Using an Automated Speech Recognition Approach to Differentiate Between Normal and Aspirating Swallowing Sounds Recorded from Digital Cervical Auscultation in Children.

Use of machine learning to accurately detect aspirating swallowing sounds in children is an evolving field. Previously reported classifiers for the detection of aspirating swallowing sounds in children have reported sensitivities between 79 and 89%. This study aimed to investigate the accuracy of using an automatic speaker recognition approach to differentiate between normal and aspirating swallowing sounds recorded from digital cervical auscultation in children.

RR-APET - Heart rate variability analysis software.

Background And Objectives: Heart rate variability (HRV) has increasingly been linked to medical phenomena and several HRV metrics have been found to be good indicators of patient health. This has enabled generalised treatment plans to be developed in order to respond to subtle personal differences that are reflected in HRV metrics. There are several established HRV analysis platforms and methods available within the literature; some of which provide command line operation across databases but do not offer extensive graphical user interface (GUI) and editing functionality, while others offer extensive ECG editing but are not feasible over large datasets without considerable manual effort.