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Full-Text Articles in Physical Sciences and Mathematics
Probabilistic Data Fusion Model For Heart Beat Detection From Multimodal Physiological Data, Tehseen Zia, Zulqarnian Arif
Probabilistic Data Fusion Model For Heart Beat Detection From Multimodal Physiological Data, Tehseen Zia, Zulqarnian Arif
Turkish Journal of Electrical Engineering and Computer Sciences
Automatic detection of heart beats constitutes the basis for electrocardiogram (ECG) analysis and mainly relies on detecting QRS complexes. Detection is typically performed by analyzing the ECG signal. However, when signal quality is low, it often leads to the triggering of false alarms. A contemporary approach to reduce false alarm rate is to use multimodal data such as arterial blood pressure (ABP) or photoplethysmogram (PPG) signals. To leverage the correlated temporal nature of these signals, a probabilistic data fusion model for heart beat detection is proposed. A hidden Markov model is used to decode waveforms into segments. A Bayesian network …
Electrocardiogram Signal Analysis For R-Peak Detection And Denoising With Hybrid Linearization And Principal Component Analysis, Harjeet Kaur, Rajni Rajni
Electrocardiogram Signal Analysis For R-Peak Detection And Denoising With Hybrid Linearization And Principal Component Analysis, Harjeet Kaur, Rajni Rajni
Turkish Journal of Electrical Engineering and Computer Sciences
In the areas of biomedical and healthcare, electrocardiogram (ECG) signal analysis is one of the major aspects of research. The accuracy in the detection of subtle characteristic features in ECG is of great significance. This paper deals with an algorithm based on hybrid linearization and principal component analysis for ECG signal denoising and R-peak detection. The ECG data have been taken from the MIT-BIH Arrhythmia Database for performance evaluation. The signal is denoised by applying the hybrid linearization method, which is an arrangement of the extended Kalman filter along with discrete wavelet transform, and then principal component analysis is employed …
K-Nn-Based Classification Of Sleep Apnea Types Using Ecg, Oğuz Han Ti̇muş, Emi̇ne Doğru Bolat
K-Nn-Based Classification Of Sleep Apnea Types Using Ecg, Oğuz Han Ti̇muş, Emi̇ne Doğru Bolat
Turkish Journal of Electrical Engineering and Computer Sciences
Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder that yields cardiovascular diseases, excessive daytime sleepiness, and poor quality of life if not treated. Classification of OSAS from electrocardiograms (ECGs) is a noninvasive method and much more affordable than traditional methods. This study proposes a pattern recognition system for automated apnea diagnosis based on heart rate variability (HRV) and ECG-derived respiratory signals. The k-nearest neighbor (k-NN) classifier has been used to develop the models for classifying the sleep apnea types. For comparison purposes, classification models based on multilayer perceptron, support vector machines, and C4.5 decision tree (C4.5 DT) have …