Bio-signal Monitoring and Heart Diseases
In terms of heart diseases, researchers from Asan hospital, Seoul represented AI which can concisely predict ventricular tachycardia before 1 hour from when it strokes.
This increased the viability of patients from heart attack but required the human effort to set an artificial neural network which was based on specific feature.
Furthermore, researchers from Vuno institution, developed DeepEWS, which used RNN (Recurrent Neural Network) deep learning system to learn 7 types of information from individual patient.
RNN was used for this system as researchers wanted to predict heart disease before 24 hours and concentrate on data variance and reducing false warning alert.
Similarly, Cardiogram applied deep learning to the heartbeat sensor in wearable device to identify atrial fibrillation and atrial flutter as heartbeat’s distinctive rhythm was able to recognize some irregular pulse.
Irregular pulse specificity of wearable device was evaluated with two criteria (Sequence F1- figure out arrhythmia on proper time, Set F1- detect arrhythmia from whole data).
As a result, deep learning was showing much higher specificity with both sequence F1 and set F1 when it compared to cardiologist.
This research identified that AI could easily classify two types of atrioventricular block which most professional cardiologists were having difficulty to identify.
If data aggregation (electrocardiogram, heartbeat rate…etc), analyzing, and classifying can be constantly processed through wearable device or apple watch, predicting heart diseases with high accuracy will be developed.