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In this study, wavelet-based features of electrohys-terogram (EHG) recordings obtained from pregnant women are applied for preterm birth classification. The wavelet-based feature of EHG signal, Δι, is determined from a difference between the logarithms of variances of detail coefficients of EHG signal corresponding to two consecutive levels, i.e., level I and level 1 + 1. The performance on preterm...
The cognitive states of students in a lecture can give good indications of student concentration and learning, and therefore, modeling them would have a positive impact on their quality of education by enabling the intervention of instructors. In a traditional class, the instructor would assess the students' level of attention. However, the assessment may not be accurate for a variety of reasons....
In a multi-script country like India, prior identification of script from document images is an essential step before choosing appropriate script specific OCR. The problem becomes more complex and challenging in case of HSI (Handwritten Script Identification). An automatic HSI technique for document images of six popular Indic scripts namely Bangla, Devanagari, Malayalam, Oriya, Roman and Urdu is...
Currently, sleep disorders are considered as one of the major human life issues. There are several stable physiological stages that the human brain goes through during sleep. Nowadays, many biomedical signals such as EEG, ECG, EMG, and EOG offer useful details for clinical setups that are used in identifying sleep disorders. In this work, we propose an efficient technique that could be implemented...
Epilepsy is a crucial neurological disorder in which patients experience epileptic seizure caused by abnormal electrical discharges from the brain. It is highly common in children and adults at the age of 65–70. Around 1 % of the world's population is affected by this disease. The mechanism of epilepsy is still incomprehensible to researchers; however, 80% of the seizure activity can be treated effectively...
More than a decade of research has produced numerous representations and similarity measures to support time series classification and clustering. Yet most of the work in the field is so focused on the representation or similarity measure that it ignores the possibility of improving performance using ensembles of representations or classifiers. This paper explores ways of exploiting representational...
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