Machine Learning and Cardiology
Heartbeat Classification - Conclusions
9.1 Discussion
In classification a hierarchical structure is used, first to determine if a heartbeat is normal or not, and if not then decide on its problem. Some could see this as an inefficient classification process, as one must go through two levels of classification before an answer to what abnormality has occured can be gained. This however does make sense in a real world system. When a physician is diagnosing a patient, he would most appreciate a result that would show that either the patient is healthy or there is a problem. This decreases time in diagnosing normal heartbeats as they are shifted out early in classiffication. When there is a problem, in a real world situation, only then would further diagnosis be taken, much like in our classiffication model.
The largest errors as gained during ventricular ectopic and fusion heartbeats. This is actually quite understandable given the morphology of these beats. In all consideration a fusion beat looks like a normal beat, albeit in a more prominent state. It would be too easy to confuse this as just a normal beat with noise. The error on the ventricular class is harder to explain but nonetheless also intuitive. Here the emphasis is on extra or delayed QRS-complexes, the main complex that is searched for in the model. This error could be attributed to a smaller window size than is required or that the extra complexes are taken as indicators for the other class.
9.2 Conclusions
Judging by the overall accuracy rating, the methods used here were also extremely useful for further research in this field. Further development would need to occur in the classification of fusion and ventricular ectopic beats, but that would require much decomposition of the signal to find even some of the smallest differences, highly inefficient when considering computation time and power.
As can be seen in various sections of this report, a lot of time went into processing the data for use in the two systems. Indeed, not even the same database was used in either classification or fetal peak extraction. This has an effect on the conclusion as there was now uniform database that had all the information needed for testing on both parts of the research.