One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient's heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.
Multidrug-resistant organisms (MDROs) such as multidrug-resistant (MDR) Acinetobacter baumannii and Escherichia coli are important pathogens associated with nosocomial infections in both human and animal health care facilities. Surfaces of inanimate objects in health care facilities can serve as sources of infection. However, studies on prevalence of these pathogens in veterinary settings are lacking in the country. Therefore, the objectives of this study were to determine the occurrence of A. baumannii and E. coli and the occurrence of MDR isolates on surfaces of inanimate objects in veterinary health care facilities in Klang Valley, Malaysia. In this study, swab samples were taken from 65 surfaces of inanimate objects that included door knobs, examination tables, labcoats, stethoscopes and weighing scales. The swab samples were cultured and all isolates were subjected to antibiotic susceptibility test. The study revealed that the occurrence of A. baumannii was 9.23% and 5 out of 6 (83.33%) A. baumannii isolates were classified as MDR. However, no E. coli was isolated. In conclusion, surfaces of inanimate objects can be a source of MDR A. baumannii in veterinary health care facilities that is of animal and public health concern.