compression, in an attempt to improve the performance of the chosen encoder. This paper address the mentioned approach and presents an investigation regarding the performance of video and image encoders, when used for compressing S-EMG signals. In addition, two new preprocessing techniques are introduced, named as euclidean distance sorting (EDS) and region-based euclidean distance sorting (REDS), which have the potential to enhance the exploitation of intersegment correlations, normally present on S-EMG records. Experiments were carried out with real isometric records acquired in laboratory, which were firstly preprocessed and then compressed with the JPEG2000, H.264/advanced video coding, and high efficiency video coding (HEVC) algorithms. A brief analysis reveals that the proposed scheme is effective, given that JPEG2000 and HEVC allied to EDS and REDS even outperform state-of-the-art schemes available in the literature, in terms of PRD × Compression Ratio and spectral-parameter estimation.

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Section: Introductionmentioning

confidence: 99%

Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

Melo

Filho

Júnior

2016

JCIS

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“…Due to the sensitive nature, external noise sources and artifacts can influence sEMG signals, which frequently mislead the classification algorithm, resulting in poor classification accuracy. Most of the noise, artifacts and interference that may contaminate sEMG signals consist of electrode noise, motion artifacts, power line noise, ambient noise and inherent noise in electrical & electronic equipment [18,19]. There are some works like [2,19,20] which respectively aim on classify and identify the destructive interference present in the sEMG signal.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the noise, artifacts and interference that may contaminate sEMG signals consist of electrode noise, motion artifacts, power line noise, ambient noise and inherent noise in electrical & electronic equipment [18,19]. There are some works like [2,19,20] which respectively aim on classify and identify the destructive interference present in the sEMG signal. Indeed, the acquisition process is a critical stage that directly affects the posterior processing stages, undermining the classification process and accuracy of the system.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Method for sEMG Movement Classification Based on Continuous Optimal Electrode Assortment

Favieiro

Cene

Balbinot

2016

BJIC

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Abstract-Surface electromyography (sEMG) analysis is becoming increasingly popular in a broad variety of applications. Despite satisfactory classification rates are frequently obtained through supervised machine learning (ML) algorithms, there are some issues mostly related to the data acquisition which are not properly addressed in current studies. In this paper we present a method capable of mitigate the noise in the sEMG acquisition caused mainly by loose or misplaced non-invasive electrodes. To address this issue we propose a stage of pre-processing capable of being adapted on a variety of classifiers. The proposed method is capable of identify this two anomalies in the signal and provide the data to retrain the classifier, discarding the problematic channels. Once the method is retrained using only the most relevant channels it is possible to increase the accuracy rate of the ML method. The method was tested on a database containing five ablebodied subjects and four amputee subjects of both sexes. The average classification accuracy for the adaptive input selection method was 83,96  6,5% for the able-bodied subjects and 61,15  7,7% for the amputees subjects against 72,06  8,0% in ablebodied subjects and 39,77  10,6% for the amputees subjects considering the non-adaptive approach. Both systems make use of the proposed method to classify 9 distinguish upper-limb movements with different degrees of freedom.Index Terms-electrode assortment, upper-limb signal, neural network, auto-adaptive methods, surface electromyography

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“…3) quantization, and 4) mitigation [56]. As the name suggests, detection is the process of Lastly, mitigation attempts to improve the biosignal quality by removing or reducing contaminants.…”

Section: Categories Of Biosignal Quality Analysismentioning

confidence: 99%

Biosignal Quality Analysis in Ambulatory Electrocardiograms to Enhance Detection of Myocardial Ischemia

Quesnel¹

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Patients can have an elevated risk of major cardiac complications following non-cardiac surgery. Myocardial ischemia is a lack of oxygen in the heart tissue and may be a precursor to infarction and cardiac death. This ischemia can be detected in the ST segment of a patient's electrocardiogram (ECG) hours before lasting tissue damage occurs. The Perioperative Ischemia Reduction Studies (PROSE) seek to leverage early detection of ischemia to establish an intervention window to stop the ischemic cascade before it results in lasting tissue damage. Ambulatory ECG is used to monitor patients for PROSE; however due to the mobile nature of the recording, ambulatory ECG is subject to higher levels of noise contaminants. This can degrade the ST analysis used in the detection of ischemia, resulting in high false alarm rates that render ambulatory ischemia monitoring clinically impractical. This thesis investigates the possibility of modifying alarm outputs on the basis of ECG signal quality to increase positive predictive value (PPV) of alarms without detriment to existing sensitivity. First, this thesis presents methods to automatically quantify ECG signal quality in a single ECG lead via a signal quality index (SQI) and validates these methods using both non-ischemic and ischemic ECG data. This thesis then proposes and evaluates three system approaches to modifying alarms using this SQI. Resulting modified alarms reveal increased PPV from 0.41 to 0.85 while maintaining sensitivity. These results are encouraging and indicate that these methods could help provide for practical monitoring of myocardial ischemia via ambulatory ECG in the context of PROSE.iii

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Automated Biosignal Quality Analysis for Electromyography Using a One-Class Support Vector Machine

Cited by 99 publications

References 34 publications

Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

Self-adaptive Method for sEMG Movement Classification Based on Continuous Optimal Electrode Assortment

Biosignal Quality Analysis in Ambulatory Electrocardiograms to Enhance Detection of Myocardial Ischemia

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