A novel technique for muscle onset detection using surface EMG signals without removal of ECG artifacts

Zhou, Ping; Zhang, Xu

doi:10.1088/0967-3334/35/1/45

Cited by 42 publications

(25 citation statements)

References 26 publications

(39 reference statements)

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“…The f low of the 318 frequency band B0 was intentionaly set to 0.08 Hz thus retaining the respiration 319 component (0.2-0.34 Hz) in the power spectra of the EHG and TOCO signals of dummy 320 intervals and non-pregnant dummy intervals, and preserveing at least one high spectral 321 peak for normalization purposes. 322 The SE has been successfully applied for analysis of many biological signals such as 323 ECG, blood pressure, electroencephalogram, and electromyogram [43]. The SE has the 324 ability to estimate the level of regularity or predictability of time series.…”

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confidence: 99%

Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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Predicting preterm birth is uncertain, and numerous scientists are searching for non-invasive methods to improve its predictability. Current researches are based on the analysis of ElectroHysteroGram (EHG) records, which contain information about the electrophysiological properties of the uterine muscle and uterine contractions. Since pregnancy is a long process, we decided to also characterize, for the first time, non-contraction intervals (dummy intervals) of the uterine records, i.e., EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal). For this purpose, we developed a new set of uterine records, TPEHGT DS, containing preterm and term uterine records of pregnant women, and uterine records of non-pregnant women. We quantitatively characterized contraction intervals (contractions) and dummy intervals of the uterine records of the TPEHGT DS in terms of the normalized power spectra of the EHG and TOCO signals, and developed a new method for predicting preterm birth. The results on the characterization revealed that the peak amplitudes of the normalized power spectra of the EHG and TOCO signals of the contraction and dummy intervals in the frequency band 1.0-2.2 Hz, describing the electrical and mechanical activity of the uterus due to the maternal heart (maternal heart rate), are high only during term pregnancies, when the delivery is still far away; and they are low when the delivery is close. However, these peak amplitudes are also low during preterm pregnancies, when the delivery is still supposed to be far away (thus suggesting the danger of preterm birth); and they are also low or barely present for non-pregnant women. We propose the values of the peak amplitudes of the normalized power spectra due to the influence of the maternal heart, in an electro-mechanical sense, in the frequency band 1.0-2.2 Hz as a new biophysical marker for the preliminary, or early, assessment of the danger of preterm birth. The classification of preterm and term, contraction and dummy intervals of the TPEHGT DS, for the task of the automatic prediction of preterm birth, using sample entropy, the median frequency of the power spectra, and the peak amplitude of the normalized power spectra, revealed that the dummy intervals provide quite comparable and slightly higher classification performances than these features obtained from the contraction intervals. This result suggests a novel and simple clinical technique, not necessarily to seek contraction intervals but using the dummy intervals, for the early assessment of the danger of preterm birth. Using the publicly available TPEHG DB database to predict preterm birth in terms of classifying between preterm and term EHG records, the 1/50 proposed method outperformed all currently existing methods. The achieved classification accuracy was 100% for early records, recorded around the 23rd week of pregnancy; and 96.33%, the area under the curve of 99.44%, for all records of the database. Since the proposed method is cap...

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confidence: 99%

Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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“…When it comes to the classification and assessment of changes in muscle activity recorded via surface electromyography (EMG), entropy is a commonly used tool [13,14,15], with measures based on approximate entropy (ApEn) [16] being a popular choice. ApEn provides a family of statistics to allow estimation of the rate of information generation in a time series, with sample entropy (SampEn) [17] being a modification of ApEn aimed at reducing the bias inherent in the calculation of ApEn.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

Jelfs

Tin

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The concept of muscle synergies has proven to be an effective method for representing patterns of muscle activation. The number of degrees of freedom to be controlled are reduced while also providing a flexible platform for producing detailed movements using synergies as building blocks. It has previously been shown that small components of movement are crucial to producing precise and coordinated movement. Methods which focus on the variance of the data make it possible to overlook these small components in the synergy extraction process. However, algorithms which address the inherent complexity in the neuromuscular system are lacking. To that end we propose a new nonnegative matrix factorization algorithm which employs a cross fuzzy entropy similarity measure, thus, extracting muscle synergies which preserve the complexity of the recorded muscular data. The performance of the proposed algorithm is illustrated on representative EMG data.

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“…Since it is very difficult to separate the actual signal from the associated noise using the conventional analog signal processing techniques for proper interpretation, so now a time with the advancement in the high speed digital computers technology and advancement in sophisticated processing techniques [1] with human computer interaction based [2][3][4][5][6][7][8][9][10][11] mathematical models have made it possible to develop Surface Electromyogram detection and analysis algorithms to be used in robotic devices and upper-limb prostheses [12].…”

Section: Introductionmentioning

confidence: 99%

A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier

Veer

2015

Measurement

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A novel technique for muscle onset detection using surface EMG signals without removal of ECG artifacts

Cited by 42 publications

References 26 publications

Characterization and automatic classification of preterm and term uterine records

Characterization and automatic classification of preterm and term uterine records

Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier

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