SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

Sleep apnea (SA) is the most commonly known sleeping disorder characterized by pauses of airflow to the lungs and often results in day and night time symptoms such as impaired concentration, depression, memory loss, snoring, nocturnal arousals, sweating and restless sleep. Obstructive Sleep Apnea (OSA), the most common SA, is a result of a collapsed upper respiratory airway, which is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated approach towards identifying sleep apnea. The idea is based on efficient feature extraction of the electrocardiogram (ECG) signal by employing a hybrid of signal processing techniques and classification using a linear-kernel Support Vector Machine (SVM). The optimum set of RR-interval features of the ECG signal yields a high classification accuracy of 97.1% when tested on the Physionet Apnea-ECG recordings. The results provide motivating insights towards future developments of convenient and effective OSA screening setups.http://dx.doi.org/10.18201/ijisae.7907

EEG Signal Analysis for Effective Classification of Brain States

EEG (Electroencephalogram) is a non-stationary signal that has been well established to be used for studying various states of the brain, in general, and several disorders, in particular. This work presents efficient signal processing and classification of the EEG signal. The digital filters used during decomposition of the input EEG signal have transfer functions which are simple and easily realizable on digital signal processors (DSP) and embedded systems. The features selected in this study; energy, entropy and variance; are among the most efficient and informative to analyze the EEG signal strength and distribution for detecting brain disorders such as seizure. Training and testing of the extracted features are performed using linear kernel (Support Vector Machine) SVM and thresholding in DSP algorithms and hardware, respectively. The experimental results for the digital signal processing algorithms show a high classification accuracy of 95% in the occurrence of seizure in epileptic patients. The techniques in this work are also under investigation for classifying other brain states/disorders such as sleep stages, sleep apnea and multiple sclerosis