A group of practicing biomedical engineers from many parts of the world have come together to describe some of the significant advances in the areas of medical devices and medical electronics over the last 100 year in terms of their impact on the practice of medicine. These devices have made clinicians better able to diagnose disease and assess injury by giving them technologies to look into the body and to quantitatively evaluate physiologic functions of their patients. Medical devices have made it possible to monitor the progress of treatment of patients and have made many advancements in the rehabilitation of these patients. While some have claimed that these developments have been responsible for increased healthcare costs, it could also be argued that this technology has been responsible for prolonging life and improving its quality. They found that the rate of change of medical device development over the past 100 years has rapidly increased the closer we get to the present.

Medical devices and medical electronics are areas that had little to offer 100 years ago. However, there were three important existing technologies that led to many further developments over the following 100 years. These are the stethoscope, electrocardiography, and X-ray medical imaging. Although these technologies had been described and were available to some extent when the Proceedings of the IEEE pages first appeared, they had yet to achieve the widespread use that they have today. The stethoscope is the oldest of these, and it helped physicians to hear sounds of the body and relate them to functioning and malfunctioning organs. The early use of the stethoscope by physicians was more of an art than a science, but as the Proceedings matured, so did this technology. Engineers were able to make this a more quantitative process by graphically displaying the sounds and ultimately using techniques such as voiceprint analysis to assist the physician in diagnosis and monitoring of treatment. The electrocardiograph had been invented a few years prior to the appearance of the Proceedings, but the apparatus was awkward to use, especially for sick people, and was considered more of an oddity than a viable medical technology 100 years ago. Today, it and devices derived from it such as cardiac patient monitors are important parts of our healthcare system. Similarly, X-rays represented a new technology 100 years ago, but unlike electrocardiography physicians immediately saw the value of this technology and quickly adopted it. Many improvements have been made to the basic technology over the last 100 years culminating in computer tomography and complex image processing. Other devices to create high-quality and 3-D medical images have also been developed in recent years to make medical imaging a very important aspect of clinical care today. Looking to the future is always a difficult task, but it is clear that the electronic health record will play an important role in consolidating the information from various medical devices as well as providing readily available data on patients wherever it might be needed. Future medical devices will need to not only address the problems of diagnostic and therapeutic medicine but also be capable of addressing important societal problems such as worldwide disparities in the availability of medical care, continually rising healthcare costs, and healthcare for travel beyond Earth. The next 100 years promises to be even more exciting than the last from the perspective of medical devices and medical electronics.

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and short-time Fourier transform (ST FT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where spectrograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.