The ECG data is generated by a script taken from Quiroz and et al. work . The main .m file has two parts. First part is used to generate 12 lead ideal noise-free ECG signals. At this noiseless state, we can obtain RR intervals with a simple algorithm for recognition of the signal maximums. For second state i.e. noisy ECG signal, we should use complex algorithms. The latter state also consists of 12 leads.
4.1 ANFIS learning methods:
The training data is collected from artificial 12 lead ECG signals (50 percent of data) and fuzzy ‘fis’ file is used to simulate the remained 50 percent to test the generated ECG data by ANFIS.
The ANFIS method used at this article is used for generation of the missed ECG signal offline but it can be used in applications that needs real-time and online estimation of missed signal.
The offline methods that investigated are:
And the online methods that can be investigated later are:
- CANFIS (Co-Active ANFIS)
-DENFIS (Dynamic Evolving Neural Fuzzy Inference System)
4.2 Design of FCM based ANFIS:
With ANFIS, we can estimate any missed signal from 12 lead ECG signals with a good accuracy.
First the following 12 lead ECG are generated by the MATLAB script :
lead_I, lead_II, lead_III, lead_aVL, lead_aVL, lead_aVL, lead_V1, lead_V2, lead_V3, lead_V4, lead_V5, lead_V6
The V3 signal omitted from the data. Then by FCM method for ANFIS with following parameters as input the lost signal (V3) has been reconstructed:
Number of clusters: 10
Maximum repetition rate: 100
Minimum improvement error: 1e-5
Maximum no. of epocs: 100
Error Goal: 0
Initial Step Size:0.01
Step Size Decrease Rate: 0.9
Step Size Increase Rate: 1.1
The train and test signals and ALL data is shown at figures 5,6 and 7.
Rms error for the three cases are:
a. Train data, RMSE = 1.7112e-5
b. Test data, RMSE = 5.184e-3
c. All data (train and test), RMSE = 2.2663e-3