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1

Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search

Valavan K. K., Manoj S., Abishek S., Gokull Vijay T. G., Vojaswwin P., Rolant Gini J., Ramachandran K. I.

International Journal of Electronics and Telecommunications

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2021

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Vol. 67, No. 1

5--12

EN

Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening.

2

An improved cardiac arrhythmia classification using an RR interval-based approach

Rahul Jagdeep, Sora Marpe, Sharma Lakhan Dev, Bohat Vijay Kumar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

656--666

EN

Accurate and early detection of cardiac arrhythmia present in an electrocardiogram (ECG) can prevent many premature deaths. Cardiac arrhythmia arises due to the improper conduction of electrical impulses throughout the heart. In this paper, we propose an improved RR interval-based cardiac arrhythmia classification approach. The Discrete Wavelet Transform (DWT) and median filters were used to remove high-frequency noise and baseline wander from the raw ECG. Next, the processed ECG was segmented after the determination of the QRS region. We extracted the primary feature RR interval and other statistical features from the beats to classify the Normal, Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC). The K-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naı¨ve Bayes (NB), and Random Forest (RF) classifier were utilised for classification. Overall performance of SVM with Gaussian kernel achieved Se % = 99.28, Sp % = 99.63, +P % = 99.28, and Acc % = 99.51, which is better than the other classifiers used in this method. The obtained results of the proposed method are significantly better and more accurate.

3

A comparative study of the effects of hyperbaric oxygenation on heart rate variability using wavelet analysis and fast Fourier transform

Juvet T., Abdelhadi S.

Polish Hyperbaric Research

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2013

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nr 4(45)

19--36

EN

Heart rate variability (HRV) is a recognized and reliable parameter of the autonomous nervous system’s effects on the heart. Hyperbaric oxygenation (HBO) has been shown to increase HRV and decrease heart rate suggesting increased vagal tone. Several methods have been developed to analyze HRV, including time-domain, frequency-domain and wavelet time/frequency analysis. To compare the use of these methods in analyzing HRV over shorter periods of time, electrocardiogram recordings were made of 6 subjects under normal resting conditions, followed by breathing 100% O2 at 253 kPa (2.5 ATA) of pressure. HRV was analyzed over two and ten minute periods using both fast Fourier transform and wavelet analysis. Results showed that wavelet and FFT analyses have similar diagnostic features, with continuous wavelet analysis appearing more suitable in detecting changes in HRV over shorter time periods.

PL

Zmienność rytmu zatokowego (HRV) jest sprawdzonym i niezawodnym parametrem mierzącym wpływ autonomicznego układu nerwowego na pracę serca. Badania wykazały, że hiperbaryczna terapia tlenowa (HBO) powoduje zwiększenie HRV oraz redukcję w częstotliwości akcji serca, sugerując tym samym zwiększenie napięcia nerwu błędnego. W celu dokonania analizy HRV wykorzystano kilka metod, w tym analizę czasową, analizę w dziedzinie częstotliwości oraz czasowo-częstotliwościową analizę fali elementarnej. W celu porównania skuteczności tych metod w krótkookresowej analizie HRV, sześciu pacjentów poddano badaniu przy użyciu elektrokardiogramu w warunkach spoczynku, a następnie zlecono im oddychanie 100% O2 pod ciśnieniem 253 kPa (2,5 ATA). HRV analizowano w okresach dwu- i dziesięciominutowych przy użyciu szybkiej transformaty Fouriera (STF) i analizy fali elementarnej. Wyniki wykazały podobieństwa cech diagnostycznych obu metod, przy czym stwierdzono, iż nieprzerwana analiza fali elementarnej osiąga lepsze wyniki w wykrywaniu zmian w HRV w krótszych przedziałach czasowych.

4

Zastosowanie układu FPGA do akceleracji obliczeń całki korelacji interwałów RR

Pawlak S.

Pomiary Automatyka Kontrola

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2007

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R. 53, nr 5

102-104

PL

Analiza sygnałów biomedycznych jest jednym z ważniejszych obszarów badań naukowych. Istnieje duże zapotrzebowanie na wydajne systemy obliczeniowe, które pozwalały by na analizę sygnałów biomedycznych wielu pacjentów. W artykule przedstawiono propozycję architektury systemu obliczającego całkę korelacji interwałów międzyuderzeniowych RR, wykorzystującego układ FPGA do akceleracji obliczeń.

EN

Biomedical signal analysis is a very important field of research. This paper describes architecture of FPGA-accelerated computational system proposed by Author. The main purpose of this system is calculation of the correlation integral of RR intervals in cardiological patients.