Abstract
In this paper, a methodology for sleep apnea detection based on ECG signal analysis using Hilbert transform is proposed. The proposed work comprises a sequential procedure of preprocessing, QRS complex detection using Hilbert Transform, feature extraction from the detected QRS complex and the feature reduction using principal component analysis (PCA). Finally, the classification of the ECG signal recordings has been done using two different artificial neural networks (ANN), one trained with Levenberg-Marquardt (LM) algorithm and the other trained with Scaled Conjugate Gradient (SCG) method guided by K means clustering. The result of classification of the input ECG record is as either belonging to Apnea or Normal category. The performance measures of classification using the two classification algorithms are compared. The experimental results indicate that the SCG algorithm guided by K means clustering (ANN-SCG) has outperformed the LM algorithm (ANN-LM) by attaining accuracy, sensitivity and specificity values as 99.2%, 96% and 97% respectively, besides the saving achieved in terms of reduced number of principal components. Profiling time and mean square error of the ANN classifier trained with SCG algorithm is significantly reduced by 58% and 83%, respectively, as compared to LM algorithm.
Highlights
The recent studies indicate that sleep disorders are direct cause of many cardiovascular diseases among people worldwide
The Dataset of ECG signals used for the experimentation of the proposed method and the results obtained are described
The ECG signal night time recordings are gathered from the benchmark dataset, namely, ECG-Apnea database of MIT‟s Physionet.org, a very popularly used database by researchers involved in sleep studies
Summary
The recent studies indicate that sleep disorders are direct cause of many cardiovascular diseases among people worldwide. Sleep apnea is a type of sleep disorder which occurs during night time resulting in interruptions of sleep caused by cessation of breathing. In order to distinguish sleep apnea type as OSA, CSA or MSA, additional clinical information obtained in PSG is required along with ECG signal information. During the occurrence of sleep apnea condition, Electrocardiogram (ECG) signal gets modulated in its amplitude and frequency because of reduced oxygen level in the blood caused by cessations in breathing action. The objective is to propose a methodology for sleep apnea detection based only on ECG signal analysis using Hilbert transform for QRS complex detection, feature extraction in terms of signal parameters, feature reduction by PCA and classification by ANN. The result of classification is that the input ECG signals recording is either belonging to apnea category or normal category
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