Discrete Biorthogonal Wavelet Transform Based Convolutional Neural Network for Atrial Fibrillation Diagnosis from Electrocardiogram

For the problem of early detection of atrial fibrillation (AF) from electrocardiogram (ECG), it is difficult to capture subject-invariant discriminative features from ECG signals, due to the high variation in ECG morphology across subjects and the noise in ECG. In this paper, we propose an Discrete Biorthogonal Wavelet Transform (DBWT) Based Convolutional Neural Network (CNN) for AF detection, shortly called DBWT-AFNet. In DBWT-AFNet, rather than directly feeding ECG into CNN, DBWT is used to separate sub-signals in frequency band of heart beat from ECG, whose output is fed to CNN for AF diagnosis. Such sub-signals are better than the raw ECG for subject-invariant CNN representation learning because noisy information irrelevant to human beat has been largely filtered out. To strengthen the generalization ability of CNN to discover subject-invariant pattern in ECG, skip connection is exploited to propagate information well in neural network and channel attention is designed to adaptively highlight informative channel-wise features. Experiments show that the proposed DBWT-AFNet outperforms the state-of- the-art methods, especially for ECG segments classification across different subjects, where no data from testing subjects have been used in training.

Segmentation of Brain Tumour Based on Clustering Technique: Performance Analysis

Manual detection and analysis of brain tumours is an exhaustive and time-consuming process. Further, it is subject to intra-observer and inter-observer variabilities. Automated brain tumour segmentation and analysis has thus gained much attention in recent years. However, the existing segmentation techniques do not meet the requirements of real-time use due to limitations posed by poor image quality and image complexity. This article proposes a hybrid approach for image segmentation by combining biorthogonal wavelet transform, skull stripping, fuzzy c-means threshold clustering, Canny edge detection, and morphological operations. Biorthogonal wavelet transform and skull stripping are essential pre-processing steps for analysis of brain images. Initially, biorthogonal wavelet transform is used to remove impulsive noise and skull stripping is employed to eliminate non-cerebral tissue regions from the acquired images, followed by segmentation using fuzzy c-means threshold clustering, Canny edge detection, and morphological processing. The performance of the proposed automated system is tested on standard datasets using performance measures such as Jaccard index, Dice similarity coefficient, execution time, and entropy. The proposed method achieves a Jaccard index and Dice similarity coefficient of 0.886 and 0.935, respectively, which indicate better overlap between the automated segmentation method and manual segmentation method performed by an expert radiologist. The average execution time and average entropy values obtained are 1.001 s and 0.202, respectively. The results obtained are discussed in view of some reported studies in terms of execution time and tumour area. Further work is needed to evaluate the proposed method in routine clinical practice and its effect on radiologists’ performances.