Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System.

Cardiac arrhythmia is known to be one of the most common causes of death worldwide. Therefore, development of efficient arrhythmia detection techniques is essential to save patients' lives. In this paper, we introduce a new real-time cardiac arrhythmia classification using memristor neuromorphic computing system for classification of 5 different beat types. Neuromorphic computing systems utilize new emerging devices, such as memristors, as a basic building block. Hence, these systems provide excellent trade-off between real-time processing, power consumption, and overall accuracy. Experimental results showed that the proposed system outperforms most of the methods in comparison in terms of accuracy and testing time, since it achieved 96.17% average accuracy and 34 ms average testing time per beat.

Motor imagery based brain computer interface using transform domain features.

Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements. Different preprocessing, feature selection, and classification schemes were utilized to evaluate the performance of the proposed system for dataset III from BCI competition II. The maximum accuracy achieved was 90.7% while the maximum mutual information was 0.76 bit obtained using the distance series features.

Arrhythmia classification based on novel distance series transform of phase space trajectories.

Cardiac arrhythmia is a serious disorder in heart electrical activity that may have fatal consequences especially if not detected early. This motivated the development of automated arrhythmia detection systems that can early detect and accurately recognize arrhythmias thus significantly improving the chances of patient survival. In this paper, we propose an improved arrhythmia detection system particularly designed to identify five different types based on nonlinear dynamical modeling of electrocardiogram signals. The new approach introduces a novel distance series domain derived from the reconstructed phase space as a transform space for the signals that is explored using classical features. The performance measures showed that the proposed system outperforms state of the art methods as it achieved 98.7% accuracy, 99.54% sensitivity, 99.42% specificity, 98.19% positive predictive value, and 99.85% negative predictive value.