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Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1 s electrocardiogram (ECG) segments to time-frequency images, and then, the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation Database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp), and the area under the ROC curve (AUC) results are 74.96%, 86.41%, and 0.88, respectively. When excluding an extremely poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp, and AUC values of 79.05%, 89.99%, and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.

Purpose The purpose of this paper is to study the “underbanked” – those who already possess bank accounts but are patrons of alternative financial services (AFS) providers at the same time. Design/methodology/approach Linking the FDIC unbanked/underbanked surveys of nationally represented households with FDIC bank information and local MSA demographics, demographic and economic profiles of the underbanked households are examined, together with the determinants of their choice of nonbank financial services. Findings The author finds that bank fees are associated with the likelihood for households to obtain AFS, especially nonbank credit. Households’ attitudes and experience with banks are important in the choice of getting AFS. Furthermore, most underbanked households used AFS temporarily, partly reflecting rather informed and calculated financial decisions. Research limitations/implications The results from this paper provide implications for different types of AFS users. For example, the use of transactional AFS responds to the availability of online or mobile banking; meanwhile, it is also sensitive to branch closure. Users of nonbank credits are likely to be price savvy, and these products serve as valuable alternatives for short-term financing, especially during unfavorable economic situation. Social implications Better understanding of the underbanked could help banks tailor to existing clients’ needs, for instance, providing innovative short-term credit products for those with little or impaired credit history. The study also helps policy makers re-evaluate banking regulations since the Great Recession. As regulations squeezed bank profits in certain areas and forced banks to consolidate, come alongside higher bank fees, potential branch closure and loss of service, which ultimately forced banked individuals to the less regulated alternative providers. Originality/value The analysis utilizes a comprehensive set of variables, from household social-economic characteristics to local banking industry characteristics, together with households’ subjective opinions of their banking institutions. The focus on the underbanked brings attention to this underserved population and discusses areas where banks can improve. The study contributes to the understanding of AFS users, draws implications for regulation toward banking and shadow banking.