Ultra Wideband Signals and Systems in Communication Engineering

A new statistic has been developed to quantify the amount of regularity in data. This statistic, ApEn (approximate entropy), appears to have potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility. The focus of this article is ApEn. We commence with a simple example of what we are trying to discern. We then discuss exact regularity statistics and practical difficulties of using them in data analysis. The mathematic formula development for ApEn concludes the Solution section. We next discuss the two key input requirements, followed by an account of a pilot study successfully applying ApEn to neonatal heart rate analysis. We conclude with the important topic of ApEn as a relative (not absolute) measure, potential applications, and some caveats about appropriate usage of ApEn. Appendix A provides example ApEn and entropy computations to develop intuition about these measures. Appendix B contains a Fortran program for computing ApEn. This article can be read from at least three viewpoints. The practitioner who wishes to use a "black box" to measure regularity should concentrate on the exact formula, choices for the two input variables, potential applications, and caveats about appropriate usage. The physician who wishes to apply ApEn to heart rate analysis should particularly note the pilot study discussion. The more mathematically inclined reader will benefit from discussions of the relative (comparative) property of ApEn and from Appendix A.

A regularity statistic for medical data analysis

A survey on ECG analysis

Classification of myocardial infarction with multi-lead ECG signals and deep CNN

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

Third-order tensor based analysis of multilead ECG for classification of myocardial infarction

Multilead ECG data compression using SVD in multiresolution domain

Objective: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial scarring that can be detected from the electrocardiogram (ECG). Nowadays, fQRS scoring is done on a visual basis, which is time consuming and leads to subjective results. This study proposes an automated method to detect and quantify fQRS in a continuous way using features extracted from variational mode decomposition (VMD) and phase-rectified signal averaging (PRSA). Methods: In the proposed framework, QRS complexes in the ECG signals were first segmented using VMD. Then, ten VMD- and PRSA-based features were computed and fed into well-known classifiers such as support vector machine (SVM), K-nearest neighbors (KNN), Naive Bayesian (NB), and TreeBagger (TB) in order to compare their performance. The proposed method was evaluated with 12-lead ECG data of 616 patients from the University Hospitals Leuven. The presence of fQRS in each ECG lead was scored by five raters. Both detection and quantification of fQRS could be achieved in this way. Results: The experimental results indicated that the proposed method achieved AUC values of 0.95, 0.94, 0.90, and 0.89 using SVM, KNN, NB, and TB classifiers, respectively, for detecting QRS fragmentation. Assessment of quantification performance was done by comparing the fQRS score with the total score, obtained by summing the scores from the individual raters. Results showed that the fQRS score clearly correlated with this estimate of fQRS certainty. Conclusion: The proposed method obtained good results in both fQRS detection and quantification, and is a novel way of assessing the certainty of QRS fragmentation in the ECG signal.

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A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC); and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications.

Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

With the increasing number of sensors/ channels, hand gesture recognition from multiple channels has attracted the attention of the researchers in recent years. The analysis of a variety of gestures is still a challenging task in real-time applications such as in wearable devices because of the large number of channels used. This paper proposes a tensor-based approach using multilinear singular value decomposition (MLSVD) for hand gesture recognition where all available channels were used during training whereas only a single channel was used for recognition of new gestures. Tensor decompositions have very limited use in gesture recognition using surface Electromyography (sEMG) despite these signals naturally have multi-way structures. The sEMG data of different subjects were first segmented to reshape it as a fourth-order tensor of the form channels $\times $ time $\times $ gestures $\times $ subjects . The MLSVD was applied to model the tensor and extract features, which were then fed into various classifiers such as support vector machine (SVM), K-nearest neighbors (KNN), TreeBagger (TB), and dictionary learning (DL) classifiers in order to compare their performance. The proposed method was evaluated on three publicly available databases (NinaPro, CapgMyo (DB-a, DB-b and DB-c), CSL-HDEG) by performing intra-session, inter-session and inter-subject evaluations on each database. The experimental results indicated that the proposed method achieved the best accuracy (Acc) using DL, achieving Acc of 77.6% and 89.6% with CapgMyo DB-b and CSL-HDEMG databases, respectively, during inter-session evaluation, and 75.2%, 75.4%, 68.3%, and 67.7% with NinaPro, CapgMyo DB-b, CapgMyo DB-c, and CSL-HDEMG databases, respectively, during inter-subject evaluation. The proposed method performed better during both inter-session and inter-subject evaluations than the state-of-the-art methods.

Sibasankar Padhy

Papers

Third-order tensor based analysis of multilead ECG for classification of myocardial infarction

Multilead ECG data compression using SVD in multiresolution domain

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation

Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

A Tensor-Based Approach Using Multilinear SVD for Hand Gesture Recognition From sEMG Signals