Noise often appears in parts of heart sound recordings, which may be much longer than those necessary for subsequent automated analysis. Thus, human intervention is needed to select the heart sound signal with the best quality or the least noise. This paper presents an automatic scheme for optimum sequence selection to avoid such human intervention. A quality index, which is based on finding that sequences with less random noise contamination have a greater degree of periodicity, is defined on the basis of the cyclostationary property of heart beat events. The quality score indicates the overall quality of a sequence. No manual intervention is needed in the process of subsequence selection, thereby making this scheme useful in automatic analysis of heart sound signals.
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http://dx.doi.org/10.1016/j.compbiomed.2013.03.002 | DOI Listing |
Hum Brain Mapp
January 2025
Department of Psychology, Concordia University, Montreal, Quebec, Canada.
The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions.
View Article and Find Full Text PDFEur J Case Rep Intern Med
December 2024
Internal Medicine, Holy Family Hospital, Rawalpindi, Pakistan.
Background: Andersen-Tawil syndrome (ATS) is a rare autosomal dominant disorder caused by variants in the gene. It is associated with periodic paralysis, dysmorphic features and cardiac arrhythmias. The syndrome exhibits incomplete penetrance, leading to a broad spectrum of clinical manifestations, making diagnosis challenging.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
College of Nursing, University of Nebraska Medical Center, Omaha, NE, United States.
Background: The known and established benefits of exercise in patients with heart failure (HF) are often hampered by low exercise adherence. Mobile health (mHealth) technology provides opportunities to overcome barriers to exercise adherence in this population.
Objective: This systematic review builds on prior research to (1) describe study characteristics of mHealth interventions for exercise adherence in HF including details of sample demographics, sample sizes, exercise programs, and theoretical frameworks; (2) summarize types of mHealth technology used to improve exercise adherence in patients with HF; (3) highlight how the term "adherence" was defined and how it was measured across mHealth studies and adherence achieved; and (4) highlight the effect of age, sex, race, New York Heart Association (NYHA) functional classification, and HF etiology (systolic vs diastolic) on exercise adherence.
Background: Producing speech is a cognitively complex task and can be collected through devices such as handheld recorders, tablets, and smartphones. Digital voice data can also capture information at a granular millisecond‐level precision and serve as a widespread tool to collect cognitively relevant data in almost any diverse real‐world environments. Digital voice recordings of spoken responses to neuropsychological test questions have been collected through the Framingham Heart Study (FHS) since 2005.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Massachusetts Institute of Technology, Cambridge, MA, USA
Background: Speech is a predominant mode of human communication. Speech digital recordings are inexpensive to record and contain rich health related information. Deep learning, a key method, excels in detecting intricate patterns, however, it requires substantial training data.
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