I run my code of anomaly detection in ECG signal but , it give me a bad results but it’s ok I will update my code , and I will try to use numenta_detector and use getScalarMetricWithTimeOfDayAnomalyParams() (like NAB) , I guess when I use those two metrics my code will give me a good results,but the problem when I change some parameter (just to know why my result bad like that ) , the result are still the same , detect 2 anomaly and I have 4 anomaly ,
the parameter that I changed (learning Period,historic Window Size ,estimation Samples,estimation Period=100) I change it many and many time but the same result , I read some papers using author ML and I find some paperes use all the signe ECG and some one not, they cutting the ECG and use each wave (P,QRS,T).
so I got confused .
I have some questions:
HTM is supervised anomaly detection that mean the data are not labelled , why in some exemple I find HTM use a dataset with labeled data.
in the anomaly detection , the training is with (normal , abnormal or both) dataset
and the important for me ,
all of us we know that the signal ECG it has 3 waveforms (P, QRS and T waves)
so did i create a encoder just for all the signal ECG(like I did) and then (training,predict…) or cutting my ECG signal and create a encoder for each wave , because maybe a data considered an anomaly in p wave but is not in the QRS wave (they don’t have the same features)