Electrocardiograph analysis was developed to elucidate cardiac algorithm with the objective of discerning cardiac output and to better understand Korotkoff sounds measured with a twelve lead rhythm algorithm plotted on a graph which depicts a one minute interval generating the heart rate .. The first machines developed utilized metal plates and conduction gel which were attached to the patients inner limbs with a rubber strips and a six lead landmark on the chest . During the 1990s the digital and computerized EKG machine was developed where nurses examined the rhythm strip and interpreted the heart rate. Today we still utilize digital EKG but recently we have scaled the twelve lead digital EKG by adding AI or artificial Intelligence to better interpret heart rhythm particularly in emergency situations that may occur outdoors . In a research article entitled Cardiologist - Level arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using Deep Neural Network ' were 53,549 patients attached to a single lead ambulatory ECG monitoring devise was validated against an independent test data set annotated by a consensus committee of board certified practicing cardiologist , the DNN or Deep Neural Network achieved an average area under the receiver operating characteristics curve (ROC ) of 0.97 the average cardiologist 0.780 with specificity fixed at the average specificity achieved by cardiologist , the sensitivity of DNN exceeded the average cardiologist sensitivity for all rhythm classes . The article is significant because this will enable AI or Artificial Intelligence to be adequately integrated into cardiac analysis using less equipment thus being more cost effective , and adaptable to many settings and environments .
References
Hanun , A.Y ; Rapurkar, P ; Haghpanahi,, M; Tison, G.H ; Bourn,C; Turakia,M.P & Ng,A.Y (2019) Cardiologist -Level Arrythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network NCBI
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