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Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

Qaisar Saeed Mian, Khan Sibghatullah I., Dallet Dominique, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 2

681--694

The next generation healthcare systems will be based on the cloud connected wireless biomedical wearables. The key performance indicators of such systems are the compression, computational efficiency, transmission and power effectiveness with precision. The electrocardiogram (ECG) signals processing based novel technique is presented for the diagnosis of arrhythmia. It employs a novel mix of the Level-Crossing Sampling (LCS), Enhanced Activity Selection (EAS) based QRS complex selection, multirate processing, Wavelet Decomposition (WD), Metaheuristic Optimization (MO), and machine learning. The MIT-BIH dataset is used for experimentation. Dataset contains 5 classes namely, ‘‘Atrial premature contraction”, ‘‘premature ventricular contraction”, ‘‘right bundle branch block”, ‘‘left bundle branch block” and ‘‘normal sinus”. For each class, 450 cardiac pulses are collected from 3 different subjects. The performance of Marine Predators Algorithm (MPA) and Artificial Butterfly Optimization Algorithm (ABOA) is investigated for features selection. The selected features sets are passed to classifiers that use machine learning for an automated diagnosis. The performance is tested by using multiple evaluation metrics while following the 10-fold cross validation (10-CV). The LCS and EAS results in a 4.04-times diminishing in the average count of collected samples. The multirate processing lead to a more than 7-times computational effectiveness over the conventional fix-rate counter parts. The respective dimension reduction ratios and classification accuracies, for the MPA and ABOA algorithms, are 29.59-times & 22.19-times and 98.38% & 98.86%.

Fetal heart rate estimation using fractional Fourier transform and wavelet analysis

Jaba Deva Krupa Abel, Dhanalakshmi Samiappan, Sanjana N.L., Manivannan Naveen, Kumar Ramamoorthy, Tripathy Saswati

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 4

1533--1547

Objective: Monitoring fetal cardiac activity during pregnancy is a critical part of assessing the fetus’s health. Non-invasive fetal electrocardiogram (NIFECG) is a safe emerging fetal cardiac monitoring approach receiving considerable interest. This paper proposes an effective way to separate the fetal ECG signal from the single-channel abdominal ECG signals. Methods: The paper proposes a novel algorithm based on time-frequency analysis combining fractional Fourier transform (FrFT) and wavelet analysis to extract fetal ECG from abdominal signals at higher accuracy. The abdominal signals acquired from pregnant women are preprocessed and subjected to suppressing maternal ECG using fractional Fourier transform and maximum likelihood estimate. The estimated maternal signal is removed from the abdominal ECG. The residue is processed using wavelet decomposition to obtain a clean fetal ECG and calculate fetal heart rate. Results: The proposed algorithm’s performance is validated using signals from the Daisy database and Physionet challenge 2013 set-a dataset. Real-time signals acquired using Powerlab data acquisition hardware are also included for validation. The obtained results show that the proposed algorithm can effectively extract the fetal ECG and accurately estimate the fetal heart rate. Conclusion: The proposed method is a promising and straightforward algorithm for FECG extraction. Fractional Fourier transform maps the time domain abdominal signal into the fractional frequency domain, distinguishing the fetal and maternal ECG. The Wavelet transform can efficiently denoise the residue abdominal signal and provides a clean fetal ECG. The proposed approach achieves 98.12% of accuracy, 98.85% of sensitivity, 99.16% of positive predictive value, and 99.42% of F1 measure.

Falkowa analiza sygnałów (Encyklopedia PE)

Rak R.J., Majkowski A.

Przegląd Elektrotechniczny

2004

R. 80, nr 6

646-652

Cechą charakterystyczną analizy falkowej jest to, że związane z nią funkcje falkowe są dobrze zlokalizowane w czasie (przestrzeni) i jednocześnie dobrze opisują sygnał w dziedzinie częstotliwości, ściśle biorąc tzw. skali. Ponadto w odróżnieniu od funkcji sinus i cosinus, które definiują unikalną transformatę Fouriera, nie ma pojedynczego, unikalnego zbioru falkowych funkcji bazowych. Istnieje nieograniczona wręcz liczba możliwych do utworzenia falek. Która z nich jest najlepsza zależy od konkretnej implementacji. Swoją niezwykłą efektywność w zakresie analizy sygnałów, transformata falkowa zawdzięcza szybkiemu algorytmowi piramidy, opracowanemu przez Mallata. Algorytm ten umożliwia w łatwy i szybki sposób uzyskanie dekompozycji sygnału na składowe falkowe.

What makes the wavelet analysis interesting is that individual wavelet functions are quite localized in time scale (or space) and simultaneously in frequency (or characteristic scale). Unlike sine and cosine, which define a unique Fourier transform, there is not one single unique set of wavelets. In fact there are infinite variety of possible sets. Which one is the best it depends on a particular application. Wavelet analysis owes its efficiency to the fast pyramid algorithm described by Mallat. The algorithm enables, in easy way, fast decomposition of a signal into wavelet coefficients.