Enhanced processing and analysis of multi‐channel non‐invasive abdominal foetal ECG signals during labor and delivery

Martínek, Radek; Kelnar, Michal; Koudelka, Petr; Vaňuš, Jan; Bilík, Petr; Janků, Petr; Nazeran, Homer; Žídek, Jan

doi:10.1049/el.2015.2222

Cited by 23 publications

(11 citation statements)

References 7 publications

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“…Where L is the number of segments of speech signal, K is the number of segments in speech activity, V AD i is information about speech activity (values 0 and 1), further x i (n) = x(mi+n), n i (n) = n(mi+n) -segments of length M selected step m. More information in [30][31][32][33]. The DTW (Dynamic Time Warping) criterion in Tab.…”

Section: The Results Of the Experiments Conductedmentioning

confidence: 99%

“…The work in this article builds on these studies and extends them. In these studies, the authors address the issue of removing the disturbing components from useful signals (noise suppression during audio communication in the cockpit of a fighter airplane [1], noise suppression in foetal ECG [2,32,38]) using linear adaptive filtration [5]. Linear adaptive filters play an important role in statistical signal processing [6].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

Martínek

Kelnar

Vaňuš

et al. 2015

Journal of Electrical Engineering

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The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.

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Section: The Results Of the Experiments Conductedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

Martínek

Kelnar

Vaňuš

et al. 2015

Journal of Electrical Engineering

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“…There are many different methodologies to extract fECG signals using adaptive filters based on one or several maternal reference channels (as shown in Figure 5). These methodologies include the LMS and RLS Algorithms, Artificial Intelligence (AI) Techniques, Fuzzy Inference Systems (FISs) [22,23], Genetic Algorithms (GA), and Bayesian Adaptive Filtering Frameworks which comprise Kalman Filters.…”

Section: Adaptive Methodologiesmentioning

confidence: 99%

“…That is also a reason why linear algorithms yield better results when tested with synthetic data compared to those tested with real data. As the underlying physiological processes in the human body exhibit nonlinear behavior it seems more reasonable to use nonlinear methods for the construction of accurate and functional adaptive filters [22,32] to achieve better outcomes.…”

Section: An Example: An Adaptive Noise Cancellation System For Fecg Smentioning

confidence: 99%

Nonlinear Adaptive Signal Processing Improves the Diagnostic Quality of Transabdominal Fetal Electrocardiography

Martínek¹,

Kahánková²,

Skukova³

et al. 2016

Advanced Biosignal Processing and Diagnostic Methods

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The abdominal fetal electrocardiogram (fECG) conveys valuable information that can aid clinicians with the diagnosis and monitoring of a potentially at risk fetus during pregnancy and in childbirth. This chapter primarily focuses on noninvasive (external and indirect) transabdominal fECG monitoring. Even though it is the preferred monitoring method, unlike its classical invasive (internal and direct) counterpart (transvaginal monitoring), it may be contaminated by a variety of undesirable signals that deteriorate its quality and reduce its value in reliable detection of hypoxic conditions in the fetus. A stronger maternal electrocardiogram (the mECG signal) along with technical and biological artifacts constitutes the main interfering signal components that diminish the diagnostic quality of the transabdominal fECG recordings. Currently, transabdominal fECG monitoring relies solely on the determination of the fetus' pulse or heart rate (FHR) by detecting RR intervals and does not take into account the morphology and duration of the fECG waves (P, QRS, T), intervals, and segments, which collectively convey very useful diagnostic information in adult cardiology. The main reason for the exclusion of these valuable pieces of information in the determination of the fetus' status from clinical practice is the fact that there are no sufficiently reliable and well-proven techniques for accurate extraction of fECG signals and robust derivation of these informative features. To address this shortcoming in fetal cardiology, we focus on adaptive signal processing methods and pay particular attention to nonlinear approaches that carry great promise in improving the quality of transabdominal fECG monitoring and consequently impacting fetal cardiology in clinical practice. Our investigation and experimental results by using clinicalquality synthetic data generated by our novel fECG signal generator suggest that adaptive neuro-fuzzy inference systems could produce a significant advancement in fetal monitoring during pregnancy and childbirth. The possibility of using a single device to

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“…Discriminative characteristics must be selected from these features by applying extra processing over them [10]. The problem of extracting characteristic based features is when we use fiducial points that may cause difficulties in detecting the boundaries especially in the presence of noise.…”

Section: Ecg Signal Physiologymentioning

confidence: 99%

Utilizing ECG Waveform Features as New Biometric Authentication Method

Shdefat¹,

Joo²,

Kim³

2018

IJECE

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In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects" raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.

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Enhanced processing and analysis of multi‐channel non‐invasive abdominal foetal ECG signals during labor and delivery

Cited by 23 publications

References 7 publications

A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

Nonlinear Adaptive Signal Processing Improves the Diagnostic Quality of Transabdominal Fetal Electrocardiography

Utilizing ECG Waveform Features as New Biometric Authentication Method

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