•Permutation entropy (PE) is a broadly used algorithm to measure the complexity of signals.•Multiscale PE (MPE) is based on assessing the PE for a number of coarse-grained sequences representing ...temporal scales.•To increase the stability and reliability of MPE, improved MPE (IMPE) is proposed.•Several signal processing concepts are used to show the ability of IMPE.•We also apply MPE and IMPE for real publicly available electroencephalogram (EEG) signals.
Permutation entropy (PE) is a well-known and fast method extensively used in many physiological signal processing applications to measure the irregularity of time series. Multiscale PE (MPE) is based on assessing the PE for a number of coarse-grained sequences representing temporal scales. However, the stability of the conventional MPE may be compromised for short time series. Here, we propose an improved MPE (IMPE) to reduce the variability of entropy measures over long temporal scales, leading to more reliable and stable results. We gain insight into the dependency of MPE and IMPE on several straightforward signal processing concepts which appear in biomedical activity via a set of synthetic signals. We also apply these techniques to real biomedical signals via publicly available electroencephalogram (EEG) recordings acquired with eyes open and closed and to ictal and non-ictal intracranial EEGs. We conclude that IMPE improves the reliability of the entropy estimations in comparison with the traditional MPE and that it is a promising technique to characterize physiological changes affecting several temporal scales. We provide the source codes of IMPE and the synthetic data in the public domain.
Recently, lower limb activity recognition (LLAR) based on surface electromyography (sEMG) signal has attracted increasing attention, mainly due to its applications in the control of robots and ...prosthetics, medical rehabilitation, etc. Traditional machine learning-based LLAR methods rely on expert experience for feature extraction. In addition, the noise interference and class-imbalanced problem can also affect the recognition effect. Aiming at these problems, a LLAR framework based on sEMG data augmentation (DA) and enhanced capsule network (ECN) is proposed in this paper. Firstly, a hybrid denoising technique combining variational mode decomposition and non-local means estimation is designed to effectively filter out noise components mixed in the sEMG. Then, K-Means synthetic minority oversampling technique is utilized to synthesize new samples for minority classes, thereby overcoming the influence of class imbalance on recognition reliability. Finally, an ECN model is constructed to implement end-to-end LLAR, in which an efficient channel attention module is embedded to mine sensitive features, thus further improving the feature learning ability of the classifier. Experimental results indicate that the proposed framework is applicable to multiple types of individuals, including healthy subjects, patients with knee abnormalities, and patients with stroke, providing more satisfactory recognition performance and robustness than state-of-the-art methods..
Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the ...health-related quality of life (HRQoL). Currently, they are evaluated primarily
via
Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (
N
= 39) and patients with neurodegenerative disorders (NDD,
N
= 31) and immune mediated inflammatory diseases (IMID,
N
= 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.
An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel ...data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer's disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.
Classification and analysis of surface EMG (sEMG) signals have been of particular interest due to their numerous applications in the biomedical field. They can be used for the diagnosis of ...neuromuscular diseases, kinesiological studies, and human-machine interaction. However, these signals are difficult to process due to their noisy nature. To overcome this problem, a hybrid of wavelet with ensemble empirical mode decomposition pre-processing technique called WD-EEMD is proposed for classifying lower limb activities based on sEMG signals in healthy and knee abnormal subjects. First, Wavelet De-noising is used for filtering out white Gaussian Noise (WGN) and unwanted signals (contribution of other muscle signals). Next, an Ensemble Empirical Mode Decomposition is used for filtering out power line interference (PLI) and baseline wandering (BW) noises, followed by extraction of a total of nine time-domain features. Finally, the performance parameters of the Linear Discriminant Analysis (LDA) classifier are calculated with a 3-fold cross-validation technique. This study involves 11 healthy and 11 individuals with a knee abnormality for three different activities: walking, flexion of the leg up (standing), and leg extension from sitting position (sitting). Different pre-processing techniques similar to that of WD-EEMD were compared. It was observed that the proposed method achieves an average classification accuracy of 90.69% and 97.45% for healthy subjects and knee abnormal subjects, respectively.
One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure ...(BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010-2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.
•Doppler cardiogram (DCG) could be used as contact-free heart signal sensing method for continuous heart state monitoring.•Signal reconstruction system based on variational autoencoder (VAE) is ...proposed to improve the heart state information consistency of DCG.•Three types of processed DCG sets are applied to system to optimize VAE performance.•Unified analysis of system performance using processed DCG sets is presented better than individual results.•Final result achieved consistency growth in 75.5% of validation set.
A contact-free continuous heart rate variability (HRV) analysis is required to conduct daily heart monitoring and minimize physical contact during medical remedies owing to COVID-19. This paper suggests a Doppler cardiogram (DCG) signal processing and reconstruction system that enables the standard deviation of normal-to-normal peaks (SDNN) obtained from DCG to be used as an actual HRV index. The heartbeat signals of twelve healthy adults were recorded. Three electrodes and a Doppler radar module were used to record the electrocardiogram (ECG) and DCG signals, respectively. To optimize the performance of the signal reconstruction system, two signal processing methods were applied to the dataset. These DCG signals were reconstructed into a signal that mimicked the ECG using a variational autoencoder (VAE), to enhance the consistency of the SDNN. The synthetic signal quality was assessed by comparing the SDNN of the synthetic ECG with that of the reference ECG. A total of 1,430 signals were reconstructed to achieve a valid SDNN. A unified analysis of the signal reconstruction results using different signal processing methods was built up to raise the consistency growth. The final result of the signal reconstruction system represented a consistency improvement of 75.5%, compared to the SDNN of the input DCG.
Reconstructing Time-Dependent Dynamics Clemson, Philip; Lancaster, Gemma; Stefanovska, Aneta
Proceedings of the IEEE,
02/2016, Letnik:
104, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The usefulness of the information contained in biomedical data relies heavily on the reliability and accuracy of the methods used for its extraction. The conventional assumptions of stationarity and ...autonomicity break down in the case of living systems because they are thermodynamically open, and thus constantly interacting with their environments. This leads to an inherent time-variability and results in highly nonlinear, time-dependent dynamics. The aim of signal analysis usually is to gain insight into the behavior of the system from which the signal originated. Here, a range of signal analysis methods is presented and applied to extract information about time-varying oscillatory modes and their interactions. Methods are discussed for the characterization of signals and their underlying nonautonomous dynamics, including time-frequency analysis, decomposition, coherence analysis and dynamical Bayesian inference to study interactions and coupling functions. They are illustrated by being applied to cardiovascular and EEG data. The recent introduction of chronotaxic systems provides a theoretical framework within which dynamical systems can have amplitudes and frequencies which are time-varying, yet remain stable, matching well the characteristics of life. We demonstrate that, when applied in the context of chronotaxic systems, the methods presented facilitate the accurate extraction of the system dynamics over many scales of time and space.
Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, ...has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.
In this work, we propose
, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark
against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).
Our results show that
outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.
Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.
The aim of this study was to compare the decomposition results obtained from high-density surface electromyography (EMG) and concurrently recorded intramuscular EMG. Surface EMG signals were recorded ...with electrode grids from the tibialis anterior, biceps brachii, and abductor digiti minimi muscles of twelve healthy men during isometric contractions ranging between 5% and 20% of the maximal force. Bipolar intramuscular EMG signals were recorded with pairs of wire electrodes. Surface and intramuscular EMG were independently decomposed into motor unit spike trains. When averaged over all the contractions of the same contraction force, the percentage of discharge times of motor units identified by both decompositions varied in the ranges 84%-87% (tibialis anterior), 84%-86% (biceps brachii), and 87%-92% (abductor digiti minimi) across the force levels analyzed. This index of agreement between the two decompositions was linearly correlated with a self-consistency measure of motor unit discharge pattern that was based on coefficient of variation for the interspike interval (R(2) = 0.68 for tibialis anterior, R(2) = 0.56 for biceps brachii, and R(2) = 0.38 for abductor digiti minimi). These results constitute an important contribution to the validation of the noninvasive approach for the investigation of motor unit behavior in isometric low-force tasks.
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