But now, researchers at the Indian Institute of Technology in Kharagpur have developed a new technique that could circumvent the problem of the failing stethoscope skills of medical graduates and reduce errors of judgment
This analytical method developed by Samit Ari and Goutam Saha can automatically classify a much wider range of heart sounds than is possible even by the most skilled stethoscope-wielding physician.
The technique is based on a mathematical analysis of the sound waves produced by the beating heart known as Empirical Mode Decomposition (EMD). This method breaks down the sounds of each heart cycle into its component parts, enabling them to isolate the sound of interest from background noise, such as the movements of the patient, internal body gurgles, and ambient sounds.
The analysis thus produces a signal based on twenty five different sound qualities and variables, which can then be fed into a computer-based classification system. The classification uses an Artificial Neural Network (ANN) and a Grow and Learn (GAL) network. These are trained with standardized sounds associated with a specific diagnosis.
Later, the team tested the trained networks using more than 100 different recordings of normal heart sounds, sounds from hearts with a variety of valve problems, and different background noises. They found that the EMD system performs more effectively in all cases than conventional electronic, wavelet-based, approaches to heart sound classification.
The researchers explained that a large number of medical graduates cannot properly diagnose heart conditions using a stethoscope, and the poor sensitivity of the human ear to low frequency heart sounds makes this task even more difficult. And this fallacy may be averted by using the automatic classification of heart sounds based on Ari and Saha's technique.
The study is published in the inaugural issue of the International Journal of Medical Engineering and Informatics looks.