Dean Cvetkovic - Academia.edu (original) (raw)

Papers by Dean Cvetkovic

This study is carried out with the aim of classifying healthy and insomniac subjects based on the... more This study is carried out with the aim of classifying healthy and insomniac subjects based on their wake-to-sleep transition (sleep onset process) features. The features were extracted from those signals using non-parametric and parametric methods in frequency domain. Wavelet transform was used to calculate non-parametric features: relative power of EEG sub bands (delta, theta, alpha, beta and gamma). After that Sleep onset reference epochs were determined using first and last intersection of delta and alpha respectively. The statistical analysis was applied on the features obtained. The data was divided into two groups: training data and testing data. Classification tree model was executed on training data to predict the healthy and insomniac groups in test data. K-fold cross-validation method was used for this estimation.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016

This paper presents a new and robust algorithm for detection of sleep stages by using the lead I ... more This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight PSG recordings consisting of 20 human subjects (9 insomniac and 11 healthy). Heart Rate Variability (HRV) and Pulse Transit Time (PTT) biomarkers which were extracted from ECG and PPG biosignals then employed to extract features. Distance Weighted k-Nearest Neighbours (DWk-NN) was used as classifier to differentiate sleep epochs. The validation of the algorithm was evaluated by Leave-One-Out-Cross-Validation method. The average accuracy of 73.4% with standard deviation of 6.4 was achieved while the algorithm could distinguish stages 2, 3 of non-rapid eye movement sleep by average sensitivity of almost 80%. The lowest mean sensitivity of 53% was for stage 1. These results demonstrate that an algorithm based on PTT and HRV spectral analysis is able to classify and distinguish sleep stages with high accuracy and sensitivity. In addition the proposed algorithm is capable to be improved and implemented as a wearable, comfortable and cheap instrument for sleep screening.

2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), 2018

Insomnia is the inability to sleep that causes interruption in sleep. ECG signals which measured ... more Insomnia is the inability to sleep that causes interruption in sleep. ECG signals which measured during sleep carry valuable information about heart activity. In the current study, ECG signals of 10 primary insomnia patients and 10 healthy controls during four sleep stages namely, N1, N2, N3 and REM were analyzed. Time and frequency domain of Heart Rate Variability (HRV) were computed and statistically analyzed to evaluate existence of statistical significant differences between the two groups at different sleep stages. A high value of LF index was observed during N3 sleep stage in the primary insomnia patient can be regarded as predominant of sympathetic nervous system.

Signal Processing and Machine Learning for Biomedical Big Data, 2018

2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016

K-complex detection is a fundamental requirement during sleep stage analysis. A number of past st... more K-complex detection is a fundamental requirement during sleep stage analysis. A number of past studies have used commonly applied wavelets to detect K-complexes. In this study we have constructed a wavelet specifically to match the structure of K-complexes and further as preliminary testing we have applied the designed wavelet for K-complex detection on the publicly available DREAMS© database and second private database. Results obtained from the DREAMS© database showed a True Positive Rate of 84% at a Positive Predictive Value of 62%. The results are on par with other algorithms that were tested on the same databases.

INTRODUCTION Adequately controlled electromagnetic radiation can be used for therapeutic purposes... more INTRODUCTION Adequately controlled electromagnetic radiation can be used for therapeutic purposes. New possibilities have been discovered lately in pulsating magnetic fields of ELF from 2 to 24Hz [1]. This radiation has proven to be successful in curing various diseases. This mechanism that forms the basis of the curing method is the increased dynamic of ions due to changes of the electrical potential in cell membranes caused by varying electromagnetic field. The effect is a better supply of cells with oxygen.

IFMBE Proceedings, 2015

This work aims to investigate new indexes quantitatively differentiate sleep insomnia patients fr... more This work aims to investigate new indexes quantitatively differentiate sleep insomnia patients from healthy subjects, in the context of sleep onset fluctuations. Our study included the use of existing PSG dataset, of 20 healthy subjects and 20 insomniac subjects. The differences between normal sleepers and insomniacs was investigated, in terms of dynamics and content of Sleep Onset (SO) process. An automated system was created to achieve this and it consists of six steps: 1) pre-processing of signals 2) feature extraction 3) classification 4) automatic scoring 5) sleep onset detection 6) identification of subject groups. The pre-processing step consisted of the removal of noise and movement artifacts from the signals. The feature extracting step consists of extracting time, frequency and non-linear features of Electroencephalogram (EEG) and Electromyogram (EMG) signals. In the third step, classification was done using ANN (Artificial Neural Networks) classifier. The fourth step consisted of scoring sleep stages (wake, S1, S2, S3 and REM) and produced a hypnogram. In the fifth step, we are detecting sleep onset from our automatic detected hypnogram and identified time of SO reference point and the combination of stages. In the final step we differentiated healthy subjects from insomniac patients based on the parameters calculated in the fifth step.

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2017

Sleep Apnoea Syndromes (SAS) is a sleep disorder which caused breathing pauses during sleep at ni... more Sleep Apnoea Syndromes (SAS) is a sleep disorder which caused breathing pauses during sleep at night. There is various method of analyzing sleep EEG signals can be found in the literature. In this paper both linear; Discrete Wavelet Transform (DWT) and non-linear; Approximate Entropy (ApEn) extraction methods were performed to differentiate features of each sleep stages between apnoea and healthy person. The efficiency of both extraction methods was compared by using ANOVA. In our study, we observed the non-linear feature of ApEn improves the ability to discriminate healthy and sleep apnoea at different sleep stages.

Primary insomnia is a term used to describe a subtype of insomnia that constitutes the disorder i... more Primary insomnia is a term used to describe a subtype of insomnia that constitutes the disorder itself and is not a consequent to any other psychiatric or sleep disorder. Hitherto, there is no clear objective markers from Polysomnography (PSG) signal to characterize insomnia. Although linear methods like spectral analysis of EEG frequency bands have been used to detect physiological arousal in patients with insomnia, these methods may not be sufficient enough to extract valuable information and detect abnormalities in the signals. The EEG signal itself originate from a complex neuronal activity in the brain, therefore the use of nonlinear measures may show some hidden information that could better explain the activation of this hyperarousal. The aim of the present study is to classify the primary insomnia patient from the healthy based on the supervised learning machine technique of SVM and the usage of nonlinear features of EEG signal. The classification result by using SVM achieve...

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Jul 1, 2017

Sleep arousal is generally known as a transient episode of wakefulness into the sleepiness. Sleep... more Sleep arousal is generally known as a transient episode of wakefulness into the sleepiness. Sleep arousals can be classified based on their association and accompany with pathological episodes. In this paper, our objective was to find out whether various types of sleep arousals influence on blood pressure and Heart Rate Variability (HRV). We analysed continuous Diastolic and Systolic Blood Pressures (DBP and SBP), Pulse Transit Time (PTT) as well as High and Low Frequency components (HF and LF) of HRV in different types of arousals. We developed Slope Index (SI) to determine whether a feature was ascending or descending before, during and after the occurrence of a sleep arousal. Slope Index Positive Percentage (SIPP) was created and computed for all features to find out the percentage of arousals with an ascending trend of a cardiovascular feature. In pre-arousal epochs, we obtained SIPPDBP= 48.9%, SIPPSBP = 48.2% and SIPPLF = 41%. Whilst during the arousal episodes, the SIPPDBP, SI...

Estonian Journal of Engineering

Journal of Bioprocessing & Biotechniques

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

Sleep spindle detection using modern signal processing techniques such as the Short-Time Fourier ... more Sleep spindle detection using modern signal processing techniques such as the Short-Time Fourier Transform and Wavelet Analysis are common research methods. These methods are computationally intensive, especially when analysing data from overnight sleep recordings. The authors of this paper propose an alternative using pre-designed IIR filters and a multivariate Gaussian Mixture Model. Features extracted with IIR filters are clustered using a Gaussian Mixture Model without the use of any subject independent thresholds. The Algorithm was tested on a database consisting of overnight sleep PSG of 5 subjects and an online public spindles database consisting of six 30 minute sleep excerpts. An overall sensitivity of 57% and a specificity of 98.24% was achieved in the overnight database group and a sensitivity of 65.19% at a 16.9% False Positive proportion for the 6 sleep excerpts.

2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2015

Manual scoring of arousals is generally conducted by sleep experts in spite of being time-consumi... more Manual scoring of arousals is generally conducted by sleep experts in spite of being time-consuming and subjective. Our objective of this study was to develop an algorithm for automatic detection of sleep arousals without distinguishing between the types of arousal and sleep disorder groups. The processed and analysed data multiple overnight Polysomnography (PSG) recordings, consisting of 9 human subjects (6 male, 3 female), with age range of 34-69 and different conditions (4 patients with obstructive sleep apnoeas, 4 healthy and 1 patient with periodic limb movement disorder). PSG biosignals were processed to extract necessary features. K-nearest neighbours (KNN) was used as the classifier and performance of algorithm were evaluated by Leave-One-Out Cross-Validation. The average sensitivity, specificity and accuracy of algorithm was 79%, 95.5% and 93%, respectively. These results demonstrate that our algorithm can automatically detect arousals with high accuracy. Furthermore, the algorithm is capable to be upgraded for classification of various types of arousals based upon their origin and characteristics.

2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008

Mobile phone handsets such as those operating in the GSM network emit extremely low frequency ele... more Mobile phone handsets such as those operating in the GSM network emit extremely low frequency electromagnetic fields ranging from DC to at least 40 kHz. As a subpart of an extended protocol, the influence of these fields on the human resting EEG has been investigated in a fully counter balanced, double blind, cross-over design study that recruited 72 healthy volunteers. A decrease in the alpha frequency band was observed during the 20 minutes of ELF exposure in the exposed hemisphere only. This result suggests that ELF fields as emitted from GSM handsets during the DTX mode may have an effect on the resting alpha band of the human EEG.

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008